Health risk determination system, autonomic nerve determination system, life improvement system, sleeping posture determination system, sleeping posture determination program, molar detection system, and molar detection program

Abstract

A health risk assessment system, as an example, is provided with: a sensor unit that acquires breath information, which is information indicating the breath of a user, without contacting the user; an abnormality detection unit that detects an abnormal breathing state of the user from the breathing information; a unit number acquisition unit that acquires the number of times of detection of an abnormal breathing state for each unit time zone having a predetermined length of time from a sleep time to a wake-up time; an average number-of-times acquisition unit that acquires an average number of times of detection, which is a value obtained by dividing the sum of the number of times of detection of an abnormal breathing state between a sleep time and a wake-up time by a time from the sleep time to the wake-up time; a maximum number-of-times acquisition unit that acquires a maximum number of detection times, which is the maximum value among the plurality of detection times acquired for each unit time band; and a health risk determination unit that determines the health risk of the user from both the average number of detections and the maximum number of detections.

Description

Technical Field

[0001] The present invention relates to a health risk determination system, an autonomic nerve determination system, a life improvement system, a sleeping posture determination system, a sleeping posture determination program, a bruxism detection system, and a bruxism detection program.

[0002] This application claims priority based on Japanese Patent Application No. 2022-212408 filed on December 28, 2022, Japanese Patent Application No. 2023-004346 filed on January 16, 2023, Japanese Patent Application No. 2023-021847 filed on February 15, 2023, Japanese Patent Application No. 2023-042106 filed on March 16, 2023, and Japanese Patent Application No. 2023-046741 filed on March 23, 2023, and incorporates by reference all the contents described in the aforementioned Japanese applications. Background Art

[0003] Patent Document 1 discloses an information processing apparatus that displays information indicating the possibility of blood pressure fluctuations due to sleep apnea syndrome. The information processing apparatus includes a measurement terminal and a sensor that are attached to the body of a subject and acquire predetermined measurement data from the subject.

[0004] Patent Document 2 discloses a wearable device attached to a user's wrist. The wearable device includes a pulse sensor that calculates biological information such as a pulse rate. The activity state of the autonomic nerve is calculated based on the pulse signal detected by the pulse sensor.

[0005] Patent Document 3 describes a sleep state detection system that detects a person's sleep state. The sleep state detection system includes: a radio wave transceiver that emits microwaves to a person in bed and then receives the reflected waves of the microwaves affected by the body pulsation of the person; and a computer that analyzes the reflected wave data of the reflected waves to detect the person's sleep state. The computer controls the operation stop of an air conditioner, a lighting device, and an audio device based on the detected sleep state.

[0006] Patent Document 4 describes a biological information display device. The biological information display device includes a rectangular sensor sheet and a control unit attached to an end of the sensor sheet. The biological information display device is used by being laid on a bed. The bed includes a placement portion for placing a bedding for laying and a back plate portion that stands up from an end of the placement portion, and the biological information display device is inserted under the bedding for laying placed on the placement portion for use.

[0007] Patent Document 5 describes a bruxism prevention device having a device body to be worn in the oral cavity. The device body has a piezoelectric sensor for detecting the contact state between the upper teeth and the lower teeth. The bruxism prevention device has a control unit for discriminating bruxism from the signals of the piezoelectric sensor.

[0008] Patent Document 6 describes a biological information detection device, which includes a vibration signal sensor unit for detecting the vibration of the body emitted by an animal. The vibration signal sensor unit is composed of at least one or more of a vibration sensing body, a bottom plate laminated on the upper and lower sides of the vibration sensor body, a buffer member, and a vibration collecting plate. The animal and the vibration sensor body do not contact each other. The biological information detection device separates the sound of bruxism from the signals detected by the vibration signal sensor unit. More specifically, the vibration signal sensor unit detects the signals emitted by the human body. Then, the following steps are performed: a step of preprocessing the detected signals; and a step of filtering the preprocessed signals to separate them into multiple signals, and obtaining at least two signals among respiratory vibration, heartbeat vibration, snoring, and body movement signals. In this biological information detection device, the above-mentioned snoring includes actions such as making bruxism sounds, sneezing, and talking in sleep.

[0009] Prior Art Documents

[0010] Patent Documents

[0011] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2018-149173

[0012] Patent Document 2: Japanese Unexamined Patent Application Publication No. 2018-543

[0013] Patent Document 3: Japanese Unexamined Patent Application Publication No. 2006-87850

[0014] Patent Document 4: Japanese Patent No. 3960298

[0015] Patent Document 5: Japanese Unexamined Patent Application Publication No. 2020-75018

[0016] Patent Document 6: Japanese Unexamined Patent Application Publication No. 2019-673 Summary of the Invention

[0017] Problems to be Solved by the Invention

[0018] In the above-described information processing apparatus, the measurement terminal and the sensor acquire measurement data from the subject in a state of being in contact with or close to a part of the subject's body. Therefore, there are cases where the state of contact between an object such as a sensor and the body lasts for a long time, so there is a high possibility that the burden on the subject will be great. This burden is particularly significant when trying to acquire measurement data from a sleeping subject. In the determination of health risks such as sleep apnea syndrome, it is required to accurately grasp the health risks of the subject.

[0019] Regarding the determination of the autonomic nerve during sleep, it is not always appropriate to use the same criteria as those for the determination of the autonomic nerve during waking. For example, during waking, it is desirable to have a good balance between the sympathetic nerve and the parasympathetic nerve. On the other hand, during sleep, the ideal state of the autonomic nerve may vary depending on the sleep state. Therefore, it is desirable to be able to determine the autonomic nerve corresponding to the sleep state.

[0020] The above-described sleep state detection system controls the operation stop of the air conditioner, the lighting device, and the audio device according to the sleep state of the user from going to bed to waking up. However, in order to improve the user's life, it may be required to predict the waking state after the user wakes up and to provide information necessary for improving the user's life.

[0021] The above-described biological information display device displays the time series data of the body position information and the sleeping posture on the display. However, if only the body position information and the sleeping posture are displayed, although the sleeping posture can be grasped, it is impossible to understand how to improve the sleeping posture. Therefore, there are cases where it is required to provide information that can improve the sleeping posture. Furthermore, it may also be required to improve the user's life by improving the sleeping posture.

[0022] In order to detect bruxism, the above-described bruxism prevention device must have the device main body installed in the oral cavity. Therefore, especially during sleep, there is a concern that the device main body being held in the oral cavity will cause a great burden on the subject being examined.

[0023] The above-described biological information detection device separates a signal emitted from the human body into multiple signals through pre-processing and filtering. Among the separated snoring signals, there are also sounds of bruxism, sneezing, talking in sleep, etc. Therefore, there is a concern that it is impossible to distinguish whether the signal detected from the human body is caused by bruxism, sneezing, or talking in sleep. Thus, there is still room for improvement in the detection accuracy of bruxism.

[0024] An object of the present invention is to provide a health risk determination system that can correctly grasp health risks while reducing the burden, an autonomic nerve determination system that can determine the autonomic nerves corresponding to the sleep state, a life improvement system that can predict the state of the user during waking and provide information for improving the user's life, a sleeping posture determination system and a sleeping posture determination program that can promote the improvement of the sleeping posture and provide useful information for improving life, a bruxism detection system and a bruxism detection program that can reduce the burden on the user and detect bruxism with high accuracy.

[0025] Means for Solving the Problems

[0026] (1) The health risk determination system of the present invention is a health risk determination system that determines the health risks caused by the abnormal breathing state of the user from the time of falling asleep to the time of waking up. The health risk determination system includes: a sensor unit that acquires information indicating the user's breathing, that is, breathing information, without contacting the user; an abnormality detection unit that detects the abnormal breathing state of the user based on the breathing information; a unit frequency acquisition unit that acquires the detection frequency of the abnormal breathing state for each unit time zone having a predetermined time length between the time of falling asleep and the time of waking up; an average frequency acquisition unit that acquires an average detection frequency, which is a value obtained by dividing the total value of the detection frequencies of the abnormal breathing state between the time of falling asleep and the time of waking up by the time from the time of falling asleep to the time of waking up; a maximum frequency acquisition unit that acquires a maximum detection frequency, which is the maximum value among the multiple detection frequencies acquired for each unit time zone; and a health risk determination unit that determines the health risks of the user from both the average detection frequency and the maximum detection frequency.

[0027] (2) The autonomic nerve determination system of the present invention is an autonomic nerve determination system that determines the autonomic nerves of the user from the time of going to bed to the time of getting up. The autonomic nerve determination system includes: a sensor unit that acquires at least one of information indicating the user's breathing, that is, breathing information, information indicating the user's body movement, that is, body movement information, information indicating the user's brain waves, that is, brain wave information, and information indicating the user's heart rate, that is, heart rate information; an autonomic nerve acquisition unit that acquires information indicating the state of the user's autonomic nerves, that is, autonomic nerve information, from the heart rate information; a sleep state acquisition unit that acquires the sleep state of the user from at least one of the breathing information, body movement information, heart rate information, and brain wave information; and an autonomic nerve determination unit that determines the autonomic nerves of the user from both the autonomic nerve information and the sleep state.

[0028] (3)A lifestyle improvement system according to one embodiment of the present invention is a lifestyle improvement system for improving the lifestyle of a user. The lifestyle improvement system includes: a sensor unit that acquires at least one of information indicating the user's respiration (i.e., respiration information), information indicating the user's body movement (i.e., body movement information), and information indicating the user's heartbeat (i.e., heartbeat information); a sleep state acquisition unit that acquires information indicating the user's sleep state (i.e., sleep state information) from at least one of the respiration information, body movement information, and heartbeat information; an autonomic nerve acquisition unit that acquires information indicating the state of the user's autonomic nerve (i.e., autonomic nerve information) from the heartbeat information; an instrument control unit that controls the operation of an instrument constituting the environment around the user based on at least one of the user's past sleep state information (i.e., past sleep information) and autonomic nerve information stored in advance; a prediction information generation unit that generates information indicating the predicted state of the waking user (i.e., prediction information) based on at least one of the past sleep information and autonomic nerve information; and an improvement information generation unit that generates information for improving the user's lifestyle (i.e., improvement information) based on the prediction information.

[0029] (4)Another lifestyle improvement system according to the present invention includes: a sensor unit that acquires information indicating the user's heartbeat (i.e., heartbeat information); an autonomic nerve acquisition unit that acquires information indicating the state of the user's autonomic nerve (i.e., autonomic nerve information) from the heartbeat information; a prediction information generation unit that generates information indicating the predicted state of the waking user (i.e., prediction information) based on the autonomic nerve information; and an improvement information generation unit that generates information for improving the user's lifestyle (i.e., improvement information) based on the prediction information.

[0030] (5)The sleeping posture determination system of the present invention includes: a sensor unit mounted on a bedding that bears the load of the body of the user of the bedding and outputs a waveform corresponding to the load; a sleeping posture determination unit that determines the sleeping posture of the user from the waveform output by the sensor unit; and a recommendation generation unit that generates a recommendation for improving the user's sleeping posture based on the sleeping posture determined by the sleeping posture determination unit.

[0031] (6)The sleeping posture determination program of the present invention causes a computer to execute the following steps: a step of determining the sleeping posture of the user from a waveform indicating the load of the user of the bedding output by a sensor unit mounted on the bedding; and a step of generating a recommendation for improving the user's sleeping posture based on the sleeping posture determined in the determining step.

[0032] (7) The bruxism detection system of the present invention detects the bruxism of a user from the moment of falling asleep to the moment of waking up. The bruxism detection system includes: a sensor unit that acquires vibration information indicating the vibration of the user without contacting the user; and a detection unit that extracts sign information indicating a sign occurring before bruxism from the vibration information, and detects bruxism from the vibration information and the sign information.

[0033] (8) The bruxism detection program of the present invention causes a computer to execute the following steps: a step of extracting sign information indicating a sign occurring before bruxism from vibration information indicating the vibration of the user acquired by the sensor unit; and a step of detecting bruxism from the vibration information and the sign information.

[0034] Effects of the Invention

[0035] According to the present invention, it is possible to provide a health risk determination system that can correctly grasp health risks while reducing the burden, an autonomic nerve determination system that can determine the autonomic nerve corresponding to the sleep state, a life improvement system that can predict the state of the user during waking and provide information for improving the user's life, a sleep posture determination system and a sleep posture determination program that can promote the improvement of the sleep posture and provide useful information for improving life, a bruxism detection system and a bruxism detection program that can reduce the burden on the user and detect bruxism with high precision. Description of the Drawings

[0036] Figure 1 is a block diagram showing an example of the health risk determination system.

[0037] Figure 2 (a) of is a perspective view of a pad on which the sensor unit can be mounted, Figure 2 (b) of is showing the composition Figure 2 (a) of is a perspective view of the core material of the pad shown in.

[0038] Figure 3 is a flowchart showing an example of the operation of the health risk determination system.

[0039] Figure 4 is a graph showing an example of time series data of the number of detected abnormal breathing states.

[0040] Figure 5 is a diagram showing an example of the determination criteria for health risks.

[0041] Figure 6 is a diagram showing an example of the display of health risks.

[0042] Figure 7 is a block diagram showing an example of the autonomic nerve determination system.

[0043] Figure 8FIG. (a) is a perspective view of a pad on which a sensor unit can be mounted. Figure 8 FIG. (b) shows the composition of Figure 8 FIG. (a) is a perspective view of the core material of the pad shown in FIG. (a).

[0044] Figure 9 is a flowchart showing an example of the operation of the autonomic nerve determination system.

[0045] Figure 10 is a table showing an example of the support database stored in the server.

[0046] Figure 11 is a diagram showing an example of the display of the determination result of the autonomic nerve during sleep and an example of the display of the determination result of the autonomic nerve when waking up.

[0047] Figure 12 is a diagram showing another example of the display of the determination result of the autonomic nerve.

[0048] Figure 13 is a table showing an example of the autonomic nerve database stored in the server.

[0049] Figure 14 is a block diagram showing an example of the life improvement system.

[0050] Figure 15 shows Figure 14 FIG. is a diagram showing an example of the input screen of the subjective information displayed on the information terminal shown.

[0051] Figure 16 FIG. (a) is a perspective view of a pad on which a sensor sheet can be mounted. Figure 16 FIG. (b) shows the composition of Figure 16 FIG. (a) is a perspective view of the core material of the pad shown in FIG. (a).

[0052] Figure 17 is a perspective view showing an example of the core material of a pad on which a pad sensor can be mounted.

[0053] Figure 18 shows Figure 14 FIG. is a diagram showing an example of the output screen of the prediction information and the improvement information displayed on the information terminal shown.

[0054] Figure 19 shows Figure 14 FIG. is a diagram showing an example of the output screen of the feedback information displayed on the information terminal shown.

[0055] Figure 20 is a flowchart showing an example of the operation of the life improvement system.

[0056] Figure 21(a) is a perspective view showing an example of a bedding and a sensor unit. Figure 21 (b) shows Figure 21 a perspective view of the core material of the bedding in (a) of

[0057] Figure 22 shows Figure 21 a plan view of the sensor unit in (a) of

[0058] Figure 23 shows an example of the mounting structure of the sensor unit with respect to Figure 21 the bedding in (a) of

[0059] Figure 24 shows Figure 22 a perspective view of the detailed structure of the sensor unit in

[0060] Figure 25 is a block diagram showing an example of a sleeping posture determination system and a sleeping posture determination program.

[0061] Figure 26 (a) of Figure 26 (b) of Figure 26 and (c) of Figure 21 show an example of waveforms obtained by the sensor unit in (a) of

[0062] Figure 27 shows an example of a screen displayed on the display unit of the sleeping posture determination system through Figure 25

[0063] Figure 28 shows an example of a screen displayed on the display unit of the sleeping posture determination system through Figure 25

[0064] Figure 29 shows an example of a screen displayed on the display unit of the sleeping posture determination system through Figure 25

[0065] Figure 30 shows an example of a screen displayed on the display unit of the sleeping posture determination system through Figure 25

[0066] Figure 31 shows an example of a screen displayed on the display unit of the sleeping posture determination system through Figure 25

[0067] Figure 32 shows an example of a screen displayed on the display unit of the sleeping posture determination system through Figure 25

[0068] Figure 33is a diagram showing an example of a screen displayed on a display unit of a sleeping position determination system through Figure 25 The diagram is an example of a screen displayed on a display unit of a sleeping position determination system through Figure 25 .

[0069] Figure 34 is a block diagram showing an example of a molar detection system.

[0070] Figure 35 In (a) of Figure 35 , it is a perspective view of a mattress on which a sensor unit can be installed. Figure 35 In (b) of Figure 35 , it shows Figure 35 a perspective view of the core material of the mattress shown in (a) of Figure 35 .

[0071] Figure 36 In (a) of Figure 36 , it is a schematic waveform diagram of teeth grinding. Figure 36 In (b) of Figure 36 , it is a schematic waveform diagram of turning over. Figure 36 In (c) of Figure 36 , it is a schematic waveform diagram at rest.

[0072] Figure 37 is a schematic waveform diagram showing the waveform of sign information generated due to an increase in heart rate before teeth grinding.

[0073] Figure 38 is a schematic diagram showing an example of a reference for the high or low occurrence frequency of teeth grinding.

[0074] Figure 39 is a flowchart showing an example of the operation of a molar detection system. Detailed Embodiments

[0075] Hereinafter, specific examples of the health risk determination system, autonomic nerve determination system, life improvement system, sleeping position determination system, sleeping position determination program, molar detection system, and molar detection program of the present invention will be described with reference to the accompanying drawings. The health risk determination system, autonomic nerve determination system, life improvement system, sleeping position determination system, sleeping position determination program, molar detection system, and molar detection program can be combined with each other. In the description of the accompanying drawings, the same or corresponding elements are denoted by the same reference numerals, and repeated descriptions are appropriately omitted. For the sake of easy understanding, some parts of the accompanying drawings may be simplified or exaggeratedly depicted, and the dimensional ratios, etc. are not limited to the content shown in the accompanying drawings.

[0076] First, an example of a health risk determination system will be described. As an example, the health risk determination system determines the health risks caused by the abnormal breathing state of the user. The health risk determination system determines the health risks of the user from the time of falling asleep to the time of waking up. The health risk determination system can be used, for example, for personal or home use, or for experimental research in research institutions, etc. In addition, the health risk determination system can also be used for treatment in hospitals, etc. The user refers to a person who is the object of health risk determination by the health risk determination system. The user is, for example: a person with health risks caused by an abnormal breathing state, a person receiving sleep treatment in a hospital, etc., or a person who wishes to have their own health risks determined.

[0077] Health risks refer to, for example, the risk of heart failure and the like occurring with sleep apnea syndrome (SAS: Sleep Apnea Syndrome). The abnormal breathing state refers to the breathing state during sleep that may pose health risks to the user. The abnormal breathing state includes, for example, apnea and hypopnea. Apnea refers to a state where the user's breathing stops for 10 seconds or more. Hypopnea refers to a state where the amplitude of the airflow of the user's breathing decreases by 30% or more, and the state where the arterial oxygen saturation (SpO2) decreases by 4% or more continues for 10 seconds or more.

[0078] Figure 1 FIG. 7 is a block diagram showing an example of the health risk determination system 1. The health risk determination system 1 can be accessed from information terminals such as a computer, a tablet terminal, a smartphone, a watch, or a wearable terminal. The health risk determination system 1 includes, for example, a sensor unit 11 and a health risk determination application 40.

[0079] The health risk determination application 40 is, for example, an application executed on the information terminal. The health risk determination application 40 can also be an application downloaded to the information terminal and executed on the information terminal. Hereinafter, an example will be described in which the health risk determination application 40 is downloaded to the application of the information terminal and the function of the health risk determination application 40 is executed on the information terminal.

[0080] The information terminal is, for example, a portable terminal. The portable terminal represents, for example, a portable information terminal such as a portable phone including a smart phone, a tablet, or a notebook personal computer. The information terminal may also be a terminal other than a portable terminal, for example, a desktop personal computer. An example of the information terminal includes a processor (such as a CPU) that executes an operating system (OS) and software (application programs), a main memory unit composed of a ROM and a RAM, an auxiliary memory unit composed of a flash memory, etc., a communication control unit composed of a wireless communication module, etc., an input device, and an output device such as a display. However, the configuration of the information terminal is not limited to the above description and can be appropriately changed.

[0081] Each function of the health risk determination application program 40 is implemented by causing the processor or the main memory unit to read a predetermined software and execute the software. The processor causes the aforementioned communication control unit, input device, or output device to operate according to the software, and reads and writes data to and from the main memory unit or the auxiliary memory unit. The data or database required for the execution of the functions of the health risk determination application program 40 is stored in the main memory unit or the auxiliary memory unit.

[0082] Each functional element of the information terminal is implemented by causing the processor or the memory unit (such as the aforementioned main memory unit or auxiliary memory unit) to read a predetermined software and execute the software. The processor causes the aforementioned communication control unit, input device, or output device to operate according to the software, and reads and writes data to and from the memory unit. The data or database used in the processing of the information terminal is stored in the memory unit.

[0083] The health risk determination application program 40 may be a distributed processing system composed of multiple computers, or a client-server system or a cloud system. As an example of the health risk determination application program 40, it includes, for example, a main module, a data acquisition module, a determination module, and an output module. By executing the data acquisition module, the determination module, and the output module, the functions of each functional element of the health risk determination application program 40 are exerted. As an example, the health risk determination application program 40 may be provided after being fixedly recorded on a tangible memory medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. The health risk determination application program 40 may also be provided via a communication network as a data signal superimposed on a carrier wave.

[0084] The sensor unit 11 can obtain information about the user's body. As an example, the sensor unit 11 obtains respiratory information, body movement information, and heartbeat information. The respiratory information is information indicating the user's respiration. The respiratory information may include, for example, the number of breaths per unit time (hereinafter simply referred to as the breathing rate). This unit time can be variable. The body movement information is information indicating the user's body movement. The heartbeat information is information indicating the user's heartbeat. The content of the information to be obtained by the sensor unit 11 can be appropriately changed. The sensor unit 11 can also obtain, for example, information indicating blood pressure.

[0085] Figure 2 (a) of which is a perspective view showing the pad 10 on which the sensor unit 11 is mounted, Figure 2 (b) of which shows the composition of Figure 2 the core material 2 of the pad 10 shown in (a) of which. As Figure 1 shown in Figure 2 (a) and Figure 2 (b) of which, the sensor unit 11 is, for example, a sensor sheet that can be attached to and detached from the pad 10. The sensor unit 11 obtains vital data of the user lying horizontally on the pad 10. As an example, the sensor unit 11 can obtain an electrocardiogram. The aforementioned respiratory information, body movement information, and heartbeat information are included in, for example, the above-mentioned vital data.

[0086] For example, the sensor unit 11 has a sheet-like fabric embroidered with a linear sensor, and the position of the sheet-like fabric relative to the pad 10 can be changed. The linear sensor of the sensor unit 11 is fixed by embroidery, for example, in a two-dimensionally extended manner on the sheet-like fabric. An example of the linear sensor is a piezoelectric sensor. In this case, an electrical signal corresponding to the load of the user's body applied to the piezoelectric sensor of the sensor unit 11 is generated, and the sensor unit 11 obtains this electrical signal as the aforementioned respiratory information, body movement information, and heartbeat information. Hereinafter, the electrical signal obtained as the respiratory information, body movement information, and heartbeat information is sometimes referred to as a signal.

[0087] The sensor unit 11 has, for example: the above-mentioned linear sensor (as an example, a piezoelectric sensor); and a communication unit that outputs the respiratory information, body movement information, and heartbeat information, which are electrical signals generated by the linear sensor, to the outside of the sensor unit 11. Although the above has been described by taking the sensor unit 11 having a piezoelectric sensor as an example. However, the type of the sensor of the sensor unit 11 is not limited to a piezoelectric sensor and is not particularly limited. For example, the sensor unit 11 can also include an acceleration sensor.

[0088] Respiratory information includes signals representing the user's body movement with breathing. The body movement with breathing represents the actions of the body with breathing. The sensor unit 11 detects the user's body movement with breathing. The sensor unit 11 outputs the signal representing the body movement with breathing to the outside of the sensor unit 11. Heartbeat information includes signals representing the user's body movement with heartbeat. The body movement with heartbeat represents the actions of the body with heartbeat. The sensor unit 11 detects the user's body movement with heartbeat. The sensor unit 11 outputs the signal representing the body movement with heartbeat to the outside of the sensor unit 11. Body movement information includes signals representing the user's body movement that is not with breathing and heartbeat. The body movement that is not with breathing and heartbeat represents the actions of the body that have no relation with breathing and heartbeat, such as the body movement caused by turning over. The sensor unit 11 detects the user's body movement that is not with breathing and heartbeat. The sensor unit 11 outputs the signal representing the body movement that is not with breathing and heartbeat to the outside of the sensor unit 11.

[0089] For example, the sensor unit 11 is used by being mounted on the mat 10 that bears the user's body. When viewed from above, the mat 10 is rectangular. The mat 10 extends in the long side direction D1 and the short side direction D2 orthogonal to the long side direction D1. The mat 10 has a thickness in the thickness direction D3 orthogonal to both the long side direction D1 and the short side direction D2.

[0090] The length of the mat 10 in the long side direction D1 is, for example, 120 cm or more and 210 cm or less (one example is 195 cm). The length of the mat 10 in the short side direction D2 is, for example, 70 cm or more and 180 cm or less (one example is 97 cm). The length of the mat 10 in the thickness direction D3 is, for example, 3 cm or more and 40 cm or less (one example is 9 cm).

[0091] As an example, the mat 10 is a mattress. The user of the mat 10 places his or her body on the mat 10. At this time, the direction in which the body of the lying user extends (that is, the direction connecting the user's head and feet) is, for example, the same as the long side direction D1 of the mat 10.

[0092] As Figure 2 shown in (b) of FIG., the mat 10 has a core material 2 housed inside a cover cloth 3 described later. As an example, the core material 2 is rectangular when viewed from above. The core material 2 has, for example, contents and a bag body that houses the contents. The contents are, for example, urethane foam or polyester. The material of the bag body is, for example, cotton or polyester.

[0093] The core material 2 is in a rectangular parallelepiped shape. The core material 2 has an upper surface 21 for bearing the user's body and a lower surface 22 on the side opposite to the upper surface 21. The upper surface 21 is a surface facing one side in the thickness direction D3 ( Figure 2 the upper side in (b) of). The lower surface 22 is a surface facing the other side in the thickness direction D3 ( Figure 2 the lower side in (b) of). In addition, the core material 2 has a plurality of side surfaces 23 connecting the upper surface 21 and the lower surface 22.

[0094] As Figure 2 shown in (a) of, the cushion body 10 has a cover cloth 3. As an example, the cover cloth 3 is in a bag shape and is rectangular when viewed from above. The cover cloth 3 has, for example, a fabric 31 covering the upper surface 21 of the core material 2, a lining 32 covering the lower surface 22 of the core material 2, and an opening / closing member 33 connecting the fabric 31 and the lining 32 to each other. The opening / closing member 33 is provided at a position of the cover cloth 3 corresponding to the side surface 23 of the core material 2. When viewed from the thickness direction D3, the opening / closing member 33 is located outside the side surface 23 of the core material 2. The cover cloth 3 can be attached to and detached from the core material 2. Attaching to and detaching from the core material includes rolling up the cover cloth relative to the core material and rolling up the cover cloth to expose at least a part of the core material.

[0095] An example of the opening / closing member 33 is a so-called double slider. That is, the opening / closing member 33 may have two slider members 34 and an opening / closing part 35 that separates or closes the fabric 31 and the lining 32 by the sliding of the slider member 34.

[0096] In this case, by sliding one slider member 34 along the opening / closing part 35 in one direction ( Figure 2 the clockwise direction in (a) of), and, by sliding the other slider member 34 along the opening / closing part 35 in the other direction ( Figure 2 the counterclockwise direction in (a) of), the fabric 31 and the lining 32 can be separated.

[0097] In addition, by sliding one slider member 34 along the opening / closing part 35 in the other direction, and, by sliding the other slider member 34 along the opening / closing part 35 in one direction, the fabric 31 and the lining 32 can be closed. The opening / closing member 33 may also be a so-called single slider. In this case, the opening / closing member 33 has one slider member 34 and an opening / closing part 35.

[0098] The sensor unit 11 is disposed, for example, inside the bag-shaped cover cloth 3. More specifically, the sensor unit 11 is disposed between the core material 2 and the cover cloth 3. When the sensor unit 11 is mounted on the mattress body 10, for example, the sensor unit 11 is configured to extend in the short side direction D2 of the mattress body 10. In this case, the sensor unit 11 is located on the side opposite to the user's body when viewed from the cover cloth 3 of the mattress body 10. Therefore, when the mattress body 10 bears the user's body, the sensor unit 11 does not contact the user. The sensor unit 11 acquires the user's respiration information, body movement information, and heartbeat information in a non-contact manner.

[0099] The sensor unit 11 is mounted at a position corresponding to the user's heart, for example, in the long side direction D1. As an example, the sensor unit 11 is mounted at an arbitrary position from the position bearing the user's head to the position away along the long side direction D1 of the mattress body 10 ( Figure 2 the lower right position in (a)). The mounting position of the sensor unit 11 in the long side direction D1 is changeable. The distance from the position bearing the user's head to the position where the sensor unit 11 is mounted can be, for example, 40 cm or more and 50 cm or less (an example is 50 cm).

[0100] The health risk determination application program 40 obtains the user's sleep time and wake-up time from, for example, the respiration information, body movement information, and heartbeat information. The sleep time refers to the time when the user has fallen asleep. The wake-up time refers to the time when the user has woken up. The health risk determination application program 40 can also determine the sleep time and obtain the sleep time from, for example, the body movement information obtained through the sensor unit 11 and the movement of the bedding detected by the acceleration sensor of the sensor unit 11. The acquisition of the wake-up time is the same. Alternatively, different from the above example, the user operates the health risk determination application program 40 on the information terminal, and the sleep time and wake-up time are obtained through the user's operation.

[0101] Figure 1 The shown health risk determination system 1 includes a first amplifier 12 and a second amplifier 13. The first amplifier 12 and the second amplifier 13 amplify the signals representing the body movement with respiration, the signals representing the body movement with heartbeat, and the signals representing the body movement not with respiration and heartbeat output from the sensor unit 11. The first amplifier 12 and the second amplifier 13 output the amplified signals (each of the signals representing the body movement with respiration, the signals representing the body movement with heartbeat, and the signals representing the body movement not with respiration and heartbeat) to the health risk determination application program 40. For example, the first amplifier 12 and the second amplifier 13 output the amplified signals to the sleep stage acquisition unit 14 described later.

[0102] As an example, the gain of the first amplifier 12 is greater than the gain of the second amplifier 13. However, the gains of the first amplifier 12 and the second amplifier 13 only need to be different from each other. For example, the gain of the second amplifier 13 may also be greater than the gain of the first amplifier 12.

[0103] The health risk determination application program 40 has, for example: a sleep stage acquisition unit 14, an abnormality detection unit 15, a per-occurrence acquisition unit 16, an average-occurrence acquisition unit 17, a maximum-occurrence acquisition unit 18, and a health risk determination unit 19.

[0104] The sleep stage acquisition unit 14 acquires the sleep stage of the user. The sleep stage is a stage indicating the depth of the user's sleep. The sleep stage is classified into, for example, wakefulness, rapid-eye-movement sleep (REM), and non-rapid-eye-movement sleep. The non-rapid-eye-movement sleep is classified into four sleep stages. The sleep stage acquisition unit 14 acquires the sleep stage from the respiration information, body movement information, and heartbeat information acquired by the sensor unit 11. The sleep stage acquisition unit 14 acquires the sleep state using either the signal amplified by the first amplifier 12 or the signal amplified by the second amplifier 13. For example, when the user's respiration suddenly becomes large, the signal amplified by the first amplifier 12 may reach saturation all at once. At this time, the sleep stage acquisition unit 14 acquires the signal amplified by the second amplifier 13, which has a gain smaller than the gain of the first amplifier 12.

[0105] The abnormality detection unit 15 detects an abnormal respiration state of the user. The abnormality detection unit 15 detects the abnormal respiration state from the respiration information acquired by the sensor unit 11. The abnormality detection unit 15 detects the abnormal respiration state of the user from, for example, the signal indicating body movement with respiration, the signal indicating body movement with heartbeat, and the signal indicating body movement not with respiration and heartbeat output from the sensor unit 11. The abnormality detection unit 15 detects the abnormal respiration state from, for example, the respiration rate of the user.

[0106] The abnormality detection unit 15 can pre-store abnormal respiration data and normal respiration data. The abnormal respiration data is, for example, information indicating body movement along with respiration, information indicating body movement along with heartbeat, and information indicating body movement that is not along with respiration and heartbeat during the sleep of a person suffering from SAS. The normal respiration data is information indicating body movement along with respiration, information indicating body movement along with heartbeat, and information indicating body movement that is not along with respiration and heartbeat during the sleep of a person not suffering from SAS. The abnormality detection unit 15 can pre-store an algorithm for calculating the respiration rate of the user from the respiration information, abnormal respiration data, and normal respiration data. In this case, the abnormality detection unit 15 can detect an abnormal respiration state from the respiration rate of the user.

[0107] Each of the abnormal respiration data and the normal respiration data can also be generated, for example, by the sensor unit 11 detecting the body movement of a person during sleep. The number of such persons can be one or multiple (an example is 12 persons). The abnormality detection unit 15 can refer to the abnormal respiration data and the normal respiration data and determine the respiration rate of the user from the respiration information obtained by the sensor unit 11 using the aforementioned algorithm.

[0108] However, when changing from an abnormal respiration state to a normal respiration state that is not an abnormal respiration state, there is a tendency for the user's body movement to occur. Therefore, as an example, the abnormality detection unit 15 detects an abnormal respiration state from the body movement information obtained by the sensor unit 11. The abnormality detection unit 15 detects the abnormal respiration state of the user, for example, from a signal indicating body movement along with respiration, a signal indicating body movement along with heartbeat, and a signal indicating body movement that is not along with respiration and heartbeat output from the sensor unit 11. The abnormality detection unit 15 detects the abnormal respiration state, for example, from the body movement of the user when changing from an abnormal respiration state to a normal respiration state.

[0109] In addition, when changing from an abnormal respiration state to a normal respiration state, the user's heart rate tends to suddenly increase. As an example, the abnormality detection unit 15 detects an abnormal respiration state from the heart rate information obtained by the sensor unit 11. The abnormality detection unit 15 detects the abnormal respiration state of the user, for example, from a signal indicating body movement along with heartbeat output from the sensor unit 11. The abnormality detection unit 15 detects the abnormal respiration state, for example, from the increase in the user's heart rate when changing from an abnormal respiration state to a normal respiration state.

[0110] As an example, the abnormal detection unit 15 detects an abnormal breathing state of the user based on the breathing rate of the user obtained from the breathing information, the body movement of the user when changing from the abnormal breathing state to the normal breathing state obtained from the body movement information, and the increase in the heart rate of the user when changing from the abnormal breathing state to the normal breathing state obtained from the heart rate information. In this way, the abnormal detection unit 15 can detect the abnormal breathing state of the user not only from the breathing information, but also by adding the body movement information and the heart rate information.

[0111] The unit - time - band acquisition unit 16 acquires the detection frequency of the abnormal breathing state detected by the abnormal detection unit 15 for each unit time - band from the sleep time to the wake - up time. The unit time - band is a time - band with a predetermined time length. An example of the time length of the unit time - band is one hour. For example, the time length of the unit time - band can be changed and can be set in advance by the user. The user is, for example, the user, a relative of the user including the user's family, or a doctor treating the user. The shorter the time length of the unit time - band, the more detailed the investigation of the user's sleep can be, and the higher the accuracy of the health risk determination can be. On the other hand, the longer the time length of the unit time - band, the lower the processing volume of the health risk determination system 1 can be.

[0112] The average - frequency acquisition unit 17 acquires the average detection frequency from the detection result of the abnormal detection unit 15. The average detection frequency is the value obtained by dividing the total value of the detection frequencies of the abnormal breathing state from the sleep time to the wake - up time by the time from the sleep time to the wake - up time. When the time length of the unit time - band is one hour, the average detection frequency corresponds to the so - called AHI (Apnea - Hypopnea Index).

[0113] The maximum - frequency acquisition unit 18 acquires the maximum detection frequency from the detection result of the abnormal detection unit 15. The maximum detection frequency is the maximum value among the detection frequencies of the abnormal breathing state obtained for each unit time - band. For example, the maximum - frequency acquisition unit 18 acquires the detection frequency of the abnormal breathing state for each unit time - band. Then, the maximum - frequency acquisition unit 18 acquires the detection frequency of the abnormal breathing state in the unit time - band with the most detection frequencies as the maximum detection frequency.

[0114] The health risk determination unit 19 determines the health risk of the user. The health risk determination unit 19 determines the health risk of the user from both the average detection frequency obtained by the average - frequency acquisition unit 17 and the maximum detection frequency obtained by the maximum - frequency acquisition unit 18. The health risk determination unit 19 determines, for example, the level of the health risk from both the average detection frequency and the maximum detection frequency. The health risk determination unit 19 determines the health risk in, for example, three levels: high, medium, and low.

[0115] As an example, the health risk determination unit 19 determines the health risk from the detection results of the abnormality detection unit 15 and the sleep stages obtained by the sleep stage acquisition unit 14. For example, the health risk determination unit 19 determines the health risk from the time when the abnormality detection unit 15 detects an abnormal breathing state and the sleep stage obtained by the sleep stage acquisition unit 14 at that time.

[0116] The health risk determination system 1 includes a display unit 20. The display unit 20 causes, for example, the information generated by the health risk determination application program 40 to be displayed on the display of the information terminal device. The display unit 20 can cause the information to be displayed on the display of, for example, a computer, a tablet terminal device, a smart phone, or a wearable terminal device held by the user, which is an example of the information terminal device.

[0117] The display unit 20 displays the health risk determined by the health risk determination unit 19. The display unit 20 causes the health risk to be displayed on the display of the information terminal device. The display unit 20 displays, for example, information indicating the magnitude of the risk over a long period and information indicating the magnitude of the risk over a short period. The risk over a long period refers to the health risk of the user determined using the number of detections of abnormal breathing states from the time of falling asleep to the time of waking up. The risk over a short period refers to the health risk of the user determined using the number of detections of abnormal breathing states obtained for each unit time band. The display unit 20 calculates the risk over a long period from the average number of detections obtained by, for example, the average number acquisition unit 17. The display unit 20 calculates the risk over a short period from the maximum number of detections obtained by, for example, the maximum number acquisition unit 18.

[0118] As an example, the display unit 20 displays information indicating the level of the health risk. The display unit 20 displays, for example, the level of the health risk determined by the health risk determination unit 19. The display unit 20 obtains information indicating the level of the health risk of the user from, for example, the health risk determination unit 19. The display unit 20 displays the health risk in, for example, three levels: high, medium, and low.

[0119] As an example, the display unit 20 displays information indicating the relationship between the occurrence time of the abnormal breathing state and the sleep stage. The display unit 20 displays information indicating the relationship between the occurrence time of the abnormal breathing state and the sleep stage based on the detection time of the abnormal breathing state detected by the abnormality detection unit 15 and the sleep stage obtained by the sleep stage acquisition unit 14 at the detection time. The information indicating the magnitude of the risk over a long period, the information indicating the magnitude of the risk over a short period, the information indicating the level of the health risk, and the information indicating the relationship between the occurrence time of the abnormal breathing state and the sleep stage will be described in detail later.

[0120] The communication between the sensor unit 11 and the first amplifier 12 and the second amplifier 13, the communication between the first amplifier 12 and the second amplifier 13 and the health risk determination application 40, and the communication between the health risk determination application 40 and the display unit 20 can be realized, for example, through a wireless communication interface such as a wireless LAN (Local Area Network) or Bluetooth (registered trademark). In addition, the above communication can also be realized in a wired manner.

[0121] Next, an example of the operation of the health risk determination system 1 will be described. Figure 3 FIG. 5 is a flowchart showing an example of the operation of the health risk determination system 1. Before operating the health risk determination system 1, the sensor unit 11 is first installed on the mat 10. The sensor unit 11 is installed on the mat 10 in such a way that the sensor unit 11 does not come into contact with the user when the user's body is placed on the mat 10. For example, the sensor unit 11 is disposed between the core material 2 and the cover cloth 3. Next, the user's body lies horizontally on the mat 10 on which the sensor unit 11 is installed. For example, the user's body is placed on the mat 10 in contact with the fabric 31 of the cover cloth 3.

[0122] Next, the sensor unit 11 obtains respiration information, body movement information, and heartbeat information from the user's body movement with respiration, body movement with heartbeat, and body movement not with respiration and heartbeat (step S1). In step S1, the sensor unit 11 detects the user's body movement with respiration, body movement with heartbeat, and body movement not with respiration and heartbeat. The sensor unit 11 outputs a signal representing the body movement with respiration, a signal representing the body movement with heartbeat, and a signal representing the body movement not with respiration and heartbeat to the outside of the sensor unit 11.

[0123] Next, the first amplifier 12 and the second amplifier 13 amplify the signal representing the body movement with respiration, the signal representing the body movement with heartbeat, and the signal representing the body movement not with respiration and heartbeat output from the sensor unit 11. Then, the amplified signal (each signal of the signal representing the body movement with respiration, the signal representing the body movement with heartbeat, and the signal representing the body movement not with respiration and heartbeat) is output to the health risk determination application 40. The first amplifier 12 and the second amplifier 13 output the amplified signal to the sleep stage acquisition unit 14, for example.

[0124] Next, the sleep stage acquisition unit 14 acquires the user's sleep stage from the respiration information, body movement information, and heartbeat information acquired by the sensor unit 11 (step S2). The sleep stage acquisition unit 14 acquires the sleep stage from either the signal amplified by the first amplifier 12 or the signal amplified by the second amplifier 13, for example.

[0125] Then, the abnormality detection unit 15 detects an abnormal breathing state from the respiration information, body movement information, and heartbeat information acquired by the sensor unit 11 (step S3). As an example, the abnormality detection unit 15 detects the user's abnormal breathing state based on the user's respiration rate obtained from the respiration information, the user's body movement when changing from the abnormal breathing state to the normal breathing state obtained from the body movement information, and the increase in the user's heartbeat rate when changing from the abnormal breathing state to the normal breathing state obtained from the heartbeat information.

[0126] Next, the unit count acquisition unit 16 acquires the detection count of the abnormal breathing state detected by the abnormality detection unit 15 for each unit time zone between the sleep onset time and the wake-up time (step S4). Next, the average count acquisition unit 17 acquires the average detection count from the detection result of the abnormality detection unit 15 (step S5). Next, the maximum count acquisition unit 18 acquires the maximum detection count from the detection result of the abnormality detection unit 15 (step S6). In other words, in step S6, the maximum count acquisition unit 18 acquires the detection count of the abnormal breathing state for each unit time zone from the detection result of the abnormality detection unit 15. And the maximum count acquisition unit 18 acquires the detection count of the abnormal breathing state in the unit time zone with the largest detection count as the maximum detection count.

[0127] Refer to Figure 4 to illustrate a specific example of the operations in steps S4 to S6. Figure 4 is a graph showing an example of time series data of the detection count of the abnormal breathing state. Figure 4 The vertical axis shown represents the detection count of the abnormal breathing state detected by the abnormality detection unit 15, and the horizontal axis represents the time from the sleep onset time. Figure 4 In the example of, the time from the sleep onset time to the wake-up time (i.e., the user's sleep time) is eight hours. In addition, the time length of the unit time zone is one hour.

[0128] In the unit time band from the moment of falling asleep to the point one hour later, the detection count is 0. In the unit time band from the point one hour after the moment of falling asleep to the point two hours after, the detection count is 0. In the unit time band from the point two hours after the moment of falling asleep to the point three hours after, the detection count is 10. In the unit time band from the point three hours after the moment of falling asleep to the point four hours after, the detection count is 50.

[0129] In the unit time band from the point four hours after the moment of falling asleep to the point five hours after, the detection count is 60. In the unit time band from the point five hours after the moment of falling asleep to the point six hours after, the detection count is 40. In the unit time band from the point six hours after the moment of falling asleep to the point seven hours after, the detection count is 0. In the unit time band from the point seven hours after the moment of falling asleep to the point eight hours after, the detection count is 0.

[0130] The total value of the detection counts of the abnormal breathing states between the moment of falling asleep and the moment of waking up is 160. The time from the moment of falling asleep to the moment of waking up is eight hours. Therefore, Figure 4 in the example of, the average detection count obtained by the average count acquisition unit 17 is 20. In addition, among the aforementioned multiple unit time bands, the unit time band from the point four hours after the moment of falling asleep to the point five hours after has the largest detection count. Therefore, Figure 4 in the example of, the maximum detection count obtained by the maximum count acquisition unit 18 is 60.

[0131] Next, as Figure 3 shown, the health risk determination unit 19 determines the user's health risk from both the average detection count obtained by the average count acquisition unit 17 and the maximum detection count obtained by the maximum count acquisition unit 18 (step S7). The health risk determination unit 19 determines the level of health risk from both the average detection count and the maximum detection count. The health risk determination unit 19 determines the health risk at three levels, for example, high, medium, and low.

[0132] Refer to Figure 5 to illustrate a specific example of the operation when the health risk determination unit 19 determines the level of health risk. Figure 5 is a schematic diagram showing an example of the determination criteria for health risk. Figure 5The high, medium, and low text shown indicates the level of health risk. In addition, X1, X2, X3, Y1, Y2, and Y3 are predetermined natural numbers. As an example, X1 is 20, X2 is 45, and X3 is 65. As an example, Y1 is 15, Y2 is 30, and Y3 is 45. The values of X1 to X3 and Y1 to Y3 can be changeable values.

[0133] When the average detection count is 0 or more and less than Y1, and the maximum detection count is 0 or more and less than X1, the health risk determination unit 19 determines that the health risk is low. At this time, the health risk determination unit 19 determines the level of the health risk as low. When the average detection count is Y2 or more and the maximum detection count is X1 or more and less than X3, when the average detection count is Y1 or more and less than Y3 and the maximum detection count is X2 or more, and when the average detection count is Y3 or more and the maximum detection count is X3 or more, the health risk determination unit 19 determines that the health risk is high. At this time, the health risk determination unit 19 determines the stage of the health risk as high. In other cases, the health risk determination unit 19 determines that the health risk is medium. At this time, the health risk determination unit 19 determines the stage of the health risk as medium.

[0134] In step S7, the health risk determination unit 19 determines the health risk from the detection result of the abnormality detection unit 15 and the sleep stage obtained by the sleep stage acquisition unit 14. The health risk determination unit 19 obtains, for example, the time when the abnormality detection unit 15 detects an abnormal breathing state and the sleep stage obtained by the sleep stage acquisition unit 14 at that time.

[0135] Next, the display unit 20 displays the health risk determined by the health risk determination unit 19 (step S8). As described above, the display unit 20 displays at least one of information indicating the magnitude of the long-term risk, information indicating the magnitude of the short-term risk, information indicating the level of the health risk, and information indicating the relationship between the occurrence of the abnormal breathing state and the sleep stage.

[0136] Refer to Figure 6 to illustrate a specific example of the operation in step S8. Figure 6 is a diagram showing an example of the display of the health risk by the display unit 20. Figure 6 In the example of, the display unit 20 displays a vertical axis indicating the magnitude of the long-term risk and a horizontal axis indicating the magnitude of the short-term risk. The display unit 20 displays a plurality (an example is nine) of squares arranged in a matrix. This square corresponds to Figure 5 the square shown. Figure 6 The vertical axis indicating the magnitude of the long-term risk shown corresponds to Figure 5 the vertical axis indicating the average detection count shown. Figure 6 The horizontal axis indicating the magnitude of the short-term risk shown corresponds toFigure 5 corresponds to the horizontal axis showing the maximum detection times as shown.

[0137] As an example, the display unit 20 displays the health risk determination result by displaying the point P on any one of the plurality of squares as shown. Figure 6 In this way, the display unit 20 displays information indicating the magnitude of the risk for a long time and information indicating the magnitude of the risk for a short time. The display unit 20 calculates the risk for a long time from the average detection times obtained by, for example, the average times acquisition unit 17. The display unit 20 determines the position of the point P in the direction along the vertical axis ( Figure 6 the up-down direction of the paper surface in the figure) from the average detection times obtained by the average times acquisition unit 17. The display unit 20 calculates the risk for a short time from the maximum detection times obtained by, for example, the maximum times acquisition unit 18. The display unit 20 determines the position of the point P in the direction along the horizontal axis ( Figure 6 the left-right direction of the paper surface in the figure) from the maximum detection times obtained by the maximum times acquisition unit 18. Then, the display unit 20 displays the point P in any one of the plurality of squares according to the determined position in the direction along the vertical axis and the position in the direction along the horizontal axis.

[0138] Figure 6 This shows an example of the display made by the display unit 20 when the average detection times are above Y2 and less than Y3 and the maximum detection times are above Y2 and less than Y3. Therefore, the display unit 20 displays the point P in the upper right square among the nine squares located in Figure 6 the figure.

[0139] In step S8, the display unit 20 displays information indicating the level of health risk determined by, for example, the health risk determination unit 19. The display unit 20 obtains information indicating the level of the user's health risk from, for example, the health risk determination unit 19. The display unit 20 represents the level of the user's health risk as three levels: high, medium, and low. In Figure 5 and Figure 6 this example, the display unit 20 displays that the level of the user's health risk is high.

[0140] In step S8, the display unit 20 displays information indicating the relationship between the occurrence of an abnormal breathing state and the sleep stage, for example. The display unit 20 displays information indicating the relationship between the occurrence of an abnormal breathing state and the sleep stage based on, for example, the time when an abnormal breathing state is detected obtained by the health risk determination unit 19 and the sleep stage at that time. In this case, the display unit 20 may display the sleep stage in which the detection count of the abnormal breathing state detected by the abnormality detection unit 15 has increased or decreased as information indicating the relationship between the occurrence of the abnormal breathing state and the sleep stage. For example, the display unit 20 displays information indicating that the detection count of the abnormal breathing state has increased or decreased when M hours (M is a natural number, for example, 4) have elapsed since the sleep start time and the sleep depth is N (N is a natural number, for example, 3).

[0141] For example, the display unit 20 may display the possibility that the abnormal breathing state affects the user's sleep quality. For example, when the detection count of the abnormal breathing state is high during the user's deep non-rapid eye movement sleep, the display unit 20 may display information indicating that the sleep quality may deteriorate due to the abnormal breathing state as information indicating the relationship between the occurrence of the abnormal breathing state and the sleep stage.

[0142] Above, an example of the operation of the health risk determination system 1 has been described. However, the content and order of each step of the operation of the health risk determination system are not limited to the foregoing example and may be changed appropriately.

[0143] Next, the effects of the health risk determination system 1 will be described. In this health risk determination system 1, the sensor unit 11 acquires the breathing information of the user without contacting the user. Compared with the case where the sensor unit 11 contacts the user, the burden can be reduced.

[0144] The health risk determination system 1 determines the health risk of the user based on both the average detection count obtained by the average count acquisition unit 17 and the maximum detection count obtained by the maximum count acquisition unit 18. For example, there is a possibility that the user has an abnormal breathing state concentrated in a short period from the sleep start time to the wake-up time. In this case, when determining the health risk of the user only from the average detection count, there is a possibility that the health risk cannot be correctly grasped. In the health risk determination system 1, the health risk of the user is determined by adding the average detection count and the maximum detection count, so even in the case where the abnormal breathing state is concentrated in a short period, the health risk can be correctly grasped.

[0145] For example, as Figure 4 shown, there is a possibility that the user has an abnormal breathing state concentrated in a short period. Figure 4 In the example of, the average detection count is 20.Figure 4 If the state of Figure 5 is determined only according to the determination criterion of the average detection times shown, the health risk of the user may be determined to be medium (the average detection times are above Y1 and less than Y2). In contrast, in the case where the health risk is determined from both the average detection times and the maximum detection times as in the health risk determination system 1, the health risk determination unit 19 will determine the health risk of the user to be high (the average detection times are above Y1 and less than Y2 and the maximum detection times are above X2). In this way, the health risk determination system 1 can more accurately determine the health risk of the user.

[0146] As an example, the sensor unit 11 acquires information indicating the body movement of the user, that is, body movement information, and information indicating the heartbeat of the user, that is, heartbeat information. The abnormality detection unit 15 detects an abnormal breathing state from the body movement information and the heartbeat information. In this case, in addition to from the breathing information, the abnormal breathing state can also be detected from the body movement information and the heartbeat information. For example, the abnormal breathing state can be detected based on the body movement of the user and the increase in the heartbeat number of the user when changing from the abnormal breathing state to the normal breathing state. Therefore, the abnormal breathing state of the user can be detected more accurately.

[0147] As an example, the health risk determination system 1 further includes: a sleep stage acquisition unit 14 that determines the sleep stage of the user from the breathing information, the body movement information, and the heartbeat information. The health risk determination unit determines the health risk of the user from the detection result of the abnormality detection unit 15 and the sleep stage. In this case, in addition to from the detection result of the abnormal breathing state, the health risk can also be determined from the sleep stage. For example, advice on the health risk can be given to the user from the perspective of the relationship between the occurrence of the abnormal breathing state and the sleep stage.

[0148] As an example, the health risk determination system 1 further includes: a first amplifier 12 and a second amplifier 13 that amplify the signal and output the amplified signal to the sleep stage acquisition unit 14. The gain of the first amplifier 12 is larger than the gain of the second amplifier 13.

[0149] For example, when changing from the abnormal breathing state to the normal breathing state, the breathing of the user tends to become larger. Assuming that the health risk determination system 1 only includes the first amplifier 12 with a predetermined gain, there is a possibility that the signal amplified by the first amplifier 12 will reach saturation all at once when the breathing of the user becomes larger. In contrast, by including the second amplifier 13 with a gain smaller than the gain of the first amplifier 12, when the signal of the first amplifier 12 is saturated, the signal can be amplified by the second amplifier 13 and output to the sleep stage acquisition unit 14. Therefore, the sleep stage can be acquired more accurately.

[0150] Next, a specific example of the autonomic nerve determination system will be described. As an example, the autonomic nerve determination system determines the autonomic nerves of a user from the time of going to bed to the time of waking up. The autonomic nerve determination system can be used, for example, for personal or household use, or for experimental research in research institutions, etc. In addition, the autonomic nerve determination system can also be used for treatment in hospitals, etc. A user refers to a person who is the object to be determined for autonomic nerves by the autonomic nerve determination system. Examples of users include: a person with a health risk caused by disorders of the autonomic nerves, etc., a person receiving sleep treatment in a hospital, etc., or a person who wishes to determine their own autonomic nerves.

[0151] The autonomic nerves refer to the nerves that control the operations of organs such as the heart or stomach, or involuntary functions such as blood circulation. The so-called involuntary here means, for example, that it cannot be as one wishes or cannot be according to one's own will. The autonomic nerves are composed of the sympathetic nerves and the parasympathetic nerves. The sympathetic nerves and the parasympathetic nerves adjust while maintaining a balance with each other. Hereinafter, the balance between the sympathetic nerves and the parasympathetic nerves is sometimes simply referred to as the balance of the autonomic nerves. For example, a good balance of the autonomic nerves means that the sympathetic nerves and the parasympathetic nerves are in an antagonistic operating state when awake, and the parasympathetic nerves are in a superior position compared to the sympathetic nerves when falling asleep.

[0152] The sympathetic nerves operate when excited, tense, or under stress, etc. Through the operation of the sympathetic nerves, the body shows reactions such as a faster heart rate, shallower and faster breathing, blood vessel constriction, or a rise in blood pressure. The parasympathetic nerves operate when relaxed, etc. Through the operation of the parasympathetic nerves, the body shows reactions such as a slower heart rate, deeper and slower breathing, blood vessel dilation, a drop in blood pressure, or an increase in immune function. Such autonomic nerve reactions cannot be intentionally controlled by human will, so they are used as objective indicators of emotions, feelings, fatigue, and stress, etc.

[0153] Figure 7 It shows a block diagram of the autonomic nerve determination system as an example. The autonomic nerve determination system 101 can be accessed from an information terminal such as a computer, a tablet terminal, a smartphone, a watch, or a wearable terminal. The autonomic nerve determination system 101 is connected to an external server 201 of the autonomic nerve determination system 101, for example, in a communicable manner. The autonomic nerve determination system 101 includes, for example, a sensor unit 111 and an autonomic nerve determination application program 120.

[0154] The autonomic nerve determination application program 120 is an application program executed on, for example, an information terminal device. The autonomic nerve determination application program 120 can be an application program downloaded to the information terminal device and executed on the information terminal device, or can be executed on the server 201. The autonomic nerve determination application program 120 can also be an application program downloaded from the server 201. Hereinafter, taking the autonomic nerve determination application program 120 as an application program downloaded to the information terminal device and the function of the autonomic nerve determination application program 120 being executed on the information terminal device as an example for explanation.

[0155] Each function of the autonomic nerve determination application program 120 is realized by causing a processor or a main memory unit to read a predetermined software and execute the software. The data or database required for the execution of the functions of the autonomic nerve determination application program 120 is stored in the main memory unit or the auxiliary memory unit.

[0156] The autonomic nerve determination application program 120 can be a distributed processing system composed of multiple computers, or can be a client-server system or a cloud system. As an example, the autonomic nerve determination application program 120 includes, for example, a main module, a data acquisition module, a determination module, and an output module. By executing the data acquisition module, the determination module, and the output module, the functions of each functional element of the autonomic nerve determination application program 120 are exerted. As an example, the autonomic nerve determination application program 120 can be provided after being fixedly recorded on a tangible memory medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. The autonomic nerve determination application program 120 can also be provided as a data signal superimposed on a carrier wave via a communication network.

[0157] The sensor unit 111 acquires information about the user's body. The sensor unit 111 acquires at least one of respiratory information, body movement information, heartbeat information, and brain wave information. As an example, the sensor unit 111 acquires respiratory information, body movement information, and heartbeat information. The brain wave information is information indicating the user's brain waves. The content of the information to be acquired by the sensor unit 111 can be appropriately changed, for example. The sensor unit 111 can also acquire information indicating blood pressure, for example.

[0158] Figure 8 (a) of is a perspective view showing the pad 110 on which the sensor unit 111 can be installed. Figure 8 (b) of is showing the composition Figure 8 (a) of the pad 110 shown in is a perspective view of the core material 102. As Figure 7 , Figure 8 (a) of and Figure 8 (b) of show that the sensor unit 111 is, for example, a sensor sheet that can be attached to and detached from the pad 110. The sensor unit 111 acquires the vital sign data of the user lying on the pad 110. As an example, the sensor unit 111 can acquire an electrocardiogram.

[0159] For example, the sensor unit 111 has a sheet-like fabric with embroidered wire sensors, and the position of the sheet-like fabric relative to the cushion body 110 is changeable. The wire sensors of the sensor unit 111 are fixed by embroidery, for example, in a two-dimensionally extended manner on the sheet-like fabric. For example, the piezoelectric sensors of the sensor unit 111 generate electrical signals corresponding to the load of the user's body applied thereto, and the sensor unit 111 acquires the electrical signals as the aforementioned respiration information, body movement information, and heartbeat information.

[0160] The sensor unit 111 includes, for example: the aforementioned wire sensors (one example being piezoelectric sensors); and a communication unit that outputs the respiration information, body movement information, and heartbeat information, which are electrical signals generated by the wire sensors, to the outside of the sensor unit 111. Although the above has been described by taking the sensor unit 111 having piezoelectric sensors as an example, the type of sensors of the sensor unit 111 is not limited to piezoelectric sensors and is not particularly limited. For example, the sensor unit 111 may also include acceleration sensors.

[0161] The sensor unit 111 detects the body movement of the user along with respiration. The sensor unit 111 outputs the signal indicating the body movement along with respiration to the outside of the sensor unit 111. The sensor unit 111 detects the body movement of the user along with heartbeat. The sensor unit 111 outputs the signal indicating the body movement along with heartbeat to the outside of the sensor unit 111. The sensor unit 111 detects the body movement of the user that is not along with respiration and heartbeat. The sensor unit 111 outputs the signal indicating the body movement that is not along with respiration and heartbeat to the outside of the sensor unit 111.

[0162] For example, the sensor unit 111 is used by being mounted on the cushion body 110 that bears the user's body. The cushion body 110 is rectangular when viewed from above. The cushion body 110 extends in the long side direction D101 and the short side direction D102 that is orthogonal to the long side direction D101. The cushion body 110 has a thickness in the thickness direction D103 that is orthogonal to both the long side direction D101 and the short side direction D102.

[0163] The length of the cushion body 110 in the long side direction D101 is, for example, 120 cm or more and 210 cm or less (one example being 195 cm). The length of the cushion body 110 in the short side direction D102 is, for example, 70 cm or more and 180 cm or less (one example being 97 cm). The length of the cushion body 110 in the thickness direction D103 is, for example, 3 cm or more and 40 cm or less (one example being 9 cm).

[0164] As an example, the cushion body 110 is a mattress. The user of the cushion body 110 places his or her body on the cushion body 110. At this time, the direction in which the body of the user lying horizontally extends (that is, the direction connecting the head and feet of the user) is, for example, the same as the long side direction D101 of the cushion body 110.

[0165] As Figure 8 (b) shows, the cushion body 110 has a core material 102 housed inside a cover cloth 103 described later. As an example, when viewed from above, the core material 102 is rectangular. The core material 102 has, for example, a content and a bag body for housing the content. The content is, for example, a polyurethane foam or polyester. The material of the bag body is, for example, cotton or polyester.

[0166] The core material 102 has a rectangular parallelepiped shape. The core material 102 has an upper surface 121 for supporting the user's body and a lower surface 122 on the side opposite to the upper surface 121. The upper surface 121 is a surface facing one direction side in the thickness direction D103 ( Figure 8 the upper side in (b)). The lower surface 122 is a surface facing the other direction side in the thickness direction D103 ( Figure 8 the lower side in (b)). The core material 102 has a plurality of side surfaces 123 connecting the upper surface 121 and the lower surface 122.

[0167] As Figure 8 (a) shows, the cushion body 110 has a cover cloth 103. As an example, the cover cloth 103 is bag-shaped and rectangular when viewed from above. The cover cloth 103 has, for example, a fabric 131 covering the upper surface 121 of the core material 102, a lining 132 covering the lower surface 122 of the core material 102, and an opening / closing member 133 connecting the fabric 131 and the lining 132 to each other. The opening / closing member 133 is provided at a position of the cover cloth 103 corresponding to the side surface 123 of the core material 102. When viewed from the thickness direction D103, the opening / closing member 133 is located outside the side surface 123 of the core material 102. The cover cloth 103 can be attached to and detached from the core material 102.

[0168] An example of the opening / closing member 133 is a so-called double slider. That is, the opening / closing member 133 may have two slider members 134 and an opening / closing part 135 that separates or closes the fabric 131 and the lining 132 by the sliding of the slider members 134.

[0169] In this case, by sliding one slider member 134 along the opening / closing part 135 in one direction ( Figure 8 the clockwise direction in (a)), and sliding the other slider member 134 along the opening / closing part 135 in the other direction ( Figure 8 the counterclockwise direction in (a)), the fabric 131 and the lining 132 can be separated.

[0170] By sliding one slider member 134 along the opening / closing portion 135 in one direction and another slider member 134 along the opening / closing portion 135 in the other direction, the fabric 131 and the lining 132 can be closed. The opening / closing member 133 can also be a so-called single slider. In this case, the opening / closing member 133 has one slider member 134 and the opening / closing portion 135.

[0171] The sensor unit 111 is disposed, for example, inside the bag-shaped cover 103. More specifically, the sensor unit 111 is disposed between the core material 102 and the cover 103. When the sensor unit 111 is mounted on the cushion body 110, for example, the sensor unit 111 is disposed to extend in the short side direction D102 of the cushion body 110. In this case, the sensor unit 111 is located on the side opposite to the user's body when viewed from the cover 103 of the cushion body 110. When the cushion body 110 bears the user's body, the sensor unit 111 does not contact the user. The sensor unit 111 acquires the user's respiration information, body movement information, and heartbeat information in a non-contact manner.

[0172] The sensor unit 111 is mounted, for example, at a position corresponding to the user's heart in the long side direction D101. As an example, the sensor unit 111 is mounted at a position ([ Figure 8 from the position where the user's head is borne to a position away along the long side direction D101 of the cushion body 110 (the lower right position in (a))). The mounting position of the sensor unit 111 in the long side direction D101 can be changed. The distance from the position where the user's head is borne to the position where the sensor unit 111 is mounted can be, for example, 40 cm or more and 50 cm or less (an example is 50 cm).

[0173] The autonomic nerve determination application program 120 obtains the user's bedtime and wake-up time from, for example, the respiration information, body movement information, and heartbeat information. The bedtime refers to the time when the user goes to bed. The wake-up time refers to the time when the user wakes up in the next morning or the like. The autonomic nerve determination application program 120 can also determine the bedtime and obtain the bedtime from, for example, the body movement information obtained through the sensor unit 111 and the movement of the bedding detected by the acceleration sensor of the sensor unit 111. The same applies to the acquisition of the wake-up time. Alternatively, different from the above example, the user operates the autonomic nerve determination application program 120 on the information terminal, and the bedtime and wake-up time are obtained through the user's operation.

[0174] The autonomic nerve determination application program 120 includes, for example, an autonomic nerve acquisition unit 112, a sleep state acquisition unit 113, and a time zone determination unit 114. The autonomic nerve acquisition unit 112 acquires autonomic nerve information from the heartbeat information. The autonomic nerve acquisition unit 112 may also calculate the stress level while acquiring the autonomic nerve information. For example, the autonomic nerve acquisition unit 112 calculates the stress level from at least any one of the respiration information, heartbeat information, and body movement information acquired by the sensor unit 111.

[0175] The autonomic nerve information acquired by the autonomic nerve acquisition unit 112 is information indicating the state of the user's autonomic nerves. The autonomic nerve acquisition unit 112 analyzes, for example, the heartbeat information acquired by the sensor unit 111, thereby acquiring the user's heart rate variability (HRV: Heart Rate Variability). The autonomic nerve acquisition unit 112 acquires autonomic nerve information from the user's heart rate variability. For example, the autonomic nerve information includes information indicating the periodic components included in the heart rate variability. Furthermore, the method of acquiring the autonomic nerve information is not limited to the above example and can be appropriately changed.

[0176] The sleep state acquisition unit 113 acquires the sleep state of the user. The sleep state includes the sleep stage indicating the depth of the user's sleep. The sleep state acquisition unit 113 acquires the sleep state from at least one of the respiration information, body movement information, heartbeat information, and brain wave information acquired by the sensor unit 111. As an example, the sleep state acquisition unit 113 acquires the sleep stage as the sleep state from the respiration information, body movement information, and heartbeat information acquired by the sensor unit 111. The sleep state acquisition unit 113 acquires the sleep state, for example, by using the signal indicating the body movement with respiration, the signal indicating the body movement with heartbeat, and the signal indicating the body movement not with respiration and heartbeat output from the sensor unit 111.

[0177] The time zone determination unit 114 determines the detection target time zone from the body movement information acquired by the sensor unit 111. The detection target time zone refers to the time zone during which no body movement of the user occurs from the time of going to bed to the time of waking up. The time zone during which no body movement occurs means, for example, the time zone in which either the supine position or the lateral position is maintained. The time zone during which no body movement occurs may also be the time zone from the time of going to bed to the time of waking up except for the time of turning over. For example, the time zone determination unit 114 determines the detection target time zone from the signal indicating the body movement of the user not with respiration and heartbeat output from the sensor unit 111.

[0178] The autonomic nerve determination application program 120 includes an autonomic nerve determination unit 115, an instrument control unit 116, and a support information acquisition unit 117. The autonomic nerve determination unit 115 determines the user's autonomic nerve from both the autonomic nerve information obtained by the autonomic nerve acquisition unit 112 and the sleep state obtained by the sleep state acquisition unit 113. As an example, the autonomic nerve determination unit 115 determines the user's autonomic nerve in the detection target time zone determined by the time zone determination unit 114. The autonomic nerve determination unit 115 performs frequency analysis on the periodic components of the heart rate variability included in the autonomic nerve information, for example, and determines the autonomic nerve from the power spectrum of each frequency.

[0179] The power spectrum of the autonomic nerve obtained as a result of the above frequency analysis is divided into an LF (Low Frequency) component belonging to the integral value of the power spectrum in the low frequency band (for example, 0.04 Hz to 0.15 Hz) and an HF (High Frequency) component belonging to the integral value of the power spectrum in the high frequency band (for example, 0.15 Hz to 0.4 Hz). The LF component reflects sympathetic nerve activity and parasympathetic nerve activity, and the HF component reflects parasympathetic nerve activity. The autonomic nerve determination unit 115 can use, for example, a sympathetic nerve index as an index indicating the superiority of the sympathetic nerve. An example of the sympathetic nerve index is the value obtained by dividing the value of the LF component by the value of the HF component. In addition, the autonomic nerve determination unit 115 can use, for example, a parasympathetic nerve index as an index indicating the superiority of the parasympathetic nerve. An example of the parasympathetic nerve index is the value obtained by dividing the value of the HF component by the sum of the LF component and the HF component.

[0180] The autonomic nerve determination unit 115 calculates the user's activity level from, for example, the sympathetic nerve index. The activity level is a degree indicating the superiority of the sympathetic nerve relative to the parasympathetic nerve. The autonomic nerve determination unit 115 calculates the user's relaxation level from the parasympathetic nerve index, for example. The relaxation level is a degree indicating the superiority of the parasympathetic nerve relative to the sympathetic nerve. The autonomic nerve determination unit 115 classifies the state of the user's autonomic nerve from the user's activity level and relaxation level, for example. The classification of the state of the autonomic nerve will be described later.

[0181] The autonomic nerve determination unit 115 determines the autonomic nerve based on, for example, the sleep state of the user. For example, during rapid eye movement (REM) sleep, the autonomic nerve is more likely to be disordered compared to non-REM sleep. The so-called "autonomic nerve disorder" means, for example, that the activity of the tonsils related to heart rate variability becomes lively and the constancy of the autonomic nerve cannot be maintained. The autonomic nerve determination unit 115 can, for example, pre-store information indicating the state of the autonomic nerve during REM sleep of a person other than the user. The autonomic nerve determination unit 115 can compare the autonomic nerve information of the user obtained by the autonomic nerve acquisition unit 112 with the pre-stored information indicating the state of the autonomic nerve to determine the autonomic nerve of the user.

[0182] The instrument control unit 116 controls the operation of the instrument 202 that constitutes the environment around the user. The instrument control unit 116 uses the determination result of the autonomic nerve determination unit 115 to control the operation of the instrument 202. The instrument 202 is, for example, an air conditioner, a lighting fixture, a music player, an essential oil diffuser, an electric blanket or a heat therapy device covering the user, which are installed in the user's bedroom. The instrument control unit 116 can communicate with the instrument 202. In the case where the instrument 202 is an air conditioner, for example, the instrument control unit 116 can control the temperature of the space where the user is located by controlling the operation of the instrument 202.

[0183] The support information acquisition unit 117 acquires support information from the server 201 using the determination result of the autonomic nerve determination unit 115. Furthermore, the support information acquisition unit 117 may not acquire support information from the server 201, but acquire the support information included in the data from the data stored in the information terminal device by downloading the autonomic nerve determination application program 120. The support information refers to information that supports the user's life. The support information includes, for example, information indicating the content of the food that the user is expected to ingest after waking up. For example, the support information acquisition unit 117 determines at least one of the ease of falling asleep, the drowsiness when waking up, the degree of fatigue recovery, and the biorhythm from the determination result of the autonomic nerve determination unit 115 to acquire the support information. The support information acquisition unit 117 acquires the support information using, for example, the table data stored in the database stored in the server 201 and the determination result of the autonomic nerve determination unit 115. Specific examples of the support information and specific methods for acquiring the support information will be described in detail later.

[0184] The autonomic nerve determination system 101 includes a display unit 118. The display unit 118 causes the information generated by the autonomic nerve determination application program 120 to be displayed on the display of the information terminal device. The display unit 118 can cause the information to be displayed on the display of, for example, a computer, a tablet terminal device, a smartphone, or a wearable terminal device held by the user, which is an example of the information terminal device.

[0185] The display unit 118 displays the support information acquired by the support information acquisition unit 117. The display unit 118 causes the support information to be displayed on the display of the information terminal. The display unit 118 can display, as support information, a comment representing a proposal corresponding to the state of the user's autonomic nerve during sleep, based on, for example, the relaxation level at the time of falling asleep and the activity level at the time of waking up.

[0186] As an example, in addition to displaying the support information, the display unit 118 also displays the determination result of the autonomic nerve determination unit 115. The display unit 118 displays information representing an evaluation of the state of the user's autonomic nerve based on, for example, the relaxation level at the time of falling asleep and the activity level at the time of waking up. The display unit 118 can also display information representing the balance of the autonomic nerve at the time of falling asleep and waking up based on, for example, the autonomic nerve information and the sleep state. Specific examples of the aforementioned comment representing a proposal, information representing an evaluation, and information representing the balance of the autonomic nerve will be described in detail later.

[0187] The communication between the sensor unit 111 and the autonomic nerve determination application 120, the communication between the autonomic nerve determination application 120 and the server 201, and the communication between the instrument control unit 116 and the instrument 202 can be achieved through a wireless communication interface such as, for example, a wireless LAN (Local Area Network) or Bluetooth (registered trademark). In addition, these communications can also be achieved in a wired manner.

[0188] An example of the operation of the autonomic nerve determination system 101 will be described. Figure 9 It is a flowchart showing an example of the operation of the autonomic nerve determination system 101. Before operating the autonomic nerve determination system 101, the sensor unit 111 is first installed on the mat 110. The sensor unit 111 is installed on the mat 110 in such a way that the sensor unit 111 does not come into contact with the user when the user's body is placed on the mat 110. For example, the sensor unit 111 is disposed between the core material 102 and the cover 103. Next, the user's body lies horizontally on the mat 110 on which the sensor unit 111 is installed. For example, the user's body is placed on the mat 110 in contact with the fabric 131 of the cover 103.

[0189] The sensor unit 111 acquires respiration information, body movement information, and heartbeat information from the user's body movements following respiration, body movements following heartbeat, and body movements not following respiration and heartbeat (step S101). In step S101, the sensor unit 111 detects the user's body movements following respiration, body movements following heartbeat, and body movements not following respiration and heartbeat. The sensor unit 111 outputs a signal representing the body movement following respiration, a signal representing the body movement following heartbeat, and a signal representing the body movement not following respiration and heartbeat to the outside of the sensor unit 111.

[0190] Next, the autonomic nerve acquisition unit 112 acquires autonomic nerve information from the heartbeat information (step S102). In step S102, for example, the autonomic nerve acquisition unit 112 analyzes the heartbeat information acquired by the sensor unit 111 to thereby acquire the user's heart rate variability. In step S102, the autonomic nerve acquisition unit 112 acquires autonomic nerve information from the user's heart rate variability. The autonomic nerve information includes, for example, information representing the frequency of the R wave of the heartbeat.

[0191] The sleep state acquisition unit 113 acquires the user's sleep state from the respiration information, body movement information, and heartbeat information acquired by the sensor unit 111 (step S103). The sleep state acquisition unit 113 acquires the sleep state from, for example, the signal output from the sensor unit 111. In step S103, the sleep state acquisition unit 113 acquires the user's sleep stage from, for example, the respiration information, body movement information, and heartbeat information.

[0192] The time zone determination unit 114 determines the detection target time zone from the body movement information acquired by the sensor unit 111 (step S104). In step S104, the time zone determination unit 114 determines the detection target time zone from the signal representing the user's body movement not following respiration and heartbeat acquired by the sensor unit 111. As a specific example, the time zone determination unit 114 determines the time zone in which the supine position continues for a certain period of time (one example is 1 minute) or more and the time zone in which the lateral position continues for a certain period of time (one example is 1 minute) or more as the detection target time zone.

[0193] The autonomic nerve determination unit 115 determines the user's autonomic nerve from both the autonomic nerve information obtained by the autonomic nerve acquisition unit 112 and the sleep state obtained by the sleep state acquisition unit 113 (step S105). In step S105, the autonomic nerve determination unit 115 determines the user's autonomic nerve in the detected object time zone determined by the time zone determination unit 114. The autonomic nerve determination unit 115 performs frequency analysis on the heart rate variability included in the autonomic nerve information, for example, and determines the autonomic nerve from the power spectrum of each frequency. The autonomic nerve determination unit 115 calculates, for example, a sympathetic nerve index and a parasympathetic nerve index obtained by frequency analysis of the heart rate variability.

[0194] In step S105, the autonomic nerve determination unit 115 determines the autonomic nerve based on the sleep state obtained by the sleep state acquisition unit 113, for example. The autonomic nerve determination unit 115 can determine the user's autonomic nerve by comparing, for example, the user's autonomic nerve information obtained by the autonomic nerve acquisition unit 112 with the information indicating the state of the autonomic nerve pre - memorized. The determination of the autonomic nerve by the autonomic nerve determination unit 115 is performed in the following order, for example.

[0195] The autonomic nerve determination unit 115 calculates the user's activity level from the sympathetic nerve index. The autonomic nerve determination unit 115 calculates the user's relaxation level from the parasympathetic nerve index. The autonomic nerve determination unit 115 classifies the state of the user's autonomic nerve into multiple (an example is four) zones (such as the ideal zone, the fatigue zone, the stress zone, and the internal rhythm disorder zone) based on the relaxation level at the time of falling asleep and the activity level at the time of waking up.

[0196] For example, the autonomic nerve determination unit 115 performs the above - mentioned classification based on the level of relaxation and the level of activity. A high level of relaxation means that, for example, the parasympathetic nerve index is above a predetermined threshold. A low level of relaxation means that, for example, the parasympathetic nerve index is less than the predetermined threshold. A high level of activity means that, for example, the sympathetic nerve index is above a predetermined threshold. A low level of activity means that, for example, the sympathetic nerve index is less than the predetermined threshold. This predetermined threshold can be changed appropriately.

[0197] When the relaxation level at the time of falling asleep is high and the activity level at the time of waking up is high, the autonomic nerve determination unit 115 determines that the state of the user's autonomic nerve is in the ideal zone. When the relaxation level at the time of falling asleep is low and the activity level at the time of waking up is high, the autonomic nerve determination unit 115 determines that the state of the user's autonomic nerve is in the stress zone. When the relaxation level at the time of falling asleep is high and the activity level at the time of waking up is low, the autonomic nerve determination unit 115 determines that the state of the user's autonomic nerve is in the fatigue zone. When the relaxation level at the time of falling asleep is low and the activity level at the time of waking up is low, the autonomic nerve determination unit 115 determines that the state of the user's autonomic nerve is in the internal rhythm disorder zone (autonomic nerve disorder zone).

[0198] The instrument control unit 116 controls the operation of the instrument 202 using the determination result of the autonomic nerve determination unit 115 (step S106). In step S106, for example, the instrument control unit 116 controls the operation of the air conditioner installed in the user's bedroom. As an example, the instrument control unit 116 can control the operation of the air conditioner to increase (or decrease) the temperature of the bedroom according to the determination result of the autonomic nerve made by the autonomic nerve determination unit 115.

[0199] In step S106, for example, the instrument control unit 116 can adjust the brightness of the bedroom. In this case, the instrument 202 is, for example, a lighting fixture such as a light bulb. When the parasympathetic nerve is in a superior position to the sympathetic nerve even at the scheduled time for the user to wake up, the instrument control unit 116 can control the lighting fixture to make the bedroom brighter than usual. As a result of the bedroom becoming brighter, an effect that makes it easier for the user to wake up is generated, and it can be expected that the sympathetic nerve will be in a superior position to the parasympathetic nerve.

[0200] In step S106, for example, the instrument control unit 116 can also control the instrument 202 to adjust the temperature of the user's bed. In this case, the instrument 202 is, for example, an electric blanket covering the user. As described above, the autonomic nerve during rapid eye movement sleep is more likely to be disordered than the autonomic nerve during non-rapid eye movement sleep. When the sympathetic nerve is in a superior position to the parasympathetic nerve even when the user's sleep state is non-rapid eye movement sleep, there is a possibility that the user will be under stress. In this case, the instrument control unit 116 can control the heater to raise or lower the temperature of the bed.

[0201] The support information acquisition unit 117 acquires support information using the determination result of the autonomic nerve determination unit 115 (step S107). For example, the support information acquisition unit 117 acquires support information from the server 201. The support information acquisition unit 117 acquires support information using, for example, the tabular data stored in the database stored in the server 201 and the determination result of the autonomic nerve determination unit 115.

[0202] Refer to Figure 10 to illustrate a specific example of the operation of step S107. Figure 10 is a table showing an example of the table stored in the support database DB101. The support database DB101 is stored in, for example, the server 201. However, the support database DB101 can also be a part of the data held by the autonomic nerve determination application program 120, or can be downloaded to the information terminal. A plurality of support information acquired by the support information acquisition unit 117 is stored in the support database DB101 in advance.

[0203] As described above, the support information acquisition unit 117 determines at least one of the ease of falling asleep, the drowsiness upon waking up, the degree of fatigue recovery, and the biological rhythm, for example, from the determination result of the autonomic nerve determination unit 115. As an example, in the table of the support database DB101, the content of the determination executed by the support information acquisition unit 117 is recorded in the left column. The object based on the determination of the support information acquisition unit 117 is recorded in the central column of the table of the support database DB101. The content of the support information acquired by the support information acquisition unit 117 is recorded in the right column of the table of the support database DB101. In step S107, the support information acquisition unit 117 refers to the support database DB101. For example, the support information acquisition unit 117 refers to the support database DB101 by communicating with the server 201. At this time, the support information acquisition unit 117 acquires support information from the table of the support database DB101.

[0204] In step S107, the support information acquisition unit 117 determines, for example, the ease of falling asleep. The support information acquisition unit 117 determines the ease of falling asleep, for example, from the degree of stress acquired by the autonomic nerve acquisition unit 112. As an example, the support information acquisition unit 117 determines the ease of falling asleep of the user from the ratio of the degree of stress at the time of the user's past falling asleep to a predetermined degree of stress. The predetermined degree of stress can be appropriately changed by the user, for example, or can be preset by the user. The degree of stress at the time of past falling asleep can be, for example, the average value of a plurality of degrees of stress calculated in the past. The so-called past refers to a time point earlier than the time point when the autonomic nerve of the user is determined using the autonomic nerve determination system 101. The support information acquisition unit 117 calculates the ratio of the degree of stress at the time of the user's past falling asleep to the predetermined degree of stress from the information indicating the balance of the autonomic nerve at the time of falling asleep acquired by the autonomic nerve determination unit 115 in the past, for example.

[0205] For example, when the sympathetic nerve of the user was in a dominant position over the parasympathetic nerve at the time of past falling asleep, the support information acquisition unit 117 determines that the ratio of the degree of stress of the user is high and the user is in a state of difficulty in falling asleep. At this time, the support information acquisition unit 117 acquires an opinion urging a relaxation-effective exercise as support information. As an example, the support information acquisition unit 117 acquires from the support database DB101 an opinion stating that "it is better to do stretching exercises before going to bed".

[0206] In step S107, the support information acquisition unit 117 determines, for example, the drowsiness upon waking up. The support information acquisition unit 117 determines the drowsiness upon waking up from the information indicating the balance of the autonomic nerves upon waking up obtained by the autonomic nerve determination unit 115. When the parasympathetic nerve is in a superior position to the sympathetic nerve upon waking up, the support information acquisition unit 117 determines that the drowsiness upon waking up is great. At this time, the support information acquisition unit 117 acquires the information of the breakfast menu corresponding to the drowsiness upon waking up as support information. As an example, the support information acquisition unit 117 acquires from the support database DB101 the opinion stating that "it is better to ingest caffeine at breakfast".

[0207] The support information acquisition unit 117 calculates, for example, the degree of fatigue recovery and determines the calculated degree of fatigue recovery. The support information acquisition unit 117 determines the degree of fatigue recovery from the difference between the total power at the time of falling asleep and the total power at the time of waking up. The total power refers to the integrated value of the power spectrum in a predetermined frequency band (an example is 0.04 Hz to 0.4 Hz) in the case of performing HRV frequency analysis based on the electrocardiogram of the user obtained by the sensor unit 111.

[0208] In the case of a high degree of fatigue, the total power tends to be low, and in the case of a low degree of fatigue, the total power tends to be high. By calculating the difference between the total power at the time of falling asleep and the total power at the time of waking up, it is possible to calculate to what extent the fatigue can be recovered through sleep as the degree of fatigue recovery. For example, the support information acquisition unit 117 calculates the calculated degree of fatigue recovery numerically and determines whether the calculated degree of fatigue recovery is above the threshold. As a result of the support information acquisition unit 117 determining the degree of fatigue recovery, it acquires, for example, the information indicating the time zone when it is difficult to be energetic during the day and the information indicating the necessity of taking a nap during the day as support information.

[0209] In step S107, the support information acquisition unit 117 calculates the biological rhythm from, for example, the respiration information, body movement information, and heartbeat information obtained by the sensor unit 111. The biological rhythm indicates, for example, the degree of drowsiness corresponding to the time zone. The support information acquisition unit 117 determines the time zone in which it is easy to be efficient (performance) from the time series change of the autonomic nerves. At this time, the support information acquisition unit 117 acquires the information indicating the time zone in which it is easy to be efficient during the day from the calculated biological rhythm as support information. As an example, the support information acquisition unit 117 acquires from the support database DB101 the opinion stating that "higher efficiency can be achieved in the morning than in the afternoon".

[0210] Next, the display unit 118 displays the support information acquired by the support information acquisition unit 117 (step S108). As described above, the display unit 118 displays at least any one of, for example, an opinion urging an exercise with a relaxing effect, information indicating a breakfast menu corresponding to the drowsiness upon waking up, information indicating a time zone when it is difficult to be energetic during the day, information indicating the necessity of taking a short nap during the day, and information indicating a time zone when it is easy to be efficient during the day. Moreover, the display unit 118 displays the determination result of the autonomic nerve determination unit 115 (step S108).

[0211] Refer to Figures 11 to 13 to illustrate a specific example of the operation in step S108. Figure 11 is a diagram showing an example of the display of the determination result of the autonomic nerve at the time of falling asleep and an example of the display of the determination result of the autonomic nerve upon waking up. Figure 11 In the example of, the display unit 118 displays the sympathetic nerve region R101, the parasympathetic nerve region R102, and the antagonistic region R103.

[0212] As an example, the display unit 118 represents the determination result of the autonomic nerve by causing the point P101 to be displayed in a bar-shaped graph arranged in the order of the sympathetic nerve region R101, the antagonistic region R103, and the parasympathetic nerve region R102. As a specific example, this graph extends horizontally, the sympathetic nerve region R101 is located on the left side of the antagonistic region R103, and the parasympathetic nerve region R102 is located on the right side of the antagonistic region R103. The antagonistic region R103 is located between the sympathetic nerve region R101 and the parasympathetic nerve region R102. In step S108, the display unit 118 causes the point P101 to be displayed on the sympathetic nerve region R101, the parasympathetic nerve region R102, or the antagonistic region R103 according to the determination result of the autonomic nerve at the time of falling asleep and upon waking up.

[0213] In step S108, the display unit 118 displays, for example, information indicating the balance of the autonomic nerve at the time of falling asleep and upon waking up acquired by the autonomic nerve determination unit 115. The case where the point P101 is displayed on the sympathetic nerve region R101 indicates that the sympathetic nerve is in a superior position compared to the parasympathetic nerve. The case where the point P101 is displayed on the parasympathetic nerve region R102 indicates that the parasympathetic nerve is in a superior position compared to the sympathetic nerve. The case where the point P101 is displayed on the antagonistic region R103 indicates that the sympathetic nerve and the parasympathetic nerve are in a state of mutual antagonism.

[0214] At the time of falling asleep, it is preferable that the parasympathetic nerve is in a superior and relaxed state compared to the sympathetic nerve. In this case, in the determination result of the autonomic nerve at the time of falling asleep, the point P101 will be located in the parasympathetic nerve region R102( Figure 11In the ideal region). When waking up, it is preferable that the sympathetic nerve is in a superior and active state compared to the parasympathetic nerve. In this case, in the determination result of the autonomic nerve at the time of waking up, the point P101 will be located in the sympathetic nerve region R101 or the antagonistic region R103( Figure 11 In the ideal region). Furthermore, Figure 11 shows an example where the parasympathetic nerve region R102 is the ideal region during sleep, and the sympathetic nerve region R101 and the antagonistic region R103 are the ideal regions when waking up. However, it can also be the case that the parasympathetic nerve region R102 and the antagonistic region R103 are the ideal regions during sleep, and only the sympathetic nerve region R101 is the ideal region when waking up.

[0215] Figure 12 is a diagram showing another example of the display of the determination result of the autonomic nerve. Figure 12 The vertical axis shown represents the degree of relaxation during sleep, and the horizontal axis represents the degree of activity when waking up. Figure 12 In the example of, the autonomic nerve determination unit 115 classifies the state of the user's autonomic nerve into any one of an ideal region, a fatigue region, a stress region, and a body rhythm disorder region based on the degree of relaxation during sleep and the degree of activity when waking up.

[0216] In step S108, the display unit 118 displays information indicating, for example, an evaluation of the state of the user's autonomic nerve during sleep. The display unit 118 causes the point P102 to be displayed on Figure 12 the chart shown. For example, when the autonomic nerve determination unit 115 determines that the degree of relaxation during sleep is high and the degree of activity when waking up is high, the display unit 118 causes the point P102 to be displayed at a position in the ideal region of the chart. For example, when the autonomic nerve determination unit 115 determines that the degree of relaxation during sleep is low and the degree of activity when waking up is high, the display unit 118 causes the point P102 to be displayed at a position in the stress region of the chart.

[0217] For example, when the autonomic nerve determination unit 115 determines that the degree of relaxation during sleep is high and the degree of activity when waking up is low, the display unit 118 causes the point P102 to be displayed at a position in the fatigue region of the chart. For example, when the autonomic nerve determination unit 115 determines that the degree of relaxation during sleep is low and the degree of activity when waking up is low, the display unit 118 causes the point P102 to be displayed at a position in the body rhythm disorder region of the chart.

[0218] Figure 13An example of a table representing the autonomic nerve database DB102. The autonomic nerve database DB102 is stored in, for example, the server 201. However, the autonomic nerve database DB102 can also be a part of the data held by the autonomic nerve determination application program 120, or can be downloaded to the information terminal. In step S108, the display unit 118 refers to the autonomic nerve database DB102. A plurality of display contents to be displayed by the display unit 118 are pre-stored in the autonomic nerve database DB102.

[0219] In step S108, the display unit 118 displays, for example, information suggesting the state of the user's autonomic nerves during sleep. The display unit 118 obtains information indicating which area the state of the user's autonomic nerves during sleep is located in based on the relaxation level at the time of falling asleep and the activity level at the time of waking up. Then, the display unit 118 refers to the autonomic nerve database DB102 and displays the content corresponding to the obtained area.

[0220] When the state of the user's autonomic nerves during sleep is in the ideal area, the display unit 118 displays information to the effect that the parasympathetic nerve is in a superior position to the sympathetic nerve at the time of falling asleep, and the sympathetic nerve is in a superior position to the parasympathetic nerve at the time of waking up. As a specific example, the display unit 118 displays "The state of the autonomic nerves is suitable for both falling asleep and waking up. In this state, it is easy to fall asleep smoothly and wake up refreshed."

[0221] When the state of the user's autonomic nerves during sleep is in the fatigue area, the display unit 118 displays information to the effect that the parasympathetic nerve is in a superior position to the sympathetic nerve both at the time of falling asleep and at the time of waking up. As a specific example, the display unit 118 displays "The body is in a state suitable for sleep, but this state continues until waking up. In this state, there may still be drowsiness when waking up, and it may not be possible to wake up refreshed. Actively bathe in the morning sunlight to make the body active."

[0222] When the state of the user's autonomic nerves during sleep is in the stress area, the display unit 118 displays information to the effect that the sympathetic nerve is in a superior position to the parasympathetic nerve both at the time of falling asleep and at the time of waking up. As a specific example, the display unit 118 displays "The body is in a suitable state when waking up, but it is not in a suitable state for sleep when falling asleep. If this state continues, the body cannot rest and fatigue will continue to accumulate. Use relaxation programs at the time of falling asleep to make the parasympathetic nerve in a superior position."

[0223] When the state of the autonomic nerve of the user during sleep is in the internal rhythm disorder area, the display unit 118 displays information to the effect that the sympathetic nerve is in a superior position to the parasympathetic nerve during falling asleep, and the parasympathetic nerve is in a superior position to the sympathetic nerve during waking up. As a specific example, the display unit 118 displays "The state of the body is not suitable both during falling asleep and waking up. Since the balance of the autonomic nerve and the internal rhythm are both disordered, pay more attention to a regular life and make the parasympathetic nerve in a superior position during falling asleep."

[0224] As described above, an example of the steps of the autonomic nerve determination system has been described. However, the content and order of the steps of the autonomic nerve determination system are not limited to the foregoing examples and can be changed appropriately.

[0225] Next, the operation and effect of the autonomic nerve determination system 101 as an example will be described. The autonomic nerve determination system 101 obtains the sleep state of the user from the respiration information, body movement information, and heartbeat information. The autonomic nerve determination system 101 determines the autonomic nerve of the user from both the autonomic nerve information obtained by the autonomic nerve acquisition unit 112 and the sleep state obtained by the sleep state acquisition unit 113. For example, during falling asleep, it is desirable that the parasympathetic nerve is in a superior position to the sympathetic nerve, and during waking up, it is desirable that the sympathetic nerve is in a superior position to the parasympathetic nerve. The autonomic nerve determination system 101 can add the sleep state indicating whether the user's sleep is deep or shallow or whether the user can sleep soundly to determine the autonomic nerve.

[0226] For example, during rapid eye movement sleep, there is a tendency for the autonomic nerve to be more easily disordered than during non-rapid eye movement sleep. Since it is possible to determine whether the user is in rapid eye movement sleep by obtaining the sleep state of the user, the autonomic nerve can be evaluated after adding the state of whether the user is in rapid eye movement sleep. Therefore, the autonomic nerve can be determined corresponding to the sleep state of the user.

[0227] For example, the autonomic nerve of the user during waking up tends to be significantly different between the case of being under high stress and the case of not being under much stress. By obtaining the sleep state of the user, it is possible to determine whether the user is waking up, so the autonomic nerve can be evaluated after considering that the user is in an awakened state. Thus, the autonomic nerve can be determined corresponding to the sleep state of the user.

[0228] As an example, the autonomic nerve determination system 101 includes a time zone determination unit 114 that determines a detection target time zone during which the user's body movement does not occur from the body movement information from the time of going to bed to the time of waking up. The autonomic nerve determination unit 115 determines the user's autonomic nerve during the detection target time zone. For example, unlike when awake, the body usually turns over during sleep, so it is impossible to maintain a state of not moving the body. When determining the autonomic nerve in the time zone where the user's body movement occurs, the accuracy of the obtained autonomic nerve information may be reduced due to the body movement, so the determination accuracy of the autonomic nerve may be reduced. Therefore, by determining the autonomic nerve in the detection target time zone where the user's body movement does not occur, a reduction in the determination accuracy can be suppressed.

[0229] As an example, the autonomic nerve determination system 101 includes an instrument control unit 116 that controls the operation of an instrument 202 that constitutes the environment around the user using the determination result of the autonomic nerve determination unit 115. In this case, the operation of an instrument such as an air conditioner arranged around the user can be controlled corresponding to the state of the user's autonomic nerve. Therefore, the sleep environment of the user can be made more comfortable corresponding to the determined state of the autonomic nerve.

[0230] As an example, the autonomic nerve determination system 101 includes a support information acquisition unit 117 that acquires information that supports the user's life, that is, support information, using the determination result of the autonomic nerve determination unit 115, and a display unit 118 that displays the support information. In this case, by displaying the support information corresponding to the determined state of the user's autonomic nerve, the user's life can be supported. For example, a proposal can be made regarding the content of the diet corresponding to the determined state of the autonomic nerve. Therefore, the user's life can be made more comfortable using the determination result of the user's autonomic nerve.

[0231] Next, a specific example of the life improvement system will be described. The life improvement system can improve the user's life. The life improvement system can be used, for example, for personal or household use, or can also be used in research institutions, etc. for experimental research. The life improvement system can also be used for treatment in hospitals, etc. The user refers to a person who is the object to improve their life through the life improvement system. The user is, for example: a person with a health risk caused by life disorders, etc., a person receiving treatment in a hospital, etc., or a person who hopes to improve their own life.

[0232] Figure 14 It is a block diagram showing the life improvement system 301 as an example. The life improvement system 301 includes a sensor unit 311, an information terminal 401, and a life improvement server 402.

[0233] The information terminal 401 is, for example, a portable terminal. The portable terminal is, for example, a portable information terminal such as a portable phone including a smart phone, a tablet, a notebook personal computer, a wearable terminal such as a watch, etc. The information terminal 401 may also be a terminal other than a portable terminal, and may be, for example, a desktop computer. An example of the information terminal 401 includes: a processor (such as a CPU) that executes an operating system (OS) and software (application programs), a main memory unit composed of a ROM and a RAM, an auxiliary memory unit composed of a flash memory, etc., a communication control unit composed of a wireless communication module, etc., an input device, and an output device such as a display. However, the configuration of the information terminal 401 is not limited to the above description and can be appropriately changed.

[0234] The application program executed by the information terminal 401 is the life improvement application program 340. The life improvement application program 340 may be an application program downloaded to the information terminal 401 and executed on the information terminal 401, or may be executed on the life improvement server 402. The life improvement application program 340 can be downloaded from the life improvement server 402. Hereinafter, an example will be described in which the life improvement application program 340 is an application program downloaded to the information terminal 401 and the functions of the life improvement application program 340 are executed on the information terminal 401.

[0235] Each function of the life improvement application program 340 is realized by causing the processor or the main memory unit to read a predetermined software and execute the software. The data or database required for the execution of the functions of the life improvement application program 340 is stored in the main memory unit or the auxiliary memory unit.

[0236] Each functional element of the information terminal 401 is realized by causing the processor or the memory unit (such as the aforementioned main memory unit or auxiliary memory unit) to read a predetermined software and execute the software. The data or database used in the processing of the life improvement server 402 is stored in the memory unit.

[0237] The life improvement application program 340 may be a distributed processing system composed of multiple computers, or may be a client-server system or a cloud system. The life improvement application program 340 includes, for example, a main module, a data acquisition module, a determination module, and an output module. By executing the data acquisition module, the determination module, and the output module, the functions of each functional element of the life improvement application program 340 are exerted. As an example, the life improvement application program 340 may be provided after being fixedly recorded on a tangible memory medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. The life improvement application program 340 may also be provided as a data signal superimposed on a carrier wave via a communication network. The functional configuration of the life improvement application program 340 will be described later.

[0238] The information terminal 401 is a terminal that inputs information by accepting the operations of the user. The information terminal 401 can send the input information to the life improvement server 402. As an example, the information terminal 401 sends information representing the subjective evaluation of the user, that is, subjective information, to the life improvement server 402.

[0239] Figure 15 FIG. is an example of an input screen 380 that represents the subjective information displayed on the information terminal 401. The input screen 380 is a screen on which subjective information can be input. The subjective information includes, for example, a subjective evaluation of sleep. The input screen 380 includes a sleep evaluation input section 381, which is a part for inputting a subjective sleep evaluation. In the sleep evaluation input section 381, for example, a score of a subjective sleep evaluation with a full score of 100 can be input.

[0240] The information terminal 401 displays prediction information and improvement information. The detailed content of the prediction information and the improvement information will be described later.

[0241] The information terminal 401 can access a predetermined website 403. As an example, the website 403 includes at least one of a shopping website that sells goods including at least one of clothes, food, soap, and cosmetics; and a travel website that promotes travel and accepts reservations for accommodation facilities. The website 403 sends information to the information terminal 401 according to a request from the information terminal 401. The information sent by the website 403 includes, for example, a URL (Uniform Resource Locator). The information sent from the website 403 to the information terminal 401 is displayed on the information terminal 401 and provided to the user. The website 403 can also be accessed from the life improvement server 402.

[0242] The sensor unit 311 can obtain information about the user's body. The sensor unit 311 can obtain at least one of respiratory information, body movement information, and heartbeat information. As an example, the sensor unit 311 obtains respiratory information, body movement information, and heartbeat information. The content of the information obtained by the sensor unit 311 can be appropriately changed. The sensor unit 311 can further obtain at least any one of information representing the user's body temperature, information representing blood pressure, and information representing blood glucose level. The sensor unit 311 includes a sensor sheet 312 mounted on the mattress 310a and a cushion sensor 313 mounted on the cushion 310b.

[0243] Figure 16 FIG. (a) is a perspective view of the mattress 310a on which the sensor sheet 312 can be mounted. Figure 16 FIG. (b) shows the composition Figure 16A perspective view of the core material 302 of the mattress 310a shown in (a). The sensor sheet 312 can be attached to and detached from the mattress 310a, for example. The sensor sheet 312 acquires vital sign data of a user lying horizontally on the mattress 310a. As an example, the sensor sheet 312 can acquire an electrocardiogram. The sensor sheet 312 acquires the breathing information, body movement information, and heartbeat information of the user during sleep.

[0244] For example, the sensor sheet 312 has a sheet-like fabric embroidered with a linear sensor, and the position of the sheet-like fabric relative to the mattress 310a can be changed. The linear sensor of the sensor sheet 312 is fixed by embroidery in a two-dimensionally extended manner on the sheet-like fabric, for example. For example, the piezoelectric sensor of the sensor sheet 312 generates an electrical signal corresponding to the load applied by the user's body, and the sensor sheet 312 acquires this electrical signal as the aforementioned breathing information, body movement information, and heartbeat information.

[0245] The sensor sheet 312 has, for example: the aforementioned linear sensor (an example is a piezoelectric sensor); and a communication unit that outputs the breathing information, body movement information, and heartbeat information, which are electrical signals generated by the linear sensor, to the outside of the sensor sheet 312. Although the above content is described by taking the sensor sheet 312 having a piezoelectric sensor as an example, the type of sensor of the sensor sheet 312 is not limited to a piezoelectric sensor and is not particularly limited. For example, the sensor sheet 312 may also include an acceleration sensor.

[0246] The sensor sheet 312 detects the body movement of the user with breathing. The sensor sheet 312 outputs the signal representing the body movement with breathing to the outside of the sensor sheet 312. The sensor sheet 312 detects the body movement of the user with heartbeat. The sensor sheet 312 outputs the signal representing the body movement with heartbeat to the outside of the sensor sheet 312. The sensor sheet 312 detects the body movement of the user that is not with breathing and heartbeat. The sensor sheet 312 outputs the signal representing the body movement that is not with breathing and heartbeat to the outside of the sensor sheet 312.

[0247] For example, the sensor sheet 312 is installed on the mattress 310a that bears the user's body for use. The mattress 310a is rectangular when viewed from above. The mattress 310a extends in the long side direction D301 and the short side direction D302 orthogonal to the long side direction D301. The mattress 310a has a thickness in the thickness direction D303 orthogonal to both the long side direction D301 and the short side direction D302.

[0248] The length of the mattress 310a in the long side direction D301 is, for example, 120 cm or more and 210 cm or less (one example being 195 cm). The length of the mattress 310a in the short side direction D302 is, for example, 70 cm or more and 180 cm or less (one example being 97 cm). The length of the mattress 310a in the thickness direction D303 is, for example, 3 cm or more and 40 cm or less (one example being 9 cm).

[0249] As an example, a user of the mattress 310a places his or her body on the mattress 310a. At this time, the direction in which the body of the lying user extends (that is, the direction connecting the user's head and feet) is, for example, the same as the long side direction D301 of the mattress 310a.

[0250] As shown in Figure 16 (b) of, the mattress 310a has a core material 302 housed inside a cover 303 described later. As an example, the core material 302 is rectangular when viewed from above. The core material 302 has, for example, a content and a bag body for housing the content. The content is, for example, a polyurethane foam or polyester.

[0251] The core material 302 has a rectangular parallelepiped shape. The core material 302 has an upper surface 321 for supporting the user's body and a lower surface 322 facing the opposite side of the upper surface 321. The upper surface 321 is a surface facing one direction side in the thickness direction D303 ( Figure 16 the upper side in (b) of). The lower surface 322 is a surface facing the other direction side in the thickness direction D303 ( Figure 16 the lower side in (b) of). The core material 302 has a plurality of side surfaces 323 connecting the upper surface 321 and the lower surface 322.

[0252] As shown in Figure 16 (a) of, the mattress 310a has a cover 303. As an example, the cover 303 is bag-shaped and rectangular when viewed from above. The cover 303 has, for example, a fabric 331 covering the upper surface 321 of the core material 302, a lining 332 covering the lower surface 322 of the core material 302, and an opening / closing member 333 connecting the fabric 331 and the lining 332 to each other. The opening / closing member 333 is provided at a position on the cover 303 corresponding to the side surface 323 of the core material 302. When viewed from the thickness direction D303, the opening / closing member 333 is located outside the side surface 323 of the core material 302. The cover 303 can be attached to and detached from the core material 302.

[0253] An example of the opening / closing member 333 is a so-called double slider. That is, the opening / closing member 333 may have two slider members 334 and an opening / closing portion 335 that separates or closes the fabric 331 and the lining 332 by the sliding of the slider members 334.

[0254] In this case, by sliding one slider member 334 along the opening / closing portion 335 in one direction ( Figure 16 the clockwise direction in (a) thereof), and sliding the other slider member 334 along the opening / closing portion 335 in the other direction ( Figure 16 the counterclockwise direction in (a) thereof), the fabric 331 and the lining 332 can be separated.

[0255] By sliding one slider member 334 along the opening / closing portion 335 in the other direction, and sliding the other slider member 334 along the opening / closing portion 335 in one direction, the fabric 331 and the lining 332 can be closed. The opening / closing member 333 can also be a so-called single slider. In this case, the opening / closing member 333 has one slider member 334 and the opening / closing portion 335.

[0256] The sensor sheet 312 is disposed, for example, inside the bag-shaped cover 303. More specifically, the sensor sheet 312 is disposed between the core material 302 and the cover 303. When the sensor sheet 312 is mounted on the mattress 310a, for example, the sensor sheet 312 is disposed so as to extend in the short side direction D302 of the mattress 310a. In this case, the sensor sheet 312 is located on the side opposite to the user's body when viewed from the cover 303 of the mattress 310a. When the mattress 310a bears the user's body, the sensor sheet 312 does not contact the user. The sensor sheet 312 obtains the user's breathing information, body movement information, and heartbeat information in a non-contact manner.

[0257] The sensor sheet 312 is mounted, for example, at a position corresponding to the user's heart in the long side direction D301. As an example, the sensor sheet 312 is mounted at an arbitrary position from the position bearing the user's head to the position away along the long side direction D301 of the mattress 310a ( Figure 16 the lower right position in (a) thereof). The mounting position of the sensor sheet 312 in the long side direction D301 is changeable. The distance from the position bearing the user's head to the position where the sensor sheet 312 is mounted can be, for example, 40 cm or more and 50 cm or less (one example is 50 cm).

[0258] Figure 17 is a perspective view showing the core material 314 of the pad 310b on which the pad sensor 313 can be mounted. The pad 310b is formed, for example, by housing the core material 314 in a bag-shaped cover. The core material 314 has a seating portion 315 extending in the horizontal direction and a waist support portion 316 extending upward from the seating portion 315. The core material 314 is made of a soft material such as a polyurethane foam.

[0259] The seating portion 315 extends in a first direction A301 and a second direction A302 that intersects the first direction A301. The first direction A301 is the front-rear direction when viewed from a user seated on the seating portion 315, and the second direction A302 is the left-right direction when viewed from a user seated on the seating portion 315. The seating portion 315 has a thickness in a third direction A303 that intersects both the first direction A301 and the second direction A302. For example, the third direction A303 is the vertical direction. The seating portion 315 has a hip support portion 315a and two thigh support portions 315b. The hip support portion 315a and the thigh support portions 315b are arranged along the first direction A301.

[0260] Hereinafter, the front direction when viewed from a user seated on the seating portion 315 may sometimes be referred to as the front, the front side, or the frontward direction, and the opposite direction of the front direction may be referred to as the rear, the rear side, or the rearward direction. However, the above directions are used for the convenience of explanation and do not limit the positions or directions of the respective parts.

[0261] The hip support portion 315a is located at the rear side of the seating portion 315. The user's hip is borne on the hip support portion 315a. The hip support portion 315a supports the user's hip. The two thigh support portions 315b are arranged along the second direction A302 at the front side of the seating portion 315. The inner side of the user's thigh is borne on the thigh support portions 315b. The thigh support portions 315b support the inner side of the user's thigh.

[0262] The lumbar support portion 316 extends upward from the end (rear end) of the seating portion 315 in the first direction A301. The lumbar support portion 316 has, for example, a general portion 316a that extends along both the second direction A302 and the third direction A303, and a sacrum support portion 316b that is located at the center of the second direction A302 of the general portion 316a. The general portion 316a represents the portion of the lumbar support portion 316 other than the sacrum support portion 316b.

[0263] The sacrum support portion 316b is convex and protrudes forward. The sacrum support portion 316b protrudes forward from the general portion 316a. As an example, the shape of the sacrum support portion 316b when viewed from the front is a shape having a major axis and a minor axis. For example, the shape of the sacrum support portion 316b when viewed from the front is an oval (one example is an ellipse).

[0264] The mat sensor 313 acquires the vital sign data of the user sitting on the mat 310b. As an example, the mat sensor 313 acquires the heartbeat information of the user during the day. The mat sensor 313 is, for example, a piezoelectric sensor fixed to the core material 314. In this case, the mat sensor 313 measures the pressure of the user's body on each part of the mat 310b. The type of the mat sensor 313 is not particularly limited. The mat sensor 313 includes, for example, a seating part sensor 313a installed in the seating part 315, a sacral support part sensor 313b installed in the sacral support part 316b, and thigh part support sensors 313c installed in the respective thigh part support parts 315b of the two thigh part support parts.

[0265] The seating part sensor 313a detects that the user is sitting on the seating part 315. The seating part sensor 313a, for example, detects that the ischium of the user has come into contact with the seating part sensor 313a. The sacral support part sensor 313b measures, for example, the load of the user's body in contact with the sacral support part sensor 313b. The thigh part support part 315b measures, for example, the load of the thigh part of the user sitting on the seating part 315. The seating part sensor 313a, the sacral support part sensor 313b, and the thigh part support sensors 313c generate an electrical signal corresponding to the load from the load of the user's body and acquire the electrical signal as the aforementioned heartbeat information.

[0266] An example of the functional configuration of the life improvement application program 340 will be described. As Figure 14 shown, the life improvement application program 340 has a sleep state acquisition unit 341, an autonomic nerve acquisition unit 342, a memory unit 343, an instrument control unit 344, a prediction information generation unit 345, and an improvement information generation unit 346 as its functional configuration.

[0267] The sleep state acquisition unit 341 acquires information indicating the sleep state of the user, that is, sleep state information, from at least one of the respiration information, body movement information, and heartbeat information acquired by the sensor unit 311. The sleep state refers to the state of the user's sleep. The sleep state may include, for example, states of sleep such as sleep time and sleep stage, and biological information of the user other than the states of sleep such as fatigue degree and stress level. As an example, the sleep state information includes at least one of the user's bedtime, wake-up time, proportion of sleep stages, sleep latency (time to fall asleep), sleep efficiency, number of mid-sleep awakenings, and time of mid-sleep awakenings.

[0268] The sleep stage is an index indicating the depth of the user's sleep. The sleep stages are classified, for example, into wakefulness, rapid eye movement (REM) sleep, and non-REM sleep. The non-REM sleep is classified into four sleep stages. Furthermore, the non-REM sleep can also be classified into two or three sleep stages. Hereinafter, the stages of REM sleep or non-REM sleep may sometimes be referred to as sleep. The time when the user's sleep stage changes from wakefulness to sleep between the time of going to bed and the time of waking up is called the sleep onset time. The time when the user's sleep stage finally changes to the wakefulness stage between the time of going to bed and the time of waking up is called the waking time.

[0269] The proportion of sleep stages refers to the proportion of the time in each sleep stage state relative to the time from the time of going to bed to the time of waking up. The sleep latency (time to fall asleep) refers to the length of the time from the time of going to bed to the sleep onset time. The sleep efficiency is an index used to evaluate the sleep quality. The sleep efficiency is, for example, the following value: the time from the sleep onset time to the waking time minus the time of waking up during the period, and then the value obtained by dividing the time thus obtained by the time from the time of going to bed to the sleep onset time. Waking up during the period refers to the user waking up between the sleep onset time and the waking time. Here, the so-called waking up means that the sleep stage changes from the stage of REM sleep or non-REM sleep to the wakefulness stage.

[0270] The sleep state acquisition unit 341 determines the time of going to bed and the time of waking up from, for example, the body movement information obtained through the sensor unit 311 and the movement of the bedding detected by the sensor unit 311 (e.g., an acceleration sensor), and acquires the time of going to bed and the time of waking up. The sleep state acquisition unit 341 acquires the sleep stage by using, for example, the signal indicating the body movement with breathing, the signal indicating the body movement with heartbeat, and the signal indicating the body movement not with breathing and heartbeat output from the sensor unit 311.

[0271] The sleep state acquisition unit 341 acquires the proportion of sleep stages from, for example, the sleep stage, the time of going to bed, and the time of waking up. The sleep state acquisition unit 341 acquires the time from the time of going to bed to the sleep onset time as the sleep latency based on, for example, the time of going to bed and the sleep stage.

[0272] The sleep state acquisition unit 341 acquires the time of waking up during the period and the number of times of waking up during the period from, for example, the time of going to bed, the time of waking up, and the sleep stage. More specifically, the sleep state acquisition unit 341 acquires the sleep onset time from, for example, the time of going to bed and the sleep stage, and acquires the waking time from the time of waking up and the sleep stage. The sleep state acquisition unit 341 acquires the time of waking up during the period and the number of times of waking up during the period from the sleep onset time, the waking time, and the sleep stage.

[0273] The sleep state acquisition unit 341 obtains the sleep efficiency from, for example, the bedtime, wake-up time, and sleep stages. More specifically, the sleep state acquisition unit 341 obtains the sleep efficiency from, for example, the bedtime, wake-up time, sleep onset time, wake-up time, and the time of mid-sleep awakening.

[0274] The autonomic nerve acquisition unit 342 obtains information indicating the state of the user's autonomic nerves, i.e., autonomic nerve information, from the heartbeat information acquired by the sensor unit 311. The autonomic nerves refer to the nerves that control the operation of organs such as the heart or stomach, or involuntary functions such as blood circulation. Here, the term "involuntary" means, for example, that it cannot be as desired or as intended by oneself. Furthermore, the autonomic nerve response is a response that cannot be intentionally controlled by human will and is used as an objective indicator of emotions, feelings, fatigue, stress, etc.

[0275] The autonomic nerve acquisition unit 342 analyzes, for example, the heartbeat information acquired by the sensor unit 311, and thereby obtains the heart rate variability (HRV: Heart Rate Variability) of the user. As an example, the autonomic nerve acquisition unit 342 obtains information indicating the periodic components included in the heart rate variability from the heart rate variability of the user. The autonomic nerve acquisition unit 342 performs frequency analysis on the periodic components of the heart rate variability and obtains information indicating the power spectrum of each frequency.

[0276] The autonomic nerve acquisition unit 342 obtains information indicating the sympathetic nerve index and information indicating the parasympathetic nerve index as autonomic nerve information. The sympathetic nerve index is an index indicating the predominance of the sympathetic nerves. The parasympathetic nerve index is an index indicating the predominance of the parasympathetic nerves.

[0277] The autonomic nerve acquisition unit 342 obtains the mental state of the user from the autonomic nerve information. The autonomic nerve acquisition unit 342 classifies, for example, the mental state of the user into any one of a high-efficiency state, a relaxed state, a stress state, and a depressive state based on the autonomic nerve information.

[0278] The high-efficiency state means, for example, a state in which the sympathetic nerve index is above a predetermined first threshold and the parasympathetic nerve index is above a predetermined second threshold. The relaxed state means, for example, a state in which the sympathetic nerve index is less than the first threshold and the parasympathetic nerve index is above the second threshold. The stress state means, for example, a state in which the sympathetic nerve index is above the first threshold and the parasympathetic nerve index is less than the second threshold. The depressive state means, for example, a state in which the sympathetic nerve index is less than the first threshold and the parasympathetic nerve index is less than the second threshold.

[0279] As an example, the life improvement application 340 generates information representing the user's exercise performance from past sleep information stored in the life improvement server 402, autonomic nerve information obtained by the autonomic nerve acquisition unit 342, information representing body temperature, information representing blood pressure, and information representing heart rate obtained by the sensor unit 311. The past sleep information refers to the user's past sleep state information. The "past" refers to the time point before the instrument control unit 344 controls the operation of the instrument 404. The past sleep information is, for example, cumulative data of the user's sleep information from several days ago to the previous day.

[0280] The above-mentioned exercise performance is an index representing the user's physical condition. The life improvement application 340 generates information representing thinking ability from past sleep information, autonomic nerve information, and respiratory information. The life improvement application 340 generates, for example, information representing concentration and information representing drowsiness from autonomic nerve information. The life improvement application 340 generates information representing reaction speed from autonomic nerve information and body movement information. The life improvement application 340 can also numerically represent the user's exercise performance, thinking ability, concentration, and reaction speed respectively. The life improvement application 340 generates information representing the number of turns from, for example, past sleep information and body movement information.

[0281] The memory unit 343 stores the sleep state information obtained by the sleep state acquisition unit 341. As an example, the memory unit 343 stores the sleep state information in the life improvement server 402. The memory unit 343 can also store the sleep state information in the memory of, for example, the information terminal 401.

[0282] The instrument control unit 344 controls the operation of the instrument 404 based on at least one of the past sleep information pre-stored in the life improvement server 402 and the autonomic nerve information obtained by the autonomic nerve acquisition unit 342. The instrument control unit 344 can determine, for example, from the past sleep information whether the proportion of rapid eye movement sleep is above a predetermined value. The instrument control unit 344 can determine, for example, from the past sleep information whether the sleep efficiency is above a predetermined value. The instrument control unit 344 can determine, for example, whether the average value of the time (sleep latency) from the bedtime to the falling asleep time in the past sleep information is above a predetermined time.

[0283] The instrument control unit 344 can determine whether the number of turns is above a predetermined value from information such as the number of turns indicating turning over. The instrument control unit 344 can determine whether the sympathetic nerve index is above a predetermined value from, for example, autonomic nerve information. The instrument control unit 344 can determine whether the number of mid-awakenings obtained by the sleep state acquisition unit 341 is above a predetermined value. The instrument control unit 344 can determine whether the user's motor performance is above a predetermined value from information such as information indicating motor performance. The instrument control unit 344 can determine whether the user's thinking ability is above a predetermined value from information such as information indicating thinking ability. The instrument control unit 344 can determine whether the user's reaction speed is above a predetermined value from information such as information indicating reaction speed. The instrument control unit 344 can control the operation of the instrument 404 based on the determination result of at least any one of the foregoing.

[0284] The life improvement system 301 further includes, for example, an instrument server 405. The instrument server 405 can communicate with the instrument control unit 344. The instrument server 405 receives information for controlling the operation of the instrument 404 from the instrument control unit 344. The instrument server 405 controls the instrument 404 based on the received information. However, when the instrument control unit 344 can directly communicate with the instrument 404, the instrument server 405 may not be provided. The instrument server 405 can also communicate with the life improvement server 402. In this case, the instrument server 405 can communicate with the information terminal 401 via the life improvement server 402.

[0285] The instrument 404 constitutes the environment around the user. The instrument 404 includes at least any one of, for example, an air conditioner installed in the user's bedroom, lighting fixtures, a pillow used by the user for sleeping, a warm mattress, a coffee machine, and a car.

[0286] When the instrument 404 is an air conditioner, for example, the instrument control unit 344 can adjust the temperature of the space where the user is located by controlling the operation of the air conditioner. The instrument control unit 344 can control the instrument 404 to raise the temperature of the space to warm the space when it is determined that the proportion of rapid eye movement sleep is above a predetermined value.

[0287] In the case where the instrument 404 is, for example, a lighting fixture, the instrument control unit 344 can adjust at least one of the brightness and color temperature of the space where the user is located by controlling the operation of the lighting fixture. The instrument control unit 344 can, for example, control the instrument 404 to brighten the space where the user is located before noon, such as the next morning, when it is determined that the sleep efficiency is not above a predetermined value. The instrument control unit 344 can perform the same control as described above, for example, when the mental state of the user is classified into a relaxed state or a depressed state by the autonomic nerve acquisition unit 342. In this case, since the space can be brightened, it helps to improve the mental state of the user and the like.

[0288] In the case where the instrument 404 is, for example, a pillow, the instrument control unit 344 can control the angle of the placement surface of the pillow that supports the user's head relative to the horizontal plane by controlling the operation of the pillow. The instrument control unit 344 can, for example, control the instrument 404 to make the angle of the placement surface relative to the horizontal plane smaller when it is determined that the average value of the time from the bedtime to the falling asleep time in the past sleep information is above a predetermined time. For example, when it is determined from the past sleep information and respiratory information that it is necessary to ensure the respiratory tract, the angle of the user's head and neck relative to the horizontal plane can be adjusted, so the inclination angle of the pillow can be adjusted so that the user's head is in a side-sleeping position. Thereby, the user's snoring can be suppressed.

[0289] In the case where the instrument 404 is, for example, a warm mattress, the instrument control unit 344 can adjust the temperature inside the user's bed by controlling the operation of the warm mattress. The instrument control unit 344 can, for example, control the instrument 404 to raise the temperature inside the bed when it is determined that the number of turns is not above a predetermined value and the sleep efficiency is not above a predetermined value. The instrument control unit 344 can, for example, control the instrument 404 to raise the temperature inside the bed in real time when it is determined that the number of mid-awakenings is above a predetermined value.

[0290] In the case where the instrument 404 is, for example, a coffee machine, the instrument control unit 344 can adjust the concentration of the coffee that the user will drink after waking up by controlling the operation of the coffee machine. The instrument control unit 344 can, for example, control the instrument 404 to make the concentration of the coffee higher when it is determined that the sympathetic nerve index is not above a predetermined value.

[0291] In the case where the instrument 404 is a vehicle, for example, the vehicle can perform vehicle control in multiple driving modes. The multiple driving modes include, for example, an assisted driving mode (autopilot mode) that assists the driver in driving, and a normal mode that does not assist in driving. The instrument control unit 344 can, for example, switch the driving mode of the vehicle to the assisted driving mode or the normal mode by controlling the vehicle, or recommend the user to make the switch. The instrument control unit 344 can, for example, when it is determined that the motor performance is not above a predetermined value, switch the operation of the vehicle to the assisted driving mode or recommend the user to make the switch. The instrument control unit 344 can, for example, when it is determined that the user's thinking ability is not above a predetermined value, perform the same control as described above. The instrument control unit 344 can, for example, when it is determined that the user's reaction speed is not above a predetermined value, perform the same control as described above. The instrument control unit 344 can also, for example, switch the operation of the vehicle to the normal mode or recommend the user to make the switch.

[0292] The prediction information generation unit 345 generates information representing the predicted state of the waking user, i.e., prediction information, based on at least one of the past sleep information obtained by the sleep state acquisition unit 341 and the autonomic nerve information obtained by the autonomic nerve acquisition unit 342. As an example, the prediction information includes information representing the mental state of the user, i.e., mental information, information representing the physical condition of the user, i.e., physical condition information, and information representing the state of the user's mind, i.e., mind information.

[0293] The prediction information generation unit 345 can determine whether the sleep efficiency is above a predetermined value from, for example, the past sleep information. The prediction information generation unit 345 can determine whether the user's concentration is above a predetermined value from, for example, the information representing concentration. The prediction information generation unit 345 can generate prediction information from the determination result of the sleep efficiency and the determination result of the concentration described above.

[0294] The prediction information generation unit 345 generates mental information from, for example, the autonomic nerve information. The mental information includes, for example, information related to the ups and downs of the user's mood. As an example, the mental information includes information indicating whether the user's mood tends to be low. The prediction information generation unit 345 can, for example, when the autonomic nerve acquisition unit 342 classifies the user's mental state as a stress state or a depressive state, generate information indicating that the user's mood tends to be low as mental information.

[0295] The prediction information generation unit 345 generates physical condition information from, for example, past sleep information. The physical condition information includes, for example, information indicating whether the physical condition of the user during waking is good or not. The prediction information generation unit 345 can generate, for example, information indicating that the physical condition of the user tends to deteriorate as the physical condition information when it is determined that the sleep efficiency is not above a predetermined value. As an example, the physical condition information includes skin information indicating the state of the user's skin. The skin information includes, for example, information indicating that the predicted skin quality of the user is poor. Poor skin quality means, for example, a state in which the moisture content of the skin has decreased below a certain value.

[0296] The prediction information generation unit 345 generates brain information from, for example, past sleep information and autonomic nerve information. The brain information is information indicating whether the operation of the user's brain is good or not. The brain information includes, for example, information indicating whether the concentration, memory, and thinking ability of the user are good or not. The prediction information generation unit 345 can generate, for example, information indicating that the predicted concentration of the user is high as the brain information when the psychological state of the user is classified as a high-efficiency state by the autonomic nerve acquisition unit 342. The prediction information generation unit 345 can generate, for example, information indicating that the predicted thinking ability of the user is low as the brain information when it is determined that the sleep efficiency is not above a predetermined value. The prediction information generation unit 345 can also perform the same processing as above when, for example, the psychological state of the user is classified as a stress state, a relaxation state, or a depression state by the autonomic nerve acquisition unit 342, or when it is determined that the concentration of the user is not above a predetermined value.

[0297] The improvement information generation unit 346 generates information for improving the user's life, that is, improvement information, based on the prediction information generated by the prediction information generation unit 345. As an example, the improvement information includes at least one of information indicating the type of clothes to be recommended to the user, information indicating the types of soap, cosmetics, and food, and information indicating the recommended actions. The information indicating the type of clothes includes, for example, at least one of information about the color of the clothes, the type of fabric of the clothes, and the number of pieces of clothes worn.

[0298] As an example, the improvement information generation unit 346 generates information indicating the type of cosmetics as the improvement information from the skin information generated by the prediction information generation unit 345. The improvement information generation unit 346 generates information indicating the types of cosmetics that improve the moisture retention of the skin when, for example, the prediction information generation unit 345 generates information indicating that the predicted skin quality is poor.

[0299] The improvement information generation unit 346 generates information indicating the type (e.g., color) of clothes to be recommended to the user as improvement information from information indicating, for example, that the mood of the expected user tends to be low. As an example, when the autonomic nerve acquisition unit 342 classifies the mental state of the user as a depressive state, the improvement information generation unit 346 generates improvement information with the main idea of recommending warm-colored (e.g., red) clothes. The improvement information generation unit 346 generates at least one of information indicating the types of soap, cosmetics, and food to be recommended to the user and information indicating that sunlight should be avoided during the day as improvement information from information indicating, for example, that the skin condition of the predicted user is poor.

[0300] The improvement information generation unit 346 generates information indicating that it is recommended not to make important decisions as improvement information from information indicating, for example, that the thinking ability of the predicted user is very low. The improvement information generation unit 346 generates information indicating which of the assisted driving mode or the normal mode should be recommended as the driving mode of the vehicle as improvement information from information indicating, for example, sports performance information, thinking ability information, and reaction speed information.

[0301] As an example, the life improvement application program 340 causes the prediction information generated by the prediction information generation unit 345 and the improvement information generated by the improvement information generation unit 346 to be displayed on the information terminal 401. Figure 18 FIG. is an example of an output screen 382 showing the prediction information and the improvement information displayed on the information terminal 401. The life improvement application program 340 causes, for example, the prediction information and the improvement information to be displayed as the output screen 382 on the information terminal 401. The output screen 382 is, for example, a screen for providing the prediction information and the improvement information to the user. The output screen 382 includes a prediction information display unit 383, which is a part for displaying the prediction information, and an improvement information display unit 384, which is a part for displaying the improvement information.

[0302] The prediction information display unit 383 displays at least one of information indicating, for example, that the mood of the predicted user tends to be low, information indicating that the skin condition is predicted to be poor, and information indicating that the concentration and thinking ability are very low. The prediction information display unit 383 may also display information indicating, for example, that the mood of the predicted user tends to be exciting, information indicating that the skin condition is predicted to be good, and information indicating that the concentration and thinking ability are very high.

[0303] The improvement information display unit 384 displays information such as information indicating the color of clothes recommended for the user, information indicating that sunlight should be avoided during the day, information indicating that major decisions should not be made, and information indicating that the assisted driving mode is recommended as the driving mode of the vehicle. The improvement information display unit 384 displays information such as information on soaps, cosmetics, and foods recommended for the user. The improvement information display unit 384 displays the URL of a website 403 that reveals information related to the products recommended for the user, for example.

[0304] As an example, the improvement information generation unit 346 generates improvement information from the aforementioned prediction information and subjective information. More specifically, the improvement information generation unit 346 obtains objective information from past sleep information and autonomic nerve information. The so-called objective information refers to the objective information of the user obtained from objective data obtained by measurement such as sleep state information or autonomic nerve information. The objective information includes, for example, an objective evaluation of sleep. The improvement information generation unit 346 obtains the subjective evaluation of sleep included in the subjective information. The improvement information generation unit 346 generates feedback information as improvement information from the subjective evaluation of sleep and the objective evaluation of sleep. The feedback information includes, for example, information indicating the difference between the subjective evaluation of sleep and the objective evaluation of sleep.

[0305] As an example, the life improvement application program 340 causes the feedback information generated by the improvement information generation unit 346 to be displayed on the information terminal 401. Figure 19 It is a diagram showing an example of the output screen 385 of the feedback information displayed on the information terminal 401. The life improvement application program 340 causes, for example, the feedback information to be the output screen 385 and displayed on the information terminal 401. The output screen 385 is a screen for providing feedback information to the user, for example. The output screen 385 includes a subjective information display unit 386 that displays the subjective evaluation of sleep, an objective information display unit 387 that displays the objective evaluation of sleep, and a feedback display unit 388 that displays the feedback information.

[0306] The subjective information display unit 386 displays subjective information. The subjective information display unit 386 displays, for example, the subjective evaluation of sleep input by the user in the sleep evaluation input unit 381 (see Figure 15 ). The objective information display unit 387 displays objective information. The objective information display unit 387 displays the objective evaluation of sleep of the user based on, for example, the sleep state information obtained by the sleep state acquisition unit 341 and the autonomic nerve information obtained by the autonomic nerve acquisition unit 342. The objective information display unit 387 displays, for example, an objective evaluation of sleep with a full score of 100. The feedback display unit 388 displays, for example, information indicating the difference between the subjective evaluation of sleep and the objective evaluation of sleep.

[0307] The life improvement application 340 implements total feedback. The total feedback includes improving the accuracy of the control of the instrument 404. The total feedback includes improving the accuracy of the generation of prediction information and improvement information. The instrument control unit 344 can, for example, prioritize the control of the instrument 404 based on subjective evaluation over the control of the instrument 404 based on objective evaluation when the subjective evaluation of sleep is higher than a predetermined value more than the objective evaluation of sleep. In this way, more control of the instrument 404 according to the user's subjective can be achieved.

[0308] The communication between the sensor unit 311 and the life improvement application 340, the communication between the life improvement application 340 and the life improvement server 402, the communication between the life improvement server 402 and the information terminal 401, the communication between the life improvement server 402 and the instrument server 405, the communication between the instrument control unit 344 and the instrument server 405, and the communication between the instrument server 405 and the instrument 404 can be achieved through a wireless communication interface such as a wireless LAN (Local Area Network) or Bluetooth (registered trademark). The above communication can also be implemented in a wired manner.

[0309] An example of the operation of the life improvement system 301 will be described. Figure 20 It is a flowchart showing an example of the operation of the life improvement system 301. Before operating the life improvement system 301, first, the sensor sheet 312 is installed on the mattress 310a, and the pad sensor 313 is installed on the pad 310b. Next, the user's body is placed on the mattress 310a or the pad 310b. For example, during sleep, the user's body is placed on the mattress 310a in contact with the fabric 331 of the cover 303. For example, during the day, the user's body is placed on the seating portion 315 of the pad 310b.

[0310] The sensor unit 311 obtains respiration information, body movement information, and heartbeat information from the user's body movement with respiration, body movement with heartbeat, and body movement not associated with respiration and heartbeat (step S301). When obtaining the user's vital sign data through the sensor sheet 312, in step S301, the sensor sheet 312 detects the user's body movement with respiration, body movement with heartbeat, and body movement not associated with respiration and heartbeat. The sensor sheet 312 outputs a signal representing the body movement with respiration, a signal representing the body movement with heartbeat, and a signal representing the body movement not associated with respiration and heartbeat to the outside of the sensor sheet 312. When obtaining the user's vital sign data through the pad sensor 313, in step S301, the pad sensor 313 detects the user's body movement with heartbeat. The pad sensor 313 outputs a signal representing the body movement with heartbeat to the outside of the pad sensor 313. The sensor unit 311 outputs a signal representing the body movement with respiration, a signal representing the body movement with heartbeat, and a signal representing the body movement not associated with respiration and heartbeat to the outside of the sensor unit 311.

[0311] The sleep state acquisition unit 341 obtains sleep state information from the signals output by, for example, the sensor unit 311. The sleep state acquisition unit 341 obtains the user's sleep state information from at least one of the respiration information, body movement information, and heartbeat information obtained by the sensor unit 311 (step S302). In step S302, the sleep state acquisition unit 341 obtains, for example, the user's bedtime, wake-up time, proportion of sleep stages, sleep latency, sleep efficiency, number of mid-sleep awakenings, and mid-sleep awakening time from the respiration information, body movement information, and heartbeat information.

[0312] The autonomic nerve acquisition unit 342 obtains autonomic nerve information from the heartbeat information obtained by the sensor unit 311 (step S303). In step S303, for example, the autonomic nerve acquisition unit 342 analyzes the heartbeat information obtained by the sensor unit 311 to obtain the user's heart rate variability. In step S303, the autonomic nerve acquisition unit 342 obtains autonomic nerve information from the user's heart rate variability. In step S303, the autonomic nerve acquisition unit 342 obtains the user's mental state from the autonomic nerve information.

[0313] The instrument control unit 344 controls the operation of the instrument 404 based on at least one of the past sleep information and autonomic nerve information (step S304). In step S304, for example, the instrument control unit 344 controls the operation of the coffee machine. The instrument control unit 344 can control the operation of the coffee machine to increase the concentration of the coffee the user will drink after waking up according to, for example, the past sleep information and the user's mental state.

[0314] The prediction information generation unit 345 generates prediction information based on at least one of the past sleep information pre-stored in the life improvement server 402 and the autonomic nerve information obtained by the autonomic nerve acquisition unit 342 (step S305). In step S305, the prediction information generation unit 345 generates at least one of mental information, physical condition information, and brain information as prediction information based on, for example, the past sleep information and the autonomic nerve information. The prediction information generation unit 345 generates, for example, skin information as the physical condition information. The prediction information generation unit 345 generates, for example, information indicating that the mood of the predicted user tends to be low as the prediction information. The prediction information generation unit 345 generates, for example, information indicating the goodness or badness of the physical condition of the user during waking as the prediction information. The prediction information generation unit 345 generates, for example, information indicating that the thinking ability and concentration of the predicted user are high as the prediction information.

[0315] The improvement information generation unit 346 generates improvement information based on the prediction information generated by the prediction information generation unit 345 (step S306). The improvement information generation unit 346 generates, for example, information indicating the type of clothes to be recommended to the user as the improvement information. The improvement information generation unit 346 generates, for example, information indicating the types of soap, cosmetics, and food as the improvement information. The improvement information generation unit 346 generates, for example, information indicating the recommended actions as the improvement information.

[0316] In step S306, the improvement information generation unit 346 generates improvement information based on the prediction information and the subjective information. The improvement information generation unit 346 obtains, for example, the objective evaluation of sleep included in the objective information obtained from the sleep state information and the autonomic nerve information. The improvement information generation unit 346 obtains, for example, the subjective evaluation of sleep included in the subjective information input by the user. The improvement information generation unit 346 generates feedback information based on, for example, the subjective evaluation of sleep and the objective evaluation of sleep. The improvement information generation unit 346 generates, for example, information indicating the difference between the subjective evaluation of sleep and the objective evaluation of sleep.

[0317] The life improvement application 340 displays the prediction information generated by the prediction information generation unit 345 and the improvement information generated by the improvement information generation unit 346. The life improvement application 340, for example, causes the prediction information and the improvement information to be output screen 382 (see Figure 18 ) and is displayed on the information terminal 401. The life improvement application 340 displays the feedback information generated by the improvement information generation unit 346. The life improvement application 340, for example, causes the feedback information to be output screen 385 (see Figure 19 ) and is displayed on the information terminal 401.

[0318] The life improvement application 340 implements overall feedback (step S307). In step S307, for example, when the subjective sleep evaluation is higher than the objective sleep evaluation by more than a predetermined value, the instrument control unit 344 of the life improvement application 340 gives priority to the control of the subjective instrument 404 over the control of the objective instrument 404.

[0319] As described above, an example of the steps of the operation of the life improvement system 301 has been described. However, the content and order of the steps of the operation of the life improvement system 301 are not limited to the foregoing examples and can be changed as appropriate.

[0320] Next, the effects of the life improvement system 301 will be described. As an example, the life improvement system 301 generates prediction information of the user based on at least one of past sleep information and autonomic nerve information. The life improvement system 301 can provide information indicating the physical and mental state of the user, for example, as the prediction information to the user. The life improvement system 301 generates improvement information based on the prediction information. The life improvement system 301 can, for example, when predicting that the physical and mental state of the user will tend to be bad, provide information indicating that it is recommended not to make important decisions as the improvement information to the user. The life improvement system 301 not only controls the instrument 404 during the user's sleep, but can also predict the state of the user during waking based on the user's sleep, and can provide information for improving the user's life according to the sleep state.

[0321] The life improvement system 301 can generate prediction information and improvement information based on the user's past sleep state. Not only the sleep state of the current day, but also the user's past sleep state can be added to generate prediction information indicating the predicted state of the waking user and improvement information for improving life. Compared with the case of generating prediction information and improvement information only based on the sleep state between when the user goes to bed and wakes up, prediction information and improvement information based on a longer-term record can be generated. Therefore, the prediction information and improvement information can be generated more accurately.

[0322] As an example, the sensor unit 311 includes a mat sensor 313 installed on the mat 310b on which the user sits. Thus, not only during sleep, for example, but also during the day, the vital sign data of the user can be continuously obtained. Since the vital sign data of the user continuously measured during the day can be added to generate prediction information and improvement information, the prediction information and improvement information can be generated more accurately using the states of both when the user is asleep and awake.

[0323] As an example, the prediction information includes information indicating the mental state of the user, i.e., mental information, information indicating the physical condition of the user, i.e., physical condition information, and information indicating the mental state of the user, i.e., mental information. Thus, the mental, physical condition, and mental state of the waking user can be predicted. The physical condition information includes skin information indicating the state of the user's skin. Thus, information indicating the state of the user's skin can be provided as prediction information.

[0324] As an example, the improvement information generation unit 346 generates improvement information based on information indicating the subjective evaluation of the user, i.e., subjective information, and the prediction information. Since the improvement information is generated by adding the subjective evaluation of the user, the improvement information can be generated more accurately.

[0325] As an example, the improvement information includes information indicating the types of clothes, foods, and cosmetics to be recommended to the user. Thus, information indicating the types of clothes, foods, and cosmetics to be recommended to the user can be provided as improvement information.

[0326] As an example, the instrument 404 may include a pillow used by the user for sleeping. The instrument control unit 344 can control the angle of the placement surface of the pillow supporting the user's head with respect to the horizontal plane. In this case, since the angle of the placement surface of the pillow can be adjusted during the user's sleep, the sleep state of the user can be further improved.

[0327] As an example, the life improvement system 301 includes: a sensor unit 311 that acquires information indicating the heartbeat of the user, i.e., heartbeat information; an autonomic nerve acquisition unit 342 that acquires information indicating the state of the user's autonomic nerves, i.e., autonomic nerve information, from the heartbeat information; a prediction information generation unit 345 that generates information indicating the predicted state of the waking user, i.e., prediction information, based on the autonomic nerve information; and an improvement information generation unit 346 that generates information for improving the user's life, i.e., improvement information, based on the prediction information.

[0328] For example, the life improvement system 301 generates prediction information of the user based on the autonomic nerve information and generates improvement information based on the prediction information. Thus, the state of the user during waking can be predicted, and information for improving the user's life can be provided.

[0329] Next, a specific example of the sleeping posture determination system and the sleeping posture determination program will be described. As an example, the sleeping posture determination system and the sleeping posture determination program not only determine the sleeping posture of the user, but also provide the user with suggestions for improving the sleeping posture. The sleeping posture determination system and the sleeping posture determination program can be used for personal or household use, or for experimental research use. The sleeping posture determination system and the sleeping posture determination program can also be used in facilities such as hospitals or welfare facilities.

[0330] A user refers to a person who uses the sleeping posture determination system or the sleeping posture determination program. The user can be a user of bedding. The user is, for example, a person who wishes to determine their sleeping posture, a person with sleep troubles, or a person who will experience physical and mental disorders without good sleep. The number of users can be one or more. The sleeping posture determination system and the sleeping posture determination program can be used for multiple users.

[0331] As an example, the sleeping posture determination system has a sensor unit mounted on the bedding. First, refer to Figure 21 (a) and Figure 21 illustrate an example of the sensor unit with reference to (b). Figure 21 (a) of is a perspective view showing a bedding 501 equipped with a sensor unit 511 as an example. Figure 21 (b) of is a perspective view showing the core material 502 of the bedding 501.

[0332] As shown in Figure 21 (a) and Figure 21 (b) of, the bedding 501 is a mat that appears rectangular when viewed from above. The bedding 501 extends in the long side direction D501 and the short side direction D502 orthogonal to the long side direction D501. The bedding 501 has a thickness in the thickness direction D503 orthogonal to both the long side direction D501 and the short side direction D502.

[0333] For example, the bedding 501 is a mattress. The bedding 501 has a core material 502 and a cover 503 that houses the core material 502. The core material 502 has, for example, contents and a bag body that houses the contents. The core material 502 has an upper surface 502b that bears the user's body, a lower surface 502c facing the opposite side of the upper surface 502b, and a plurality of side surfaces 502d that connect the upper surface 502b and the lower surface 502c to each other.

[0334] The cover 503 has, for example: an upper fabric 503b that covers the upper surface 502b of the core material 502, a lower fabric 503c that covers the lower surface 502c of the core material 502, and an opening / closing member 503d that connects the upper fabric 503b and the lower fabric 503c to each other. The opening / closing member 503d is arranged at a position facing the side surface 502d of the core material 502. When viewed from the thickness direction D503, the opening / closing member 503d is located outside the side surface 502d of the core material 502.

[0335] The cover 503 can be attached to and detached from the core material 502. Being attachable to and detachable from the core material includes rolling up the cover upward with respect to the core material and rolling up the cover to expose a part of the core material. An example of the opening / closing member 503d is a double slider. For example, the opening / closing member 503d has two sliders 503f and an element 503g that is the movement path of the slider 503f and that attaches and detaches the upper cloth 503b with respect to the lower cloth 503c by the movement of the slider 503f.

[0336] In a state where the upper cloth 503b is attached to the lower cloth 503c, an opening 504 through which a wire 514b described later passes is formed between the two sliders 503f. In this state, by moving either one of the two sliders 503f along the element 503g to widen the opening 504, the core material 502 can be taken out from the widened opening 504. In a state where the core material 502 is housed in the lower cloth 503c, by moving either one of the two sliders 503f along the element 503g to close the opening 504, the upper cloth 503b can be attached to the lower cloth 503c.

[0337] The cover 503 has a fixed portion 503h to which the sensor unit 511 is fixed. The fixed portion 503h is provided on the inside of the cover 503. The cover 503 has a plurality of fixed portions 503h. The fixed portion 503h is provided, for example, at a position on the upper cloth 503b facing the side surface 502d of the core material 502. As an example, the plurality of fixed portions 503h are arranged along the long side direction D501. Figure 21 In the example of (a), two fixed portions 503h are arranged along the thickness direction D503. For example, a plurality of rows (an example is four rows) formed by two fixed portions 503h arranged along the thickness direction D503 are arranged along the long side direction D501. For example, the fixed portion 503h is constituted by a snap button 503s.

[0338] For example, the sensor unit 511 is a sensor sheet disposed between the core material 502 and the cover 503. The sensor unit 511 acquires vital sign data of the user of the bedding 501. The vital sign data includes, for example, information related to the user's heartbeat, breathing, and body movement. The content of the vital sign data can be appropriately changed. The vital sign data may also include, for example, information related to blood pressure.

[0339] The sensor unit 511 is disposed, for example, at a position on the long side direction D501 corresponding to the user's heart. As an example, the sensor unit 511 is disposed at an arbitrary position from the position where the user's head is supported to a position away along the long side direction D501 of the bedding 501 ( Figure 21 the lower right position in (a)).

[0340] For example, the sensor unit 511 has a long side 511b extending along the long side direction of the sensor unit 511, that is, the first direction A501, and a short side 511c extending along the short side direction of the sensor unit 511, that is, the second direction A502. For example, the sensor unit 511 is arranged such that the long side 511b extends along the short side direction D502 of the bedding 501, and the short side 511c extends along the long side direction D501 of the bedding 501.

[0341] Figure 22 is a plan view showing the sensor unit 511. As Figure 21 in (a) of Figure 22 shown, the sensor unit 511 includes, for example, a sheet-like fabric 512, a sensor 513, and a power supply unit 514. In a state where the sensor unit 511 is arranged on the bedding 501, the central portion of the sheet-like fabric 512 is placed on the upper surface 502b of the core material 502, and both end portions of the sheet-like fabric 512 in the first direction A501 face the side surface 502d of the core material 502. For example, the sheet-like fabric 512 has stretchability.

[0342] For example, the sheet-like fabric 512 is a triple structure formed by overlapping three pieces of fabric along a third direction A503 that intersects both the first direction A501 and the second direction A502. The sheet-like fabric 512 has an inner fabric 512b and an outer fabric 512c that houses the inner fabric 512b. Inside the outer fabric 512c, the inner fabric 512b is sewn to the outer fabric 512c. As an example, the peripheral portion of the inner fabric 512b is sewn to the outer fabric 512c.

[0343] For example, the outer fabric 512c is made of a material having waterproof properties. As an example, the outer fabric 512c can be a fabric with lamination processing or resin processing applied to the inside (the inner surface of the outer fabric 512c). The outer fabric 512c can be a membrane fabric. The outer fabric 512c can be a fabric having waterproof properties.

[0344] The sensor 513 has, for example, a linear sensor 515 embroidered on the sheet-like fabric 512 and detecting vital sign data from the body of the user borne by the sensor unit 511, and a data acquisition unit 516 that acquires the vital sign data detected by the linear sensor 515. For example, the linear sensor 515 is embroidered on the inner fabric 512b. The linear sensor 515 is fixed by embroidery in a two-dimensional extended manner on the sheet-like fabric 512, for example. The data acquisition unit 516 is fixed to an end portion of the inner fabric 512b in the first direction A501. The data acquisition unit 516 outputs the acquired vital sign data to the outside of the sensor unit 511. The function of the data acquisition unit 516 will be described later.

[0345] The linear sensor 515 detects the vital sign data of the user of the bedding 501. The type of the linear sensor 515 is not particularly limited. For example, the linear sensor 515 is a single sensor, and a single linear sensor 515 is fixed by embroidery in a two-dimensional extended manner on the sheet-like fabric 512. Figure 22 In the example, the linear sensor 515 is in the center of the second direction A502 of the sheet-like fabric 512 and has a shape symmetric with respect to the center line L extending along the first direction A501.

[0346] For example, the linear sensor 515 has a curved portion 515b that is curved when viewed along the third direction A503 and a straight portion 515c that is straight when viewed along the third direction A503. The curved portion 515b has a first waveform portion 515d located on one side in the short side direction D502 when viewed from the center line L and a second waveform portion 515f located on the other side in the short side direction D502 when viewed from the center line L.

[0347] The first waveform portion 515d has first peak portions 515h and first valley portions 515j alternately arranged along the first direction A501, and the second waveform portion 515f has second peak portions 515k and second valley portions 515p alternately arranged along the first direction A501. The first waveform portion 515d and the second waveform portion 515f are connected to each other at the end of the sheet-like fabric 512 opposite to the data acquisition portion 516.

[0348] The straight portion 515c has a first straight portion 515q and a second straight portion 515r arranged along the second direction A502. One end of the first straight portion 515q in the first direction A501 is connected to the end of the first waveform portion 515d in the first direction A501. One end of the second straight portion 515r in the first direction A501 is connected to the end of the second waveform portion 515f in the first direction A501.

[0349] The end of the first straight portion 515q opposite to the first waveform portion 515d and the end of the second straight portion 515r opposite to the second waveform portion 515f are each connected to the data acquisition portion 516. By connecting the first straight portion 515q, the first waveform portion 515d, the second waveform portion 515f, and the second straight portion 515r to each other, the linear sensor 515 is formed as a single sensor.

[0350] The power supply unit 514 starts the sensor 513 by supplying power to the data acquisition unit 516. The power supply unit 514 has, for example, a wire 514b and a plug 514c. One end of the wire 514b is connected to the data acquisition unit 516, and a connector 514d is provided at the other end of the wire 514b. The wire 514b is connected to the plug 514c via the connector 514d. For example, the plug 514c can be inserted into a socket. In the power supply unit 514, power is supplied from the plug 514c to the data acquisition unit 516 via the connector 514d and the wire 514b.

[0351] The sensor unit 511 includes fixing parts 517, 518. The fixing parts 517, 518 are parts for fixing the sheet-like fabric 512 to the cover cloth 503. The fixing parts 517, 518 can be attached to and detached from the cover cloth 503. The fixing part 517 is provided at one end side of the sheet-like fabric 512 in the first direction A501, and the fixing part 518 is provided at the other end side of the sheet-like fabric 512 in the first direction A501.

[0352] Each of the fixing parts 517, 518 has a snap fastener 519. The snap fastener 519 is snapped onto, for example, the snap fastener 503s of the cover cloth 503. Each of the fixing parts 517, 518 has a plurality (an example is four) of snap fasteners 519. In the fixing part 517, two snap fasteners 519 arranged along the first direction A501 are provided at one end side of the sheet-like fabric 512 in the second direction A502. In the fixing part 517, two snap fasteners 519 arranged along the first direction A501 are provided at the other end side of the sheet-like fabric 512 in the second direction A502. In the fixing part 517, for example, the plurality of snap fasteners 519 are arranged in a polygonal shape (an example is a quadrangular shape). Since the arrangement of the snap fasteners 519 of the fixing part 518 is the same as that of the snap fasteners 519 of the fixing part 517, the description thereof is omitted.

[0353] The portion of the sheet-like fabric 512 where the fixing parts 517, 518 are not provided is placed on the upper surface 502b of the core material 502. Each of the fixing parts 517, 518 is fixed to the fixed part 503h of the cover cloth 503 at a position facing the side surface 502d of the core material 502. At this time, each snap fastener 519 is snapped onto each snap fastener 503s constituting the fixed part 503h.

[0354] Figure 23 It is a perspective view showing the fixing portion of the sensor unit 511 to the cover cloth 503. Figure 23 Shown is the state where the upper fabric 503b that winds the cover cloth 503 is rolled up in the state where the sensor unit 511 is fixed to the cover cloth 503. The fixing parts 517, 518 fix the sheet-like fabric 512 to the cover cloth 503 at a position between the side surface 502d of the core material 502 and the cover cloth 503.

[0355] Two snap fasteners 519 arranged along the first direction A501 of the fixing part 518 are respectively fixed to the fixed part 503h of the cover cloth 503 (refer to Figure 21 ). As described above, the fixed part 503h has two snap fasteners 503s arranged in the thickness direction D503 of the cover cloth 503. The two snap fasteners 519 arranged in the first direction A501 of the fixing part 518 are respectively snapped onto the two snap fasteners 503s arranged in the thickness direction D503 of the cover cloth 503. Similar to the fixing part 518, the fixing part 517 is snapped onto the snap fasteners 503s of the fixed part 503h of the cover cloth 503.

[0356] By fixing the sensor part 511 to the cover cloth 503 by using a plurality of snap fasteners 519, the position deviation of the sensor part 511 can be suppressed when the user's body is placed on the bedding 501. Since the fixing parts 517 and 518 fix the sensor part 511 to the cover cloth 503 via the snap fasteners 519, the sensor part 511 can be easily detached from the cover cloth 503.

[0357] Figure 24 It is a perspective view showing the data acquisition part 516 as an example. The data acquisition part 516 has an electrical functional part 516b, a mounting member 516c, and a screw 516d. The electrical functional part 516b has electronic components and a housing 516f that houses the electronic components. The electronic components are electrically connected to the linear sensor 515.

[0358] The housing 516f has an insertion port 516h into which one end of the power supply line 514b is inserted and an insertion hole for inserting the screw 516d. A screw thread groove for screwing the screw 516d is formed on the inner surface of the insertion hole. The mounting member 516c is a member for mounting the electrical functional part 516b to the sheet-like fabric 512. The mounting member 516c has: a plate-like part 516j, and a protruding part 516k that protrudes in the third direction A503 from the end in the first direction A501 of the plate-like part 516j ( Figure 24 the lower right end in ). The plate-like part 516j has a through hole 516p that penetrates the plate-like part 516j in the third direction A503.

[0359] A method for fixing the data acquisition part 516 to the sheet-like fabric 512 will be described. The electrical functional part 516b is arranged on the sheet-like fabric 512 (the inner fabric 512b or the outer fabric 512c). As an example, the electrical functional part 516b is arranged on the surface of the inner fabric 512b, and the mounting member 516c is arranged on the back of the inner fabric 512b ( Figure 24The lower surface in). The plate-like portion 516j contacts the inner surface of the inner fabric 512b. The plate-like portion 516j is arranged such that the protruding portion 516k protrudes further in the first direction A501 than the end of the sheet-like fabric 512 in the first direction A501, and protrudes in the third direction A503 ( Figure 24 above in).

[0360] Insert the screw 516d through the through-hole 516p along the third direction A503. For example, a hole for inserting the screw 516d is formed in the sheet-like fabric 512, and the screw 516d inserted through the through-hole 516p penetrates through this hole. Screw the screw 516d that penetrates through this hole into the screw thread groove of the insertion hole of the housing 516f. Clamp the inner fabric 512b between the electrical function portion 516b and the mounting member 516c to fix the data acquisition portion 516 to the sheet-like fabric 512.

[0361] Furthermore, the configuration of the data acquisition portion 516 and the fixing method of the data acquisition portion 516 to the sheet-like fabric 512 are not limited to the above examples and can be appropriately changed. The data acquisition portion 516 may not be fixed to the sheet-like fabric 512.

[0362] A sleep posture determination system and a sleep posture determination program as an example will be described. Figure 25 is a block diagram showing the functional configuration of a sleep posture determination system 520 and a sleep posture determination program 540 as an example. As Figure 25 shown, the sleep posture determination system 520 includes, for example, a sensor portion 511, an information terminal 521, and a sleep state improvement instrument 522.

[0363] The information terminal 521 is a computer that executes each step of the sleep posture determination program 540. The information terminal 521 is, for example, a portable terminal. Portable terminal means, for example: portable information terminals such as mobile phones including smartphones, tablets or notebook personal computers, wearable terminals such as watches, etc. The information terminal 521 may also be a terminal other than a portable terminal and may be, for example, a desktop computer.

[0364] An example of the information terminal 521 includes: a processor (such as a CPU) that executes an operating system (OS) and software (application programs), a main memory portion composed of a ROM and a RAM, an auxiliary memory portion composed of a flash memory, etc., a communication control portion composed of a wireless communication module, etc., an input device, and an output device such as a display. However, the configuration of the information terminal 521 is not limited to the above description and can be appropriately changed. Hereinafter, the ROM and the RAM will be collectively referred to as memory.

[0365] The information terminal 521 executes the sleeping posture determination program 540 as an application program. The sleeping posture determination program 540 is, for example, an application program downloaded to the information terminal 521 and executed on the information terminal 521. However, the sleeping posture determination program 540 may also be executed on the server. The sleeping posture determination program 540 may also be an application program downloaded from the server. Hereinafter, an example will be described in which the sleeping posture determination program 540 is an application program downloaded to the information terminal 521 and the functions of the sleeping posture determination program 540 are executed on the information terminal 521.

[0366] Each function of the sleeping posture determination program 540 is implemented by causing the processor or the main memory unit to read a predetermined software and execute the software. The data or database required for the execution of the functions of the sleeping posture determination program 540 is stored in the main memory unit or the auxiliary memory unit.

[0367] Each functional element of the information terminal 521 is implemented by causing the processor or the memory unit (such as the aforementioned main memory unit or auxiliary memory unit) to read a predetermined software and execute the software. The data or database used in the processing of the information terminal 521 is stored in the memory unit.

[0368] The sleeping posture determination program 540 may be a distributed processing system composed of multiple computers, or may be a client-server system or a cloud system. The sleeping posture determination program 540 includes, for example, a main module, a data acquisition module, a determination module, and an output module. The functions of each functional element of the sleeping posture determination program 540 are exerted by executing the data acquisition module, the determination module, and the output module. As an example, the sleeping posture determination program 540 may be a program provided after being fixedly recorded on a tangible memory medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. The sleeping posture determination program 540 may also be provided as a data signal superimposed on a carrier wave via a communication network.

[0369] As described above, the sensor unit 511 acquires information on the user's body as vital sign data. The sensor unit 511 acquires at least one of respiratory information, body movement information, and heartbeat information as vital sign data. As an example, the sensor unit 511 acquires respiratory information, body movement information, and heartbeat information. The content of the information to be acquired by the sensor unit 511 can be appropriately changed. The sensor unit 511 may also acquire, for example, at least any one of information indicating blood pressure (blood pressure information) and pulse.

[0370] The sensor unit 511 generates an electrical signal corresponding to the load on the user's body that is applied thereto. The sensor unit 511 generates the electrical signal in the form of a waveform that includes the aforementioned respiration information, body movement information, and heartbeat information. Hereinafter, the waveform may also be referred to as a signal. The sensor unit 511 has a communication unit that outputs the waveform to the outside of the sensor unit 511. Furthermore, in the above description, although the example in which the sensor unit 511 has the linear sensor 515 has been described. However, the sensor unit 511 may also include sensors other than the linear sensor 515, and may include, for example, an acceleration sensor.

[0371] The respiration information includes a signal (waveform) indicating the body movement of the user with respiration. The sensor unit 511 detects the body movement of the user with respiration. The sensor unit 511 outputs the signal indicating the body movement with respiration to the outside of the sensor unit 511.

[0372] The sensor unit 511 detects the body movement of the user with heartbeat. The sensor unit 511 outputs the signal indicating the body movement with heartbeat to the outside of the sensor unit 511. The sensor unit 511 detects the body movement of the user that is not with respiration and heartbeat. The sensor unit 511 outputs the signal indicating the body movement that is not with respiration and heartbeat to the outside of the sensor unit 511.

[0373] An example of the functional configuration of the sleep posture determination program 540 will be described. The sleep posture determination program 540 has a memory unit 541, a display unit 542, and a communication unit 543. The memory unit 541 stores the data used for executing the functions of the sleep posture determination program 540, and the data obtained as a result of executing the functions of the sleep posture determination program 540. The memory unit 541 stores the data in, for example, the memory of the information terminal 521. In this case, the function of the memory unit 541 is implemented by the CPU and memory of the information terminal 521.

[0374] For example, the display unit 542 causes the result of executing the functions of the sleep posture determination program 540 to be displayed on the display 521b of the information terminal 521. In this case, the function of the display unit 542 is implemented by the CPU and output device of the information terminal 521. However, the display unit 542 may also cause the result of executing the functions of the sleep posture determination program 540 to be displayed on the display of a terminal other than the information terminal 521.

[0375] The communication unit 543 transmits and receives data stored in the memory unit 541 to and from terminals other than the information terminal 521. For example, the communication unit 543 transmits data obtained as a result of the function of executing the sleeping posture determination program 540 to an information terminal outside the information terminal 521. The communication unit 543 may also receive data from an information terminal outside the information terminal 521 into the information terminal 521. The function of the communication unit 543 is implemented by the communication control unit of the information terminal 521.

[0376] The sleeping posture determination program 540 has a waveform detection unit 551, a body movement acquisition unit 552, a respiration acquisition unit 553, a heartbeat acquisition unit 554, a turning-over determination unit 555, a sleeping posture determination unit 556, a sleep state acquisition unit 557, a recommendation generation unit 558, an instrument control unit 559, an autonomic nerve acquisition unit 560, an apnea state acquisition unit 561, and a sleeping posture status generation unit 562 as its functional components. The functions of the waveform detection unit 551, the body movement acquisition unit 552, the respiration acquisition unit 553, the heartbeat acquisition unit 554, the turning-over determination unit 555, the sleeping posture determination unit 556, the sleep state acquisition unit 557, the recommendation generation unit 558, the instrument control unit 559, the autonomic nerve acquisition unit 560, the apnea state acquisition unit 561, and the sleeping posture status generation unit 562 are implemented by causing the CPU of the information terminal 521 to operate according to the commands of the sleeping posture determination program 540 installed in the information terminal 521. The autonomic nerve acquisition unit 560 acquires information indicating the state of the user's autonomic nerve, i.e., autonomic nerve information, from the heartbeat information acquired through the sensor unit 511. The apnea state acquisition unit 561 detects the apnea state of the user from the respiration information acquired through the sensor unit 511.

[0377] The waveform detection unit 551 detects the waveforms generated by the sensor unit 511 via the communication unit 543. For example, the waveforms detected by the waveform detection unit 551 include waveforms representing respiration information, body movement information, and heartbeat information. The waveforms detected by the waveform detection unit 551 may also include at least one of blood pressure information and pulse.

[0378] The body movement acquisition unit 552 acquires the user's body movement information from the waveforms obtained via the waveform detection unit 551. The respiration acquisition unit 553 acquires the user's respiration information from the waveforms obtained via the waveform detection unit 551. The heartbeat acquisition unit 554 acquires the user's heartbeat information from the waveforms obtained via the waveform detection unit 551.

[0379] The turning-over determination unit 555 determines whether the user has turned over based on, for example, the voltage value represented by a waveform obtained from the aforementioned linear sensor 515. The turning-over determination unit 555 may also determine whether there is a turning-over from the waveform, and when it determines that there is a turning-over from the waveform, it stores the information on the turning-over in the storage unit 541. The information on the turning-over represents information on changes in turning-over such as turning from supine to side-sleeping or from side-sleeping to prone-sleeping.

[0380] The sleeping posture determination unit 556 determines the sleeping posture of the user from the waveform output by the sensor unit 511. The turning-over determination unit 555 determines whether there is a turning-over. In contrast, the sleeping posture determination unit 556 determines the turning-over of the user at this time, and the sleeping posture determination unit 556 has a different function from the turning-over determination unit 555 in this regard. The sleeping posture determination unit 556 executes a step of determining the sleeping posture of the user from the waveform representing the load of the user of the bedding 501 output by the sensor unit 511 in the information terminal 521.

[0381] The sleeping posture determination unit 556 determines whether the sleeping posture of the user is supine, side-sleeping, or prone-sleeping from the waveform output by the sensor unit 511. Figure 26 (a) shows an example of the waveform obtained when supine, Figure 26 (b) shows an example of the waveform obtained when side-sleeping, Figure 26 (c) shows an example of the waveform obtained when prone-sleeping. Figure 26 (a), Figure 26 (b), and Figure 26 (c) each have the horizontal axis representing time and the vertical axis representing the amplitude of the voltage value.

[0382] As Figure 26 (a) shows, when supine, the relatively flat parts such as the back and buttocks press against the sensor unit 511, and in most cases, it is difficult for the pressure to concentrate, so the waveform W501 with a smaller amplitude is obtained through the sensor unit 511. As Figure 26 (b) shows, when side-sleeping, the relatively prominent parts such as the shoulder, the protruding part of the ilium, the upper arm, the elbow, the forearm, the side abdomen, or the rib press against the sensor unit 511, and it is easy for the pressure to concentrate at one point, so the waveform W502 with a larger amplitude is obtained through the sensor unit 511. As Figure 26 (c) shows, when prone-sleeping, because the distance from the sensor unit 511 to the heart is relatively close or the hand is sandwiched between the front of the body and the cover 503, a waveform W503 with more noise is obtained compared with other cases.

[0383] For example, the memory unit 541 pre-stores a first simulated waveform indicating supine sleep, a second simulated waveform indicating side sleep, and a third simulated waveform indicating prone sleep. The first simulated waveform is a waveform similar to waveform W501, the second simulated waveform is a waveform similar to waveform W502, and the third simulated waveform is a waveform similar to waveform W503.

[0384] For example, the sleep position determination unit 556 compares the waveform output by the sensor unit 511 with the first simulated waveform, the second simulated waveform, and the third simulated waveform to determine which of the first simulated waveform, the second simulated waveform, and the third simulated waveform the waveform output by the sensor unit 511 is closest to. In this case, when the sleep position determination unit 556 determines that the waveform output by the sensor unit 511 is closest to the first simulated waveform, it determines that the user's sleep position is supine sleep; when it determines that the waveform output by the sensor unit 511 is closest to the second simulated waveform, it determines that the user's sleep position is side sleep; and when it determines that the waveform output by the sensor unit 511 is closest to the third simulated waveform, it determines that the user's sleep position is prone sleep.

[0385] For example, the sleep position determination unit 556 can measure the time series data of supine sleep, side sleep, and prone sleep respectively. As an example, the sleep position determination unit 556 determines the user's sleep position based on the waveform of the voltage value obtained from the linear sensor 515. The sleep position determination unit 556 can also measure the time and number of times the user sleeps supine, the time and number of times the user sleeps on the side, and the time and number of times the user sleeps prone.

[0386] For example, the sleep state acquisition unit 557 acquires the sleep stage from the respiration information, body movement information, and heartbeat information. The sleep state acquisition unit 557 acquires the falling asleep state (the goodness of falling asleep) based on the time from the time of going to bed to the time of falling asleep. The sleep state acquisition unit 557 acquires the waking state (the goodness of waking up) based on the time from the waking time to the getting up time and the sleep stage a certain time before the waking time. This certain time is preset, for example, and can be appropriately changed. A certain time before the waking time means, for example, the near time before the waking time. By acquiring the waking state based on the sleep stage a certain time before the waking time, the goodness of waking up can be grasped more effectively.

[0387] The so-called falling asleep time refers to the time when, between the time of going to bed and the time of getting up, the sleep stage first changes to a sleep stage (a stage other than waking). The so-called waking time refers to the time when, between the time of going to bed and the time of getting up, the sleep stage finally changes to the waking stage.

[0388] As an example, the sleep state acquisition unit 557 obtains the user's bedtime and wake-up time from the body movement information acquired by the sensor unit 511 and the movement of the bedding detected by the acceleration sensor of the sensor unit 511. The sleep state acquisition unit 557 obtains the sleep stage by using the signal indicating the body movement with breathing, the signal indicating the body movement with heartbeat, and the signal indicating the body movement not with breathing and heartbeat output from the sensor unit 511. For example, the sleep state acquisition unit 557 obtains the sleep onset time from the bedtime and the sleep stage. More specifically, the sleep state acquisition unit 557 obtains the time of the stage that first becomes sleep between the bedtime and the wake-up time as the sleep onset time. The sleep state acquisition unit 557 obtains the awakening time from the wake-up time and the sleep stage. More specifically, the sleep state acquisition unit 557 obtains the time of the stage that finally becomes awake between the bedtime and the wake-up time as the awakening time. The sleep state acquisition unit 557 obtains the sleep stage a certain time before the awakening time.

[0389] The sleep state acquisition unit 557 determines whether the time from the bedtime to the sleep onset time is longer than a predetermined time by using, for example, the bedtime and the sleep onset time. The sleep state acquisition unit 557 determines whether the user's sleep onset state is good or not based on the determination result of the time from the bedtime to the sleep onset time. For example, the sleep state acquisition unit 557 determines whether the sleep onset state is good or not as the goodness of falling asleep.

[0390] The sleep state acquisition unit 557 determines whether the time from the awakening time to the wake-up time is longer than a predetermined time by using, for example, the awakening time and the wake-up time. The sleep state acquisition unit 557 determines whether the sleep stage of the user a certain time before the awakening time is non-rapid eye movement sleep based on, for example, the sleep stage a certain time before the awakening time. The sleep state acquisition unit 557 determines whether the user's awakening state is good or not based on at least one of the foregoing determination results. For example, the sleep state acquisition unit 557 determines whether the awakening state is good or not as the goodness of waking up.

[0391] For example, when the sleep state acquisition unit 557 determines that the time from the bedtime to the sleep onset time is not longer than the predetermined time, it determines that the user's sleep onset state is good (good at falling asleep). For example, when the sleep state acquisition unit 557 determines that the time from the bedtime to the sleep onset time is longer than the predetermined time, it determines that the user's sleep onset state is bad (bad at falling asleep).

[0392] The sleep state acquisition unit 557 determines that the user's awakening state is good (waking up well), for example, when it determines that the time from the awakening time to the waking-up time is not longer than a predetermined time. The sleep state acquisition unit 557 determines that the user's awakening state is good (waking up well), for example, when it determines that the sleep stage of the user a certain time before the awakening time is not non-rapid eye movement sleep. The sleep state acquisition unit 557 determines that the user's awakening state is not good, for example, when it determines that the time from the awakening time to the waking-up time is longer than a predetermined time, or when it determines that the sleep stage of the user a certain time before the awakening time is non-rapid eye movement sleep.

[0393] The advice generation unit 558 generates advice for improving the user's sleeping posture based on the sleeping posture determined by the sleeping posture determination unit 556. The advice generation unit 558 executes in the information terminal 521: the step of generating advice for improving the user's sleeping posture based on the sleeping posture determined in the determination step.

[0394] For example, the advice generation unit 558 includes an alarm output unit 558b and a product proposal unit 558c. The alarm output unit 558b outputs an alarm about the sleeping posture to the user as advice. For example, the alarm output unit 558b generates an alarm with the main idea that apnea often occurs because the supine sleep is more than other sleeping postures as advice. The product proposal unit 558c outputs advice on product proposals. For example, the product proposal unit 558c outputs a product proposal of a pillow that can promote side sleeping to the user who often has apnea as advice.

[0395] The advice is, for example, advice for improving the user's physical condition. For example, the advice is either advice on which sleeping posture will improve the sleep state or which sleeping posture can improve the sleep state when the sleep state is not good.

[0396] The advice can also be either advice on which sleeping posture can make the user spend the day energetically when the time of that sleeping posture is prolonged or advice on which sleeping posture can improve the immunity when the time of that sleeping posture is prolonged. The advice generated by the advice generation unit 558 only needs to be useful to the user who has received the sleeping posture determination, and the type of advice is not particularly limited.

[0397] The memory unit 541 stores various pieces of advice to be provided to the user. The advice generation unit 558 generates the advice to be provided to the user by extracting the advice corresponding to the determination result of the sleeping posture determination unit 556 from the advice stored in the memory unit 541. For example, the advice extracted by the advice generation unit 558 from the memory unit 541 is displayed on the display 521b of the information terminal 521 through the display unit 542.

[0398] Figure 27This is a diagram showing an example of a suggestion. For example, the display unit 42 displays the sleep state C together with the suggestion B. The sleep state C shows the ratio of the previous (last night) sleeping postures. The sleep state C shows the ratios of the supine, side, and prone sleeping states during the previous sleep. The suggestion B shows suggestions for improving sleep corresponding to the content of the sleep state C. As an example, the suggestion B shows that since the measured user has a tendency to have apnea, side or prone sleeping is recommended.

[0399] Figure 28 This is a diagram showing another example of a suggestion. For example, the display unit 542 displays the suggestion B501 extracted by the suggestion generation unit 558 and the sleep state C501 obtained by the sleep state acquisition unit 557 together on the display 521b of the information terminal 521. The display unit 542 displays the ratios of the sleeping postures during rapid eye movement (REM) sleep and non-REM sleep respectively according to the sleeping posture determined by the sleeping posture determination unit 556 and the sleep state obtained by the sleep state acquisition unit 557.

[0400] For example, the display unit 542 displays the ratios of the sleeping postures during REM sleep, non-REM light sleep, and non-REM deep sleep respectively. Figure 28 The examples shown are: during REM sleep, supine sleeping accounts for 30%, side sleeping accounts for 50%, and prone sleeping accounts for 20%; during light sleep, supine sleeping accounts for 15%, side sleeping accounts for 25%, and prone sleeping accounts for 60%; during deep sleep, supine sleeping accounts for 55%, side sleeping accounts for 35%, and prone sleeping accounts for 10%.

[0401] By showing the ratios of the sleeping postures in each sleep state (sleep stage), the user can grasp in which sleep state (sleep stage) it is easy to change to supine, side, or prone sleeping. Figure 28 In the case of this example, it can be grasped that during REM sleep, side sleeping is more common; during light sleep, prone sleeping is more common; during deep sleep, supine sleeping is more common.

[0402] The display unit 542 displays the suggestion B501 together with the ratios of the sleeping postures in each sleep state. The suggestion B501 is, for example, a suggestion indicating the current situation of the sleep state relative to the sleeping posture. As a specific example, the suggestion B501 is that you tend to have lighter sleep when you sleep prone, and it is recommended that you reduce prone sleeping. The type of the suggestion B501 displayed on the display 521b varies for each user according to the sleeping posture and sleep state during the previous sleep. Therefore, the most suitable suggestion for the user can be provided based on the sleeping posture and sleep state. In this way, a sleeping posture that can improve the sleep state can be proposed for each user.

[0403] Figure 29 This is a diagram showing the relationship with Figure 28Diagrams of different display screens, suggestion B502, and sleep state C502. The display unit 542 obtains the sleep state based on the sleeping position determined by the sleeping position determination unit 556 and the sleep state obtained by the sleep state acquisition unit 557, and displays the ratios of the sleep states (sleep stages) during supine sleep, side sleep, and prone sleep respectively. For example, the display unit 542 displays the ratios of rapid eye movement sleep and non-rapid eye movement sleep (light sleep and deep sleep) during supine sleep, side sleep, and prone sleep.

[0404] Figure 29 An example of the display is as follows: during supine sleep, rapid eye movement sleep accounts for 35%, light sleep accounts for 35%, and deep sleep accounts for 30%; during side sleep, rapid eye movement sleep accounts for 40%, light sleep accounts for 25%, and deep sleep accounts for 35%; during prone sleep, rapid eye movement sleep accounts for 25%, light sleep accounts for 65%, and deep sleep accounts for 10%. By displaying the ratios of the sleep states (sleep stages) in each sleeping position, the user can grasp which sleeping position is likely to result in rapid eye movement sleep, light sleep, or deep sleep. Figure 29 In the case of the example, it can be grasped that during side sleep, it is likely to become either rapid eye movement sleep or deep sleep, and during prone sleep, it is likely to become light sleep.

[0405] Figure 30 It represents the Figure 28 and Figure 29 Diagram of an example of suggestion B503 different from that. The suggestion generation unit 558 generates suggestion B503 for improving the user's sleeping position based on the sleeping position determined by the sleeping position determination unit 556, and for example, causes the suggestion B503 to be displayed on the display 521b of the information terminal 521 through the display unit 542. Figure 30 In this case, since the sleeping position determination unit 556 determines that there is more light sleep during side sleep and more deep sleep during supine sleep, the suggestion generation unit 558 generates suggestion B503 with the main idea of recommending supine sleep and causes the suggestion B503 to be displayed on the display 521b.

[0406] As Figure 25 shown, the sleeping position determination program 540 has an instrument control unit 559. The instrument control unit 559 controls the operation of the sleep state improvement instrument 522 based on at least one of, for example, the sleep state pre-stored in the memory unit 541, the autonomic nerve information obtained by the autonomic nerve acquisition unit 560, and the apnea state obtained by the apnea state acquisition unit 561.

[0407] The sleep state improvement device 522 includes at least any one of, for example, a bedding 501, an air conditioner installed in the user's bedroom, lighting fixtures, a pillow used when the user goes to bed, and a warm mattress. For example, when the sleep state improvement device 522 is a pillow, the instrument control unit 559 controls the angle of the placement surface of the pillow that supports the user's head with respect to the horizontal plane by controlling the movement of the pillow. When the instrument control unit 559 obtains a breathless state for a certain period of time or more through, for example, the apnea state acquisition unit 561, the instrument control unit 559 adjusts the inclination of the pillow to make the user's head lie on the side. Thereby, the user's apnea state can be reduced.

[0408] The autonomic nerve acquisition unit 560 obtains information indicating the state of the user's autonomic nerves, that is, autonomic nerve information, from the heartbeat information obtained by the sensor unit 511. The autonomic nerve acquisition unit 560 analyzes the heartbeat information obtained by the sensor unit 511, for example, to obtain the heart rate variability (HRV: Heart Rate Variability) of the user. As an example, the autonomic nerve acquisition unit 560 obtains information indicating the periodic components included in the heart rate variability from the user's heart rate variability. The autonomic nerve acquisition unit 560 performs frequency analysis on the periodic components of the heart rate variability to obtain information indicating the power spectrum of each frequency. The autonomic nerve acquisition unit 560 obtains information indicating the sympathetic nerve index and information indicating the parasympathetic nerve index as autonomic nerve information.

[0409] The autonomic nerve acquisition unit 560 obtains the mental state of the user from the autonomic nerve information. The autonomic nerve acquisition unit 560 classifies the mental state of the user into any one of a high-efficiency state, a relaxed state, a stressed state, and a depressive state according to the autonomic nerve information.

[0410] The autonomic nerve information obtained by the autonomic nerve acquisition unit 560 is stored in the memory unit 541. As Figure 31 shown, the recommendation generation unit 558 can generate a recommendation B504 including the autonomic nerve information obtained by the autonomic nerve acquisition unit 560, and the display unit 542 can display the recommendation B504 on the display 521b of the information terminal 521.

[0411] For example, the recommendation generation unit 558 generates a recommendation B504 on which sleeping position becoming more will make the autonomic nerves more stable and which sleeping position becoming more will make the autonomic nerves more likely to become unstable. Figure 31 In the example of, since the autonomic nerves are more stable when sleeping on the back and are more likely to become unstable when sleeping face down, a recommendation B504 with the main idea of recommending sleeping on the back is displayed on the display 521b.

[0412] The apnea state acquisition unit 561 detects the apnea state (apnea state) of the user from the respiration information. The apnea state acquisition unit 561 detects the abnormal respiration state of the user from, for example, the signal indicating the body movement with respiration, the signal indicating the body movement with heartbeat, and the signal indicating the body movement not with respiration and heartbeat output from the sensor unit 511.

[0413] However, when changing from the abnormal respiration state to the normal respiration state that is not the abnormal respiration state, there is a tendency for the body movement of the user to occur. As an example, the apnea state acquisition unit 561 detects the abnormal respiration state from the body movement information acquired by the sensor unit 511. The apnea state acquisition unit 561 detects the abnormal respiration state of the user from, for example, the signal indicating the body movement with respiration, the signal indicating the body movement with heartbeat, and the signal indicating the body movement not with respiration and heartbeat output from the sensor unit 511.

[0414] When changing from the abnormal respiration state to the normal respiration state, there may be a situation where the heart rate of the user suddenly increases. The apnea state acquisition unit 561 can detect the abnormal respiration state from the heartbeat information acquired by the sensor unit 511. In this case, the apnea state acquisition unit 561 detects the abnormal respiration state of the user from the signal indicating the body movement with heartbeat output from the sensor unit 511.

[0415] The abnormal respiration state (apnea state) detected by the apnea state acquisition unit 561 is memorized in the memory unit 541. As Figure 32 shown, the advice generation unit 558 can generate the advice B505 including the information of the abnormal respiration state acquired by the apnea state acquisition unit 561, and the display unit 542 can display the advice B505 on the display 521b of the information terminal 521.

[0416] For example, the advice generation unit 558 generates the advice B505 on which sleeping position has more occurrences of abnormal respiration state. Figure 32 In the example of

[0417] the advice generation unit 558 generates the advice B505 that side sleeping is better because there is more light sleep and more occurrences of apnea when sleeping on the back, and the advice B505 is displayed on the display 521b. Figure 32 In the example of

[0418] The sleeping posture condition generation unit 562 generates the condition of the user's sleeping posture. As Figure 33 shown, the condition of the user's sleeping posture generated by the sleeping posture condition generation unit 562 is displayed on the display 521b of the information terminal 521 through the display unit 542. For example, in a facility where the sleeping posture determination system 520 and the sleeping posture determination program 540 are used for multiple users, the sleeping posture determination unit 556 determines the sleeping postures of the respective users, the sleep state acquisition unit 557 acquires the sleep states of the respective users, and the advice generation unit 558 generates advice for the respective users. The sleeping posture condition generation unit 562 generates the sleeping postures of the respective users, the sleep states of the respective users, and the advice for the respective users. For example, the display unit 542 forms the advice into Table X and displays it on the display 521b of the information terminal 521.

[0419] In Table X, the sleeping posture, the duration of the sleeping posture, the sleep state, and whether it is a time point of body position change are displayed for each user's name. Figure 33 In the example of, whether it is a time point of body position change is displayed as the advice extracted by the advice generation unit 558. In facilities such as hospitals, there are many users (patients) who cannot change their body positions by themselves. Nurses and the like must change the body positions of the users at regular intervals to prevent pressure sores.

[0420] By displaying Table X on the display 521b, it is possible to grasp which user is better to change the body position for. Since the state of the sleeping posture of each user, the duration of the sleeping posture, and the sleep state can be grasped, the condition of the user can be grasped with higher accuracy.

[0421] The alarm output unit 558b can output an alarm for a user who has maintained a specific sleeping posture for a certain period of time or more. The output of the alarm is performed through at least one of, for example, the display on the display 521b and the sound. For example, the alarm output unit 558b can emphasize and display the row of the user whose duration in Table X reaches two hours (or three hours) or more. As an example, the alarm output unit 558b can output an alarm for a user who has maintained any one of supine, side sleeping, and prone sleeping for two hours (or three hours) or more. In this case, the user who needs to change the body position can be discriminated, so pressure sores can be more surely suppressed. Furthermore, even if a specific sleeping posture is maintained for a certain period of time or more, when the sleep state is deep sleep, for example, the advice generation unit 558 can also give advice that body position change may not be necessary (displayed as "no" in Table X, for example).

[0422] A more detailed description will be given of the effects that can be obtained from the sleeping posture determination system 520 and the sleeping posture determination program 540 as an example. As Figure 25 、 Figure 26 and Figure 27As shown, the sleeping posture determination system 520 includes a sensor unit 511 mounted on a bedding 501. The sensor unit 511 receives the load of the user's body borne by the bedding 501 and outputs a waveform corresponding to the load. In the sleeping posture determination system 520 and the sleeping posture determination program 540, a sleeping posture determination unit 556 determines the sleeping posture based on the waveform output by the sensor unit 511. In the sleeping posture determination system 520 and the sleeping posture determination program 540, a suggestion generation unit 558 generates a suggestion B for improving the user's sleeping posture based on the sleeping posture determined by the sleeping posture determination unit 556. The user can accept the suggestion B for improving their own sleeping posture. The user can improve their sleeping posture, thereby improving their life.

[0423] The sensor unit 511 can acquire, in the form of a waveform, at least one of information indicating the user's respiration, i.e., respiration information, information indicating the user's body movement, i.e., body movement information, and information indicating the user's heartbeat, i.e., heartbeat information. The sleeping posture determination system 520 and the sleeping posture determination program 540 may include: a sleep state acquisition unit 557 that acquires information indicating the user's sleep state, i.e., sleep state C, from a waveform indicating at least one of respiration information, body movement information, and heartbeat information; and a display unit 542 that displays the suggestion B generated by the suggestion generation unit 558 and the sleep state C acquired by the sleep state acquisition unit 557. The sensor unit 511 can acquire, in the form of a waveform, at least one of respiration information, body movement information, and heartbeat information, and the user's sleep state can be acquired by the sleep state acquisition unit 557 based on the waveform. The sleep state C acquired by the sleep state acquisition unit 557 can be displayed together with the suggestion B by the display unit 542. In this case, the user can simultaneously accept the suggestion B and the tendency of their own sleeping posture and sleep state, so more useful information can be provided to the user.

[0424] The sensor unit 511 can acquire, in the form of a waveform, information indicating the user's heartbeat, i.e., heartbeat information. As Figure 25 and Figure 31 shown, the sleeping posture determination system 520 and the sleeping posture determination program 540 may include an autonomic nerve acquisition unit 560 that acquires information indicating the state of the user's autonomic nerves, i.e., autonomic nerve information, from the heartbeat information. The suggestion generation unit 558 can generate a suggestion B504 that includes the autonomic nerve information acquired by the autonomic nerve acquisition unit 560. In this case, the user can grasp the tendency of their own sleeping posture and the state of the autonomic nerves, so more useful information can be provided to the user.

[0425] The sensor unit 511 can acquire, in the form of a waveform, information indicating the state of the user's respiration, i.e., respiration information. As Figure 25 and Figure 32As shown, the sleep posture determination system 520 and the sleep posture determination program 540 may include an apnea state acquisition unit 561 that detects the apnea state of the user from respiratory information. The advice generation unit 558 generates advice B505 that includes the apnea state acquired by the apnea state acquisition unit 561. In this case, the apnea state acquisition unit 561 detects the apnea state of the user, whereby the user can grasp whether he / she has experienced apnea and the frequency of apnea. The advice generation unit 558 may also generate advice B505 with the apnea state added. In this case, the user can obtain information for improving his / her apnea state.

[0426] The sensor unit 511 may be a single sensor sheet disposed on the bedding 501. In this case, various types of information (data) can be acquired without contacting the user's body. More specifically, since there is a cover 503 and the user's clothes between the user's body and the sensor unit 511, the sensor unit 511 does not directly contact the body. By using the sheet-like sensor unit 511, it is possible to easily and highly accurately acquire the sleep posture, and moreover, various types of information such as the sleep state, autonomic nerve, and apnea can be easily and highly accurately acquired.

[0427] Next, a specific example of a bruxism detection system and a bruxism detection program will be described. As an example, the bruxism detection system detects the bruxism of the user. Bruxism refers to dynamically or statically rubbing or biting the teeth. Due to bruxism, there is a concern that the teeth will gradually wear and crack, and the tooth sensitivity or periodontal disease will deteriorate. Bruxism may be a major cause of temporomandibular joint disorder, headache, or shoulder stiffness. Bruxism makes a sound and may also deteriorate the sleep quality of the person sleeping in the same room.

[0428] The bruxism detection system detects the bruxism of the user from the time of falling asleep to the time of waking up. The bruxism detection system can be used, for example, for personal or household use, or can be used in research institutions, etc. for experimental research. The bruxism detection system can also be used for treatment in hospitals, etc. The user refers to a person who is the object to be detected for bruxism by the bruxism detection system. Examples of the user include: a person who has a health risk caused by bruxism, a person who is receiving sleep treatment including bruxism in a hospital, etc., or a person who wishes to check whether he / she has bruxism during sleep.

[0429] Figure 34 It is a block diagram showing an example of a bruxism detection system 601. The bruxism detection system 601 can be executed on an information terminal 700 such as a computer, a tablet terminal, a smartphone, a watch, or a wearable terminal. The bruxism detection system 601 includes, for example, a sensor unit 611 and an information terminal 700. The information terminal 700 has a bruxism detection application program 640 (bruxism detection program) and a display unit 620.

[0430] The molar detection application 640 is, for example, an application executed on the information terminal 700. The molar detection application 640 can be an application downloaded to the information terminal 700 and executed on the information terminal 700. Hereinafter, an example will be described in which the molar detection application 640 is an application downloaded to the information terminal 700 and the functions of the molar detection application 640 are executed on the information terminal 700.

[0431] Each function of the molar detection application 640 is realized by causing a processor or a main memory unit to read a predetermined software and execute the software. The data or database required for the execution of the functions of the molar detection application 640 is stored in the main memory unit or the auxiliary memory unit.

[0432] The molar detection system 601 can be a distributed processing system executed by multiple computers, or can also be a client-server system or a cloud system. The molar detection application 640 includes, for example, a main module, a data acquisition module, a detection module, and an output module. By executing the data acquisition module, the detection module, and the output module, the functions of the respective functional elements of the molar detection application 640 are exerted. As an example, the molar detection application 640 can be fixedly recorded on a tangible memory medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory and then provided. The molar detection application 640 can also be provided as a data signal superimposed on a carrier wave via a communication network.

[0433] The display unit 620 causes information generated, for example, by the molar detection application 640 to be displayed on the display of the information terminal 700. The display unit 620 is a functional unit realized by the processor and the display included in the information terminal 700. The sensor unit 611 acquires information on the user's body. As an example, the sensor unit 611 acquires vibration information. The vibration information is information indicating the vibration of the user.

[0434] Figure 35 (a) shows a perspective view of the pad 610 on which the sensor unit 611 can be installed. Figure 35 (b) shows the composition Figure 35 A perspective view of the core material 602 of the pad 610 shown in (a) of. As Figure 34 And Figure 35 As shown, the sensor unit 611 is, for example, a sensor sheet that can be attached to and detached from the pad 610. The sensor unit 611 acquires vital sign data of the user lying on the pad 610.

[0435] For example, the sensor unit 611 has a sheet-like fabric with embroidered wire-shaped sensors, and the position of this sheet-like fabric relative to the cushion body 610 can be changed. The wire-shaped sensors of the sensor unit 611 are, for example, fixed by embroidery in a two-dimensionally extended manner on this sheet-like fabric. For example, an electrical signal corresponding to the load of the user's body is generated according to the piezoelectric sensor of the sensor unit 611, and the sensor unit 611 acquires this electrical signal as the aforementioned vibration information. Hereinafter, the electrical signal acquired as vibration information is sometimes referred to as a signal.

[0436] The sensor unit 611 has, for example: the aforementioned wire-shaped sensors (an example is a piezoelectric sensor); and a communication unit that outputs the vibration information, which is an electrical signal generated by the wire-shaped sensors, to the outside of the sensor unit 611. Although the above content is described by taking the sensor unit 611 having a piezoelectric sensor as an example, the type of sensor of the sensor unit 611 is not limited to a piezoelectric sensor and is not particularly limited. For example, the sensor unit 611 may also include an acceleration sensor.

[0437] For example, the sensor unit 611 is used by being mounted on the cushion body 610 that bears the user's body. The cushion body 610 is rectangular when viewed from above. The cushion body 610 extends in the long side direction D601 and the short side direction D602 that is orthogonal to the long side direction D601. The cushion body 610 has a thickness in the thickness direction D603 that is orthogonal to both the long side direction D601 and the short side direction D602.

[0438] The length of the long side direction D601 of the cushion body 610 is, for example, 120 cm or more and 210 cm or less (an example is 195 cm). The length of the short side direction D602 of the cushion body 610 is, for example, 70 cm or more and 180 cm or less (an example is 97 cm). The length of the thickness direction D603 of the cushion body 610 is, for example, 3 cm or more and 40 cm or less (an example is 9 cm).

[0439] As an example, the cushion body 610 is a mattress. The user of the cushion body 610 places their body on the cushion body 610. At this time, the direction in which the body of the lying user extends (that is, the direction connecting the user's head and feet) is, for example, the same as the long side direction D601 of the cushion body 610.

[0440] As shown in Figure 35 (b) of, the cushion body 610 has a core material 602 housed inside a cover cloth 603 described later. As an example, the core material 602 is rectangular when viewed from above. The core material 602 has, for example, a content and a bag body that houses the content. The content is, for example, a polyurethane foam or polyester.

[0441] The core material 602 has a rectangular parallelepiped shape. The core material 602 has an upper surface 621 for supporting the user's body and a lower surface 622 facing the opposite side of the upper surface 621. The upper surface 621 is a surface facing one direction side ( Figure 35 the upper side in (b) of) in the thickness direction D603 of the core material 602. The lower surface 622 is a surface facing the other direction side ( Figure 35 the lower side in (b) of) in the thickness direction D603 of the core material 602. The core material 602 has a plurality of side surfaces 623 connecting the upper surface 621 and the lower surface 622.

[0442] As shown in Figure 35 (a) of, the cushion body 610 has a cover cloth 603. As an example, the cover cloth 603 is in a bag shape and is rectangular when viewed from above. The cover cloth 603 has, for example: a fabric 631 covering the upper surface 621 of the core material 602, a lining 632 covering the lower surface 622 of the core material 602, and an opening / closing member 633 connecting the fabric 631 and the lining 632 to each other. The opening / closing member 633 is provided at a position corresponding to the side surface 623 of the core material 602 on the cover cloth 603. When viewed from the thickness direction D603, the opening / closing member 633 is located outside the side surface 623 of the core material 602. The cover cloth 603 can be attached to and detached from the core material 602.

[0443] An example of the opening / closing member 633 is a so-called double slider. The opening / closing member 633 may have two sliders 634 and an element 635 that separates or closes the fabric 631 and the lining 632 by the sliding of the slider 634.

[0444] By sliding one slider 634 along the element 635 in one direction ( Figure 35 the clockwise direction in (a) of), and by sliding the other slider 634 along the element 635 in the other direction ( Figure 35 the counterclockwise direction in (a) of), the fabric 631 and the lining 632 can be separated.

[0445] By sliding one slider 634 along the element 635 in the other direction, and by sliding the other slider 634 along the element 635 in one direction, the fabric 631 and the lining 632 can be closed. The opening / closing member 633 may also be a so-called single slider. In this case, the opening / closing member 633 has one slider 634 and the element 635.

[0446] The sensor unit 611 is disposed, for example, inside the bag-shaped cover cloth 603. More specifically, the sensor unit 611 is disposed between the core material 602 and the cover cloth 603. When the sensor unit 611 is mounted on the cushion body 610, for example, the sensor unit 611 is disposed so as to extend in the short side direction D602 of the cushion body 610. In this case, the sensor unit 611 is located on the side opposite to the user's body when viewed from the cover cloth 603 of the cushion body 610. When the cushion body 610 bears the user's body, the sensor unit 611 does not contact the user.

[0447] The sensor unit 611 not contacting the user means that the sensor unit 611 does not directly contact the user's body. The sensor unit 611 not contacting the user includes the case where the sensor unit 611 is indirectly connected to the user by placing the cushion body 610 between the sensor unit 611 and the user. For example, there are clothes worn by the user and the cover cloth 603 between the user's body and the sensor unit 611. Thus, the sensor unit 611 does not contact the user's body. The sensor unit 611 acquires vibration information in a manner not contacting the user.

[0448] The sensor unit 611 is mounted at a position corresponding to the user's heart in the long side direction D601, for example. As an example, the sensor unit 611 is mounted at an arbitrary position from the position bearing the user's head to the position away along the long side direction D601 of the cushion body 610 ( Figure 35 the position at the lower right in (a)). The mounting position of the sensor unit 611 in the long side direction D601 is changeable. The distance from the position bearing the user's head to the position where the sensor unit 611 is mounted can be, for example, 40 cm or more and 50 cm or less (an example is 50 cm).

[0449] The bruxism detection system 601 includes a waveform memory unit 650. The waveform memory unit 650 is a functional unit implemented by the processor and the memory unit included in the information terminal 700. As Figure 36 shown in (a), (b), and (c) respectively, the waveform memory unit 650 memorizes: the schematic waveform when the vibration caused by bruxism is waveformed (hereinafter referred to as the schematic waveform of bruxism), the schematic waveform when the vibration caused by turning over is waveformed (hereinafter referred to as the schematic waveform of turning over), and the schematic waveform when the vibration occurring during quiet is waveformed (hereinafter referred to as the schematic waveform during quiet). Figure 36 (a) is a schematic waveform diagram of bruxism. Figure 36 (b) is a schematic waveform diagram of turning over. Figure 36 (c) is a schematic waveform diagram during quiet.

[0450] The schematic waveform of teeth grinding is a waveform of theoretical values that reflects the characteristics such as the amplitude, vibration frequency, and duration of the vibration of teeth grinding. The schematic waveform of turning over is a waveform of theoretical values that reflects the characteristics such as the amplitude, vibration frequency, and duration of the vibration of turning over. The schematic waveform at rest is a waveform of theoretical values that reflects the characteristics such as the amplitude, vibration frequency, and duration of the vibration at rest. Each of the above theoretical values can be, for example, a value calculated from the age, gender, or body type of the user.

[0451] The waveform memory unit 650 stores typical examples of sign information indicating signs that occur before teeth grinding. Examples of sign information include: the case where the user's heart rate increases before the predetermined time of teeth grinding, and the case where the user's sympathetic nerve index increases before teeth grinding. Heartbeat refers to the beating of the heart. The predetermined time refers to, for example, several seconds or not long ago. The waveform memory unit 650 stores a schematic waveform of the waveform of sign information generated due to the increase in the user's heart rate before teeth grinding. Figure 37 It is a schematic waveform diagram of the waveform of sign information generated due to the increase in the user's heart rate before teeth grinding. As Figure 37 shown, a waveform generated due to an increase in the heart rate can be confirmed in the vicinity of the waveform generated due to teeth grinding.

[0452] The waveform memory unit 650 pre-stores a teeth grinding waveform. The waveform memory unit 650 stores, for example, the teeth grinding waveform output by the teeth grinding determination unit 644 described later. The schematic waveform of teeth grinding is a waveform of theoretical values that reflects the characteristics of the vibration of teeth grinding. In contrast, the teeth grinding waveform is the waveform of the user's past teeth grinding actually detected. The waveform memory unit 650 has, for example, the same number of data of teeth grinding waveforms as the number of the user's past teeth grinding waveforms detected. The teeth grinding waveform may also include the past teeth grinding waveforms of people different from the user. In this case, the waveform memory unit 650 has a larger number of data of teeth grinding waveforms than the number of the user's past teeth grinding waveforms detected.

[0453] The teeth grinding detection application program 640 obtains the user's bedtime and wake-up time from, for example, vibration information. The teeth grinding detection application program 640 can obtain the bedtime by determining the bedtime from, for example, the body movement information obtained by the sensor unit 611 and the movement of the bedding detected by the acceleration sensor of the sensor unit 611. The same applies to obtaining the wake-up time. Alternatively, different from the above example, the user operates the teeth grinding detection application program 640 using the information terminal 700, and the bedtime and wake-up time are obtained through the user's operation.

[0454] The molar detection application program 640 includes a detection unit 641 that detects the user's teeth grinding from vibration information and symptom information. The detection unit 641 has a waveform detection unit 642 that detects vibration information in the form of a waveform, a symptom information extraction unit 643 that extracts symptom information, and a molar determination unit 644 that determines whether the detected waveform is a waveform caused by teeth grinding.

[0455] The detection unit 641 extracts information indicating the state of the user's autonomic nerves, i.e., autonomic nerve information, from the vibration information. For example, the detection unit 641 extracts heartbeat information from the vibration information. The detection unit 641 analyzes the extracted heartbeat information to obtain the user's heart rate variability (HRV: Heart Rate Variability). The detection unit 641 obtains autonomic nerve information from the obtained heart rate variability of the user. The detection unit 641 performs frequency analysis on the periodic components of the heart rate variability to obtain information representing the power spectrum of each frequency. The detection unit 641 obtains information representing the sympathetic nerve index and information representing the parasympathetic nerve index as autonomic nerve information.

[0456] The waveform detection unit 642 detects vibration information in the form of a waveform by waveformizing a signal representing the change in the load applied by the user to the piezoelectric sensor of the sensor unit 611. When the difference between the amplitude of the detected waveform and the amplitude of the schematic waveform of teeth grinding is below a predetermined threshold, the waveform detection unit 642 extracts the detected waveform as a candidate waveform. For example, the amplitude of the waveform of teeth grinding measured by the sheet sensor is larger than the amplitude of the waveform caused by quiet breathing and heartbeat measured by the sheet sensor and smaller than the amplitude of the waveform caused by turning over and body movement measured by the sheet sensor. The waveform detection unit 642 can extract the waveform measured by the sheet sensor as a candidate waveform when the amplitude of the waveform measured by the sheet sensor is larger than the amplitude of the quiet waveform and smaller than the amplitude of the turning-over waveform.

[0457] The waveform detection unit 642 may also extract candidate waveforms by means other than comparing the amplitude of the detected waveform with the amplitude of the schematic waveform of grinding teeth. In addition to the amplitude of the detected waveform, the waveform detection unit 642 may also extract candidate waveforms based on, for example, the vibration frequency and the duration of the waveform. For example, the phenomenon that the periodic vibration of the grinding teeth measured by the sheet sensor is 2 Hz or more and 4 Hz or less and lasts for 2 seconds or more. The waveform detection unit 642 detects a waveform that lasts for 2 seconds or more. The waveform detection unit 642 detects the vibration frequency and the amplitude of the waveform. The waveform detection unit 642 may use the case where the difference between the amplitude of the waveform and the amplitude of the schematic waveform of grinding teeth is below a threshold, the case where the difference between the vibration frequency of the waveform and the vibration frequency of the schematic waveform of grinding teeth is below a threshold, and the case where the duration of the waveform is 2 seconds or more to extract the waveform as a candidate waveform.

[0458] The waveform detection unit 642 may further detect the case where the detected waveform does not have the characteristics of the schematic waveform of turning over and the characteristics of the schematic waveform at rest memorized in the waveform memory unit 650. In this case, it is possible to avoid confusing the waveforms of turning over and at rest with the waveform of grinding teeth.

[0459] The symptom information extraction unit 643 includes a heartbeat symptom extraction unit 6431 that extracts symptom information from the heartbeat information and an autonomic nerve symptom extraction unit 6432 that extracts symptom information from the autonomic nerve information. Before the user grinds teeth, the user's heart rate will increase. The heartbeat symptom extraction unit 6431 extracts the increase in the heart rate that occurs before grinding teeth from the heartbeat information as symptom information. More specifically, the heartbeat symptom extraction unit 6431 extracts the case where the user's heart rate increases before a predetermined time of the candidate waveform from the heartbeat information as symptom information.

[0460] Before the user grinds teeth, the user's sympathetic nerve will be hyperactive. The autonomic nerve symptom extraction unit 6432 extracts the increase in the exchange nerve index that occurs before grinding teeth from the autonomic nerve information as symptom information. More specifically, the autonomic nerve symptom extraction unit 432 extracts the case where the user's sympathetic nerve index increases before a predetermined time of the candidate waveform from the autonomic nerve information as symptom information.

[0461] The molar determination unit 644 confirms that the candidate waveform is a waveform caused by molar grinding based on the situation where the sign information has been extracted. In the case where the sign information has not been extracted, the molar determination unit 644 can refer to the waveform memory unit 650 and confirm that the candidate waveform is a waveform caused by molar grinding by comparing the pre - memorized molar grinding waveform with the candidate waveform. In the case where it has been confirmed that the candidate waveform is a waveform caused by molar grinding, the molar determination unit 644 outputs the candidate waveform as a new molar grinding waveform to the waveform memory unit 650. The waveform memory unit 650 memorizes the output new molar grinding waveform as a past molar grinding waveform.

[0462] The molar determination unit 644 determines, for example, the high or low occurrence frequency of molar grinding. Figure 38 It is a schematic diagram showing an example of a reference for the high or low occurrence frequency of molar grinding. Figure 38 The words "high", "medium", and "low" shown indicate the high or low occurrence frequency of molar grinding. Figure 38 In the two - dimensional chart shown, the horizontal axis represents the maximum detection count. The maximum detection count is the maximum number of molar grindings detected in each interval when the time period of the user's sleep is divided at a predetermined interval to generate intervals.

[0463] One of the above - mentioned intervals is a unit time obtained by dividing the sleep time at a certain time interval, and an example is one hour. In this case, for example, when the user's sleep time is from 10 p.m. to 6 a.m. the next morning, the intervals are from 10 p.m. to 11 p.m., from 11 p.m. to 0 a.m., from 0 a.m. to 1 a.m.... from 5 a.m. to 6 a.m. For example, when the duration of the waveform confirmed as molar grinding is 2 seconds or more, one occurrence of molar grinding is detected. In Figure 38 In the chart shown, the vertical axis represents the average detection count. The average detection count is the average number of molar grindings detected in each interval.

[0464] X601, X602, X603, Y601, Y602, and Y603 are predetermined natural numbers. As an example, X601 is 5, X602 is 10, and X603 is 15. As an example, Y601 is 2, Y602 is 4, and Y603 is 6. The values of X601 - X603 and Y601 - Y603 can be changeable values. The molar determination unit 644 outputs the high or low occurrence frequency of molar grinding to the display unit 620 included in the molar detection system 601.

[0465] An example of the operation of the molar detection system 601 will be described. Figure 39FIG. 0 is a flowchart showing an example of the operation of the bruxism detection system 601. Before operating the bruxism detection system 601, the sensor unit 611 is attached to the cushion 610. The sensor unit 611 is attached to the cushion 610 in such a way that the sensor unit 611 does not contact the user when the user's body is resting on the cushion 610. For example, the sensor unit 611 is disposed between the core material 602 and the cover 603. The user's body lies horizontally on the cushion 610 to which the sensor unit 611 is attached. For example, the user's body lies on the cushion 610 in contact with the fabric 631 of the cover 603.

[0466] The sensor unit 611 obtains vibration information including heartbeat information and autonomic nerve information from vibrations generated by the user's body movement or the like (step S601). In step S601, the sensor unit 611 obtains vibration information representing the vibration of the user. The sensor unit 611 outputs a signal representing, for example, a change in the load applied by the user to the piezoelectric sensor to the outside of the sensor unit 611. The detection unit 641 receives the signal output from the sensor unit 611. The detection unit 641 extracts the heartbeat information and the autonomic nerve information from the vibration information received as a signal.

[0467] The waveform detection unit 642 detects the vibration information in the form of a waveform by waveformizing the above signal output from the sensor unit 611 (step S602).

[0468] The waveform detection unit 642 determines whether the detected waveform includes a candidate waveform (step S603). In step S603, the waveform detection unit 642 extracts the candidate waveform by confirming that the detected waveform includes the characteristics of a waveform indicating bruxism and that the detected waveform does not include the characteristics of a waveform indicating turning over and the characteristics of a waveform indicating quietness.

[0469] When it is determined that the waveform detected by the waveform detection unit 642 includes a candidate waveform (the result of S603 is "yes"), the symptom information extraction unit 643 extracts symptom information from the vibration information (step S604). In step S604, the heartbeat symptom extraction unit 6431 extracts symptom information from the heartbeat information near the time point when the candidate waveform is detected. The autonomic nerve symptom extraction unit 6432 extracts symptom information from the autonomic nerve information near the time point when the candidate waveform is detected.

[0470] The bruxism determination unit 44 determines whether the vibration information includes symptom information (step S605). In step S605, the bruxism determination unit 644 determines both of the following: whether symptom information is extracted from the heartbeat information by the heartbeat symptom extraction unit 6431 and whether symptom information is extracted from the autonomic nerve information by the autonomic nerve symptom extraction unit 6432.

[0471] When the molar determination unit 644 determines that the vibration information does not contain sign information (the result of S605 is "no"), the molar determination unit 644 compares the candidate waveform with the past molar waveforms memorized in advance (step S606). In step S606, the molar determination unit 644 determines whether the candidate waveform has the characteristics of the past molar waveforms memorized in advance.

[0472] When the molar determination unit 644 determines that the sign information is included (the result of S605 is "yes"), the molar determination unit 644 confirms that the candidate waveform is the waveform caused by molar (step S607). As described above, the detection unit 641 detects molar from the vibration information and the sign information.

[0473] When the molar determination unit 644 determines that the candidate waveform has the characteristics of the past molar waveforms memorized in advance (the result of S606 is "yes"), the molar determination unit 644 confirms that the candidate waveform is the waveform caused by molar (step S607).

[0474] The molar determination unit 644 outputs the confirmed molar waveform (step S608). In step S608, the molar determination unit 644 outputs the candidate waveform as a new molar waveform to the waveform memory unit 650.

[0475] The molar determination unit 644 increases the detection count of molar by 1 time (step S609). In step S609, the molar determination unit 644 increases the detected count by 1 time in a manner related to the time when the candidate waveform is detected. For example, when the candidate waveform is detected and confirmed to be the waveform caused by molar in the interval from 3 am to 4 am (one interval), the molar count in the interval from 3 am to 4 am is incremented by 1.

[0476] During the user's sleep, the molar determination unit 644 calculates the detection count. When the user wakes up, the molar determination unit 644 calculates the average detection count and the maximum detection count of the molar count from the molar counts of each added interval. The molar determination unit 644 outputs the calculated average detection count and the maximum detection count to the display unit 620. The display unit 620 displays Figure 38 the two-dimensional graph shown. The display unit 620 displays the occurrence frequency of the user's molar by plotting the points corresponding to the maximum detection count and the average detection count of the user on Figure 38 the two-dimensional graph shown.

[0477] When the waveform detection unit 642 determines that the vibration information does not contain a candidate waveform (the result of S603 is "no"), or when the molar determination unit 644 determines that the candidate waveform does not have the characteristics of the past molar waveforms memorized in advance (the result of S606 is "no"), the molar determination unit 644 confirms that the detected waveform does not include a molar waveform (step S610). Furthermore, the content and order of each step of the operation of the molar detection system 601 are not limited to the foregoing examples and can be changed as appropriate.

[0478] The operation and effect of the molar detection system 601 as an example will be described. In the molar detection system 601, the sensor unit 611 acquires vibration information indicating the vibration of the user without contacting the user. Since the user detects molar teeth in a state where they do not contact the sensor unit 611, the burden on the user during molar tooth detection can be reduced. The detection unit 641 detects molar teeth from the vibration information indicating the vibration of the user and the sign information indicating the signs of molar teeth that occur before molar teeth. Since the vibration information contains the vibration of the molar teeth themselves, molar teeth can be detected from the vibration information. Since molar teeth are detected by adding the signs that occur before molar teeth, that is, sign information, to the vibration information, the reliability of the vibration in the vibration information being caused by molar teeth can be improved by using the sign information. Therefore, the burden on the user can be reduced, and molar teeth can be detected with high precision.

[0479] Generally speaking, there are few means that can be used in daily life to measure molar teeth. The molar detection system 601 can detect the presence or absence of molar teeth and the frequency of molar teeth in daily life by detecting the vibration peculiar to molar teeth in a non-contact manner.

[0480] The computer that executes the molar detection application program 640 detects molar teeth from the vibration information indicating the vibration of the user and the sign information indicating the signs of molar teeth that occur before molar teeth. Since the vibration information contains the vibration of the molar teeth themselves, molar teeth can be detected from the vibration information. Since molar teeth are detected by adding sign information to the vibration information, the reliability of the vibration in the vibration information being caused by molar teeth can be improved by using the sign information. Therefore, molar teeth can be detected with high precision.

[0481] The detection unit 641 extracts heartbeat information indicating the heartbeat of the user from the vibration information, and the heartbeat sign extraction unit 6431 included in the detection unit 641 extracts an increase in the heart rate that occurs before molar teeth as sign information from the heartbeat information. Before the user grinds their teeth, the user's heart rate will increase. When the heartbeat sign extraction unit 6431 extracts an increase in the heart rate that occurs before molar teeth as sign information, the reliability of the vibration in the vibration information being caused by molar teeth can be improved by using the increase in the heart rate.

[0482] The detection unit 641 extracts autonomic nerve information indicating the state of the user's autonomic nerves from the vibration information, and the autonomic nerve symptom extraction unit 6432 included in the detection unit 641 extracts an increase in the sympathetic nerve index that occurs before bruxism as symptom information from the autonomic nerve information. Before the user performs bruxism, the user's sympathetic nerves become hyperactive. Since the autonomic nerve symptom extraction unit 6432 extracts an increase in the sympathetic nerve index that occurs before bruxism as symptom information, the reliability of the detected vibration being caused by bruxism can be improved.

[0483] The waveform detection unit 642 included in the detection unit 641 detects the vibration information in the form of a waveform, and the bruxism determination unit 644 confirms bruxism by comparing the detected waveform with a pre - memorized bruxism waveform. In this case, it is possible to pre - detect and confirm whether the detected waveform is bruxism by using the bruxism waveform detected with high precision from the vibration information and the symptom information. Therefore, the reliability of the detected waveform being caused by bruxism can be improved.

[0484] As described above, specific examples of the health risk determination system, autonomic nerve determination system, life improvement system, sleep posture determination system, sleep posture determination program, bruxism detection system, and bruxism detection program of the present invention have been described. However, the configurations and functions of each of the health risk determination system, autonomic nerve determination system, life improvement system, sleep posture determination system, sleep posture determination program, bruxism detection system, and bruxism detection program of the present invention are not limited to the foregoing specific examples, but can be further changed within the scope of the gist described in the technical solution.

[0485] The present invention can combine a part of the health risk determination system, autonomic nerve determination system, life improvement system, sleep posture determination system, sleep posture determination program, bruxism detection system, and bruxism detection program of the foregoing specific examples with the remaining parts of the health risk determination system, autonomic nerve determination system, life improvement system, sleep posture determination system, sleep posture determination program, bruxism detection system, and bruxism detection program of the foregoing specific examples. The health risk determination system, autonomic nerve determination system, life improvement system, sleep posture determination system, sleep posture determination program, bruxism detection system, and bruxism detection program of the foregoing specific examples can also be combined with each other. Part or all of the above - mentioned forms can be represented by (Supplementary Note 1) to (Supplementary Note 28) described below, but are not limited to the following description.

[0486] (Supplementary Note 1)

[0487] A health risk determination system for determining the health risk caused by the abnormal breathing state of a user from the time of falling asleep to the time of waking up, the health risk determination system includes

[0488] A sensor unit that obtains information indicating the user's respiration, i.e., respiration information, without contacting the user.

[0489] An abnormality detection unit that detects the abnormal respiration state of the user from the respiration information.

[0490] A per-unit-time acquisition unit that acquires the detection count of the abnormal respiration state for each unit time band having a predetermined time length between the falling asleep time and the waking up time.

[0491] An average count acquisition unit that acquires an average detection count, which is a value obtained by dividing the total value of the detection counts of the abnormal respiration state between the falling asleep time and the waking up time by the time between the falling asleep time and the waking up time.

[0492] A maximum count acquisition unit that acquires a maximum detection count, which is the maximum value among the multiple detection counts obtained for each unit time band; and

[0493] A health risk determination unit that determines the health risk of the user from both the average detection count and the maximum detection count.

[0494] (Supplementary Note 2)

[0495] In the health risk determination system described in Supplementary Note 1, the sensor unit acquires information indicating the user's body movement, i.e., body movement information, and information indicating the user's heartbeat, i.e., heartbeat information.

[0496] The abnormality detection unit detects the abnormal respiration state from the body movement information and the heartbeat information.

[0497] (Supplementary Note 3)

[0498] In the health risk determination system described in Supplementary Note 2, a sleep stage acquisition unit is further provided, which determines the sleep stage of the user from the respiration information, the body movement information, and the heartbeat information; and

[0499] The health risk determination unit determines the health risk of the user from the detection result of the abnormality detection unit and the sleep stage.

[0500] (Supplementary Note 4)

[0501] In the health risk determination system described in Supplementary Note 3, the respiration information includes a signal indicating the user's body movement with respiration.

[0502] The health risk determination system further includes: a first amplifier and a second amplifier that amplify the signal and output the amplified signal to the sleep stage acquisition unit.

[0503] The gain of the first amplifier is greater than the gain of the second amplifier.

[0504] (Appendix 5)

[0505] An autonomic nerve determination system is an autonomic nerve determination system that determines the autonomic nerve of a user from the time of going to bed to the time of waking up. The autonomic nerve determination system includes:

[0506] A sensor unit that acquires at least one of information indicating the respiration of the user, i.e., respiration information, information indicating the body movement of the user, i.e., body movement information, information indicating the brain wave of the user, i.e., brain wave information, and information indicating the heartbeat of the user, i.e., heartbeat information;

[0507] An autonomic nerve acquisition unit that acquires information indicating the state of the autonomic nerve of the user, i.e., autonomic nerve information, from the heartbeat information;

[0508] A sleep state acquisition unit that acquires the sleep state of the user from at least one of the respiration information, the body movement information, the heartbeat information, and the brain wave information; and

[0509] An autonomic nerve determination unit that determines the autonomic nerve of the user from both the autonomic nerve information and the sleep state.

[0510] (Appendix 6)

[0511] In the autonomic nerve determination system described in Appendix 5, it further includes: a time zone determination unit that determines a detection target time zone during which the body movement of the user does not occur between the time of going to bed and the time of waking up from the body movement information; and

[0512] The autonomic nerve determination unit determines the autonomic nerve of the user in the detection target time zone.

[0513] (Appendix 7)

[0514] In the autonomic nerve determination system described in Appendix 5 or Appendix 6, it further includes an instrument control unit that controls the operation of an instrument that constitutes the surrounding environment of the user using the determination result of the autonomic nerve determination unit.

[0515] (Appendix 8)

[0516] In the autonomic nerve determination system described in any one of Supplementary Notes 5 to 7, it further includes: a support information acquisition unit that acquires information for supporting the user's life, that is, support information, using the determination result of the autonomic nerve determination unit; and

[0517] a display unit that displays the support information.

[0518] (Supplementary Note 9)

[0519] A life improvement system is a life improvement system for improving the user's life, and this life improvement system includes:

[0520] a sensor unit that acquires at least one of information indicating the user's respiration, that is, respiration information, information indicating the user's body movement, that is, body movement information, and information indicating the user's heartbeat, that is, heartbeat information;

[0521] a sleep state acquisition unit that acquires information indicating the user's sleep state, that is, sleep state information, from at least one of the respiration information, the body movement information, and the heartbeat information;

[0522] an autonomic nerve acquisition unit that acquires information indicating the state of the user's autonomic nerve, that is, autonomic nerve information, from the heartbeat information;

[0523] an instrument control unit that controls the operation of an instrument that constitutes the environment around the user according to at least one of the user's past sleep state information, that is, past sleep information, and the autonomic nerve information stored in advance;

[0524] a prediction information generation unit that generates information indicating the predicted state of the user while awake, that is, prediction information, according to at least one of the past sleep information and the autonomic nerve information; and

[0525] an improvement information generation unit that generates information for improving the user's life, that is, improvement information, according to the prediction information.

[0526] (Supplementary Note 10)

[0527] In the life improvement system described in Supplementary Note 9, the sensor unit includes a pad sensor installed on the pad on which the user sits.

[0528] (Supplementary Note 11)

[0529] In the life improvement system described in Supplementary Note 9 or Supplementary Note 10, the prediction information includes information indicating the user's mental state, that is, mental information, information indicating the user's physical condition, that is, physical condition information, and information indicating the user's mental state, that is, mental information.

[0530] (Supplementary Note 12)

[0531] In the life improvement system described in Supplementary Note 11, the physical condition information includes skin information indicating t...

Claims

1. A health risk determination system is a health risk determination system for determining the health risks caused by the abnormal breathing state of a user from the time of falling asleep to the time of waking up. The health risk determination system includes: A sensor unit that obtains information indicating the breathing of the user, i.e., breathing information, in a non-contact manner with the user; An abnormality detection unit that detects the abnormal breathing state of the user from the breathing information; A unit - time - count acquisition unit that acquires the detection count of the abnormal breathing state for each unit time band having a predetermined time length between the time of falling asleep and the time of waking up; An average - count acquisition unit that acquires an average detection count, which is a value obtained by dividing the total value of the detection counts of the abnormal breathing state between the time of falling asleep and the time of waking up by the time between the time of falling asleep and the time of waking up; A maximum - count acquisition unit that acquires a maximum detection count, which is the maximum value among the multiple detection counts obtained for each unit time band; And A health risk determination unit that determines the health risks of the user from both the average detection count and the maximum detection count.

2. An autonomic nerve determination system is an autonomic nerve determination system for determining the autonomic nerve of a user from the time of going to bed to the time of getting up. The autonomic nerve determination system includes: A sensor unit that acquires at least one of information indicating the breathing of the user, i.e., breathing information, information indicating the body movement of the user, i.e., body movement information, information indicating the brain wave of the user, i.e., brain wave information, and information indicating the heartbeat of the user, i.e., heartbeat information; An autonomic - nerve acquisition unit that acquires information indicating the state of the autonomic nerve of the user, i.e., autonomic nerve information, from the heartbeat information; A sleep - state acquisition unit that acquires the sleep state of the user from at least one of the breathing information, the body movement information, the heartbeat information, and the brain wave information; And An autonomic - nerve determination unit that determines the autonomic nerve of the user from both the autonomic nerve information and the sleep state.

3. A life improvement system is a life improvement system for improving the life of a user. The life improvement system includes: A sensor unit that acquires at least one of information indicating the breathing of the user, i.e., breathing information, information indicating the body movement of the user, i.e., body movement information, and information indicating the heartbeat of the user, i.e., heartbeat information; A sleep - state acquisition unit that acquires information indicating the sleep state of the user, i.e., sleep state information, from at least one of the breathing information, the body movement information, and the heartbeat information; An autonomic - nerve acquisition unit that acquires information indicating the state of the autonomic nerve of the user, i.e., autonomic nerve information, from the heartbeat information; An instrument control unit that controls the operation of the instruments constituting the environment around the user according to at least one of the past sleep state information, i.e., past sleep information, and the autonomic nerve information of the user pre - memorized. A prediction information generation unit generates information indicating a predicted state of the user during wakefulness, i.e., prediction information, based on at least one of the past sleep information and the autonomic nerve information. And An improvement information generation unit generates information for improving the user's life, i.e., improvement information, based on the prediction information.

4. A life improvement system for improving the user's life, the life improvement system comprising: A sensor unit that obtains information indicating the user's heart rate, i.e., heart rate information; An autonomic nerve acquisition unit that obtains information indicating the state of the user's autonomic nerve, i.e., autonomic nerve information, from the heart rate information; A prediction information generation unit that generates information indicating a predicted state of the user during wakefulness, i.e., prediction information, based on the autonomic nerve information; and An improvement information generation unit that generates information for improving the user's life, i.e., improvement information, based on the prediction information.

5. A sleeping posture determination system comprising: A sensor unit mounted on a bedding, which bears the load of the user's body on the bedding and outputs a waveform corresponding to the load; A sleeping posture determination unit that determines the user's sleeping posture from the waveform output by the sensor unit; and A recommendation generation unit that generates a recommendation for improving the user's sleeping posture based on the sleeping posture determined by the sleeping posture determination unit.

6. A sleeping posture determination program for causing a computer to execute the following steps: A step of determining the user's sleeping posture from a waveform indicating the load of the user on the bedding output by a sensor unit mounted on the bedding; and A step of generating a recommendation for improving the user's sleeping posture based on the sleeping posture determined in the determining step.

7. A bruxism detection system for detecting a user's bruxism from the time of falling asleep to the time of waking up, the bruxism detection system comprising: A sensor unit that obtains vibration information indicating the vibration of the user without contacting the user; and A detection unit that extracts symptom information indicating a symptom occurring before the bruxism from the vibration information, and detects the bruxism from the vibration information and the symptom information.

8. A bruxism detection program for causing a computer to execute the following steps: A step of extracting symptom information indicating a symptom occurring before bruxism from vibration information indicating the vibration of the user obtained by a sensor unit; and A step of detecting the bruxism from the vibration information and the symptom information.

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