Sleep improvement device and sleep improvement method

Abstract

To provide a sleep improvement device with a simple and low cost configuration, for providing a sleep improvement plan via life improvement suitable for an individual for improving a sleep state to enable contribution to improvement of a sleep state on the basis of the sleep improvement plan.SOLUTION: A sleep improvement device comprises: an autonomic nerve component calculation unit 12 for calculating an energy value of at least a very low frequency component of a user's autonomic nerve components; a sleep evaluation prediction model expression construction unit 6 for constructing a sleep evaluation prediction model expression unique to the user on the basis of a model construction sleep evaluation index calculated on the basis of a time at each sleep stage of the user and an energy value of the very low frequency component at least before getting into bed calculated by the autonomic nerve component calculation unit 12; and a sleep improvement plan proposition unit 7 for proposing a sleep improvement plan via life improvement on the basis of the constructed sleep evaluation prediction model expression and an energy value of at least the very low frequency component calculated thereafter.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a sleep improvement device and a sleep improvement method that contribute to improving a user's sleep state by providing a sleep improvement plan based on lifestyle improvements tailored to the user in order to improve the user's sleep state. [Background technology]

[0002] It is said that sleep is a barometer of health, and if you can wake up feeling good after a comfortable sleep, you will feel refreshed and healthy, as we often experience in our daily lives. On the other hand, if you suffer from insomnia or have a tendency to be unable to sleep, or if you are forced to sleep in a way that reverses your day-night routine due to late-night work, you will often not feel good after waking up. In other words, whether consciously or unconsciously, the state of sleep affects your mood and behavior when you wake up, and ultimately determines the quality of your daytime activities after waking up. In this way, sleep is an important factor that has an important effect on human physical and mental activities, and it can be said that if you can get good sleep, you can ensure healthy daily activities both physically and mentally.

[0003] It is said that sleep reflects the daytime living conditions, but since physical fatigue and psychological tension have different characteristics depending on the individual, it has been thought that it is difficult to predict sleep evaluation. Therefore, doctors mainly treat insomnia by administering sleep-inducing drugs, and various technologies related to sleep-inducing drugs have been proposed, for example, as in Patent Document 1. In addition, attempts have been made to control the environmental temperature during sleep in order to ensure comfortable sleep. As a technology that embodies such an attempt, for example, as described in Patent Documents 2 to 4, a technology has been proposed that determines the depth of sleep and the sleep state based on a biological signal, and controls the sleep environmental temperature according to the determined depth of sleep and the sleep state. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2018-044006 A [Patent Document 2] JP 2008-119454 A [Patent Document 3] JP 2009-247846 A [Patent Document 4] JP 2006-198023 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the long-term administration of sleep-inducing drugs has become a problem because of its adverse effects, such as the promotion of dementia. Therefore, ideally, it would be desirable to eliminate insomnia by improving one's lifestyle, and the correct approach to solving insomnia would be to increase physical activity and improve bodily functions, just like with lifestyle-related diseases.

[0006] The present invention has been made in consideration of the above-mentioned situation, and aims to provide a sleep improvement device and a sleep improvement method that provide an index tailored to an individual for improving sleep states using a simple and low-cost configuration, and that can contribute to improving sleep states based on this index. [Means for solving the problem]

[0007] The sleep improvement device of the present invention, which achieves the above-mentioned object, contributes to improving a user's sleep state by providing a sleep improvement plan through lifestyle improvements suited to the user in order to improve the user's sleep state, and includes heart rate signal measuring means for measuring the user's heart rate signal, autonomic nerve component calculating means for calculating an energy value of at least a very low frequency component (VLF) of 0.003 to 0.04 Hz of the user's autonomic nerve components based on the heart rate signal measured by the heart rate signal measuring means, biosignal detecting means for detecting the user's biosignal, sleep stage determining means for determining each sleep stage of the user during sleep based on the biosignal detected by the biosignal detecting means and calculating a time for each sleep stage, and a sleep evaluation index for model construction based on the time for each sleep stage determined by the sleep stage determining means. the sleep evaluation index calculation means for calculating a sleep evaluation index for a user based on the sleep evaluation index for model construction calculated by the sleep evaluation index calculation means and the energy value of at least the very low frequency component (VLF) before going to bed calculated by the autonomic nerve component calculation means; and a sleep evaluation prediction model equation construction means for constructing a sleep evaluation prediction model equation specific to the user based on the sleep evaluation prediction model equation constructed by the sleep evaluation prediction model equation construction means and the energy value of at least the very low frequency component (VLF) thereafter calculated by the autonomic nerve component calculation means, and for proposing a sleep improvement plan proposal for a user to improve lifestyle so as to increase the energy value of at least the very low frequency component (VLF) to a certain value or above based on the calculated predicted sleep evaluation index.

[0008] Furthermore, the sleep improvement method according to the present invention for achieving the above-mentioned object contributes to improving a sleep state by providing a sleep improvement plan through lifestyle improvements suited to the user in order to improve the sleep state, and includes a heartbeat signal measuring step of measuring the heartbeat signal of the user by a predetermined heartbeat signal measuring means, an autonomic nerve component calculating step of calculating an energy value of at least a very low frequency component (VLF) of 0.003 to 0.04 Hz of the autonomic nerve components of the user based on the heartbeat signal measured in the heartbeat signal measuring step by a processor performing signal processing, a biosignal detecting step of detecting the biosignal of the user by a predetermined biosignal detecting means, a sleep stage determining step of determining each sleep stage of the user during sleep based on the biosignal detected in the biosignal detecting step by the processor and determining the time for each sleep stage, and a sleep stage determining step of determining each sleep stage determined in the sleep stage determining step by the processor. The method includes a sleep evaluation index calculation step of calculating a sleep evaluation index for model construction based on the time of each sleep stage; a sleep evaluation prediction model equation construction step in which the processor constructs a sleep evaluation prediction model equation specific to the user based on the sleep evaluation index for model construction calculated in the sleep evaluation index calculation step and on the energy value of at least the very low frequency component (VLF) before going to bed calculated in the autonomic nerve component calculation step; and a sleep improvement plan proposing step in which the processor calculates a predicted sleep evaluation index at that time based on the sleep evaluation prediction model equation constructed in the sleep evaluation prediction model equation construction step and on the energy value of at least the very low frequency component (VLF) calculated in the autonomic nerve component calculation step thereafter, and proposes a sleep improvement plan by making lifestyle improvements such as to make the energy value of at least the very low frequency component (VLF) equal to or greater than a certain value based on the calculated predicted sleep evaluation index.

[0009] The sleep improvement device and sleep improvement method of the present invention construct a sleep evaluation prediction model equation specific to the user based on a sleep evaluation index for model construction based on sleep stage time and the very low frequency component (VLF) of the autonomic nerve components, calculates a predicted sleep evaluation index at that time based on this sleep evaluation prediction model equation, and proposes a sleep improvement proposal through lifestyle modifications that will raise at least the energy value of the very low frequency component (VLF) to a certain value or above based on the calculated predicted sleep evaluation index. Effect of the Invention

[0010] The present invention can provide sleep improvement suggestions based on lifestyle modifications tailored to individuals to improve sleep conditions, with a simple and low-cost configuration, and can contribute to improving sleep conditions based on these sleep improvement suggestions. [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram showing a configuration of a sleep improvement device according to an embodiment of the present invention. [Diagram 2] 1 is a diagram showing the configuration of a sleep improvement device according to an embodiment of the present invention, and is a partial cross-sectional view taken in the direction indicated by the arrow in FIG. [Diagram 3] FIG. 13 is a diagram showing an example of a comparison between the measurement results of autonomic nerve components obtained from a heart rate signal before going to bed and sleep evaluation indexes after going to bed. [Figure 4] FIG. 5 is a diagram showing cross-correlation coefficients between autonomic nerve components and sleep evaluation indexes in the example shown in FIG. [Diagram 5] FIG. 13 is a diagram showing the configuration of another biological signal detection unit. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, specific embodiments to which the present invention is applied will be described in detail with reference to the drawings.

[0013] This embodiment is a sleep improvement device that provides a sleep improvement proposal for improving a user's sleep state by making lifestyle improvements suited to the individual user, based on the user's biosignals.

[0014] Fig. 1 shows a configuration in which the processing of a sleep improvement device according to an embodiment of the present invention is represented as blocks, and Fig. 2 shows a partial cross-sectional view as seen from the direction of the arrow in Fig. 1. That is, the sleep improvement device includes a biosignal detection unit 1 that detects a biosignal of a user lying on a bed 21, a signal amplification unit 2 that amplifies the biosignal detected by the biosignal detection unit 1, a filter unit 3 that performs filtering processing on the biosignal amplified by the signal amplification unit 2, a sleep stage determination unit 4 that determines a sleep stage based on the biosignal passed through the filter unit 3, a sleep evaluation index calculation unit 5 that calculates a sleep evaluation index for model construction (to be described later) based on the time of each sleep stage determined by the sleep stage determination unit 4, a sleep evaluation prediction model equation construction unit 6 that constructs a sleep evaluation prediction model equation specific to the user based on the sleep evaluation index for model construction calculated by the sleep evaluation index calculation unit, and a sleep improvement plan proposal unit 7 that proposes a sleep improvement plan by lifestyle improvement based on the sleep evaluation prediction model equation constructed by the sleep evaluation prediction model equation construction unit 6. The sleep improvement device also includes a heart rate measurement unit 11 that measures the user's heart rate signal, and an autonomic nerve component calculation unit 12 that calculates an autonomic nerve component based on the heart rate signal measured by the heart rate measurement unit 11. Of these units, at least the sleep stage determination unit 4, sleep evaluation index calculation unit 5, sleep evaluation prediction model formula construction unit 6, sleep improvement plan proposal unit 7, and autonomic nerve component measurement unit 12 can be implemented as a program that can be executed using hardware such as a CPU (Central Processing Unit) or memory in a computer that performs signal processing, or can be implemented using a dedicated processor such as a DSP (Digital Processing Unit) mounted on an expansion board that can be attached to a computer. The sleep improvement device may also be configured such that the biosignal detection unit 1, the signal amplification unit 2, and the filter unit 3 are configured as a single bed-type device, and data measured from this bed-type device is transmitted to a cloud server, whereby the cloud server performs processing of the sleep stage determination unit 4, the sleep evaluation index calculation unit 5, the sleep evaluation prediction model formula construction unit 6, and the sleep improvement plan proposal unit 7.

[0015] The biosignal detection unit 1 is a non-invasive and non-constraint sensor that detects minute biosignals of the user. Specifically, the biosignal detection unit 1 is composed of a pressure detection tube 1a and a minute differential pressure sensor 1b that detects minute pressure fluctuations of the air contained inside the pressure detection tube 1a, and constitutes a non-invasive and non-constraint detection means for the biosignal.

[0016] The pressure detection tube 1a has an appropriate elasticity so that the internal pressure varies in accordance with the pressure variation range of the biosignal. In addition, the volume of the hollow part of the pressure detection tube 1a must be appropriately selected in order to transmit the pressure variation to the micro-differential pressure sensor 1b with an appropriate response speed. If the pressure detection tube 1a cannot simultaneously satisfy both the appropriate elasticity and the hollow part volume, a core wire of an appropriate thickness can be loaded into the hollow part of the pressure detection tube 1a over the entire length of the tube to obtain an appropriate volume of the hollow part.

[0017] Such a pressure detection tube 1a is placed on a hard sheet 22 laid on a bed 21. In the sleep stage determination device, an elastic cushion sheet 23 is laid on the hard sheet 22, and the user lies on the pressure detection tube 1a. The pressure detection tube 1a may be incorporated into the cushion sheet 23 or the like to stabilize the position of the pressure detection tube 1a.

[0018] The micro-differential pressure sensor 1b is a sensor that detects minute pressure fluctuations. In this embodiment, a low-frequency condenser microphone type is used as the micro-differential pressure sensor 1b, but it is not limited to this, and any type having appropriate resolution and dynamic range may be used. The low-frequency condenser microphone used in this embodiment is a type that has been significantly improved in the low-frequency region by providing a chamber behind the pressure receiving surface, in contrast to general acoustic microphones that do not take into consideration the low-frequency region, and is suitable for detecting minute pressure fluctuations in the pressure detection tube 1a. In addition, this condenser microphone is excellent in measuring minute differential pressures, has a resolution of 0.2 Pa and a dynamic range of about 50 Pa, and has several times the performance of a micro-differential pressure sensor that uses ceramics that is normally used, and is suitable for detecting minute pressures applied to the pressure detection tube 1a through a biological signal passing through the body surface, and can detect minute body movements with high sensitivity.

[0019] In this embodiment, two sets of pressure detection tubes 1a are provided, one for detecting the biosignals of the user's chest area and the other for detecting the user's buttocks area, and configured to detect biosignals regardless of the user's sleeping posture. Note that the sleep improvement device may be configured to place the pressure detection tubes 1a only in either the chest area or the buttocks area. The biosignals detected by such a biosignal detection unit 1 are supplied to a signal amplification unit 2. The sleep improvement device can be easily used in daily life by being configured to detect biosignals in such a non-invasive and unrestrained manner, and is particularly suitable for use by the elderly.

[0020] The signal amplifier 2 amplifies the signal detected by the biosignal detector 1 so that it can be processed in a later processing step, and further performs appropriate signal shaping processing by removing signals of obviously abnormal levels, etc. The biosignal amplified by the signal amplifier 2 is supplied to the filter unit 3.

[0021] The filter unit 3 extracts the heartbeat signal by removing unnecessary signals from the biosignal amplified by the signal amplifier unit 2 using a bandpass filter or the like. That is, the biosignal detected by the biosignal detector unit 1 is a signal in which various vibrations emitted from the human body are mixed together, and in addition to the heartbeat signal, various signals such as body movement signals due to turning over in bed are included. Of these, the heartbeat signal is a vibration that is caused by pressure changes (i.e., blood pressure) based on the pumping function of the heart and is included in the biosignal. In the sleep improvement device, this is extracted by the filter unit 3 and recognized as a heartbeat signal. The heartbeat signal that has passed through the filter unit 3 is supplied to the sleep stage determination unit 4. The sampling period of the heartbeat signal is set to 4 milliseconds.

[0022] The sleep stage determination unit 4 determines the sleep stage (awake, REM sleep, light sleep, deep sleep) according to the international sleep depth determination standard using so-called polysomnography (PSG). Specifically, the sleep stage determination unit 4 employs a method described in, for example, Japanese Patent Application Laid-Open No. 2016-022276, Japanese Patent Application Laid-Open No. 2016-202463, Japanese Patent Application Laid-Open No. 2018-029772, etc., by the present applicant, to determine the sleep stage of the sleeping user, that is, the type of the wakefulness stage, REM sleep stage, light sleep stage, and deep sleep stage. The sleep stage determination unit 4 calculates the time of each determined sleep stage and supplies information about the time to the sleep evaluation index calculation unit 5.

[0023] In order to construct a user-specific sleep evaluation prediction model equation (described later), the sleep evaluation index calculation unit 5 calculates sleep evaluation indexes for model construction based on the time information of each sleep stage determined by the sleep stage determination unit 4. The sleep evaluation index calculation unit 5 supplies the calculated sleep evaluation indexes for model construction to the sleep evaluation prediction model equation construction unit 6.

[0024] The sleep evaluation prediction model equation constructing unit 6 constructs a sleep evaluation prediction model equation specific to the user based on the autonomic nerve components calculated by the autonomic nerve component calculating unit 12, particularly the energy value of the very low frequency components (VLF) of 0.003 to 0.04 Hz. The details of this will be described later.

[0025] The sleep improvement plan proposing unit 7 outputs a sleep improvement plan by lifestyle improvement suited to the user to improve the sleep state based on the autonomic nerve components calculated by the autonomic nerve component calculating unit 12, particularly the energy value of the very low frequency components (VLF) of 0.003 to 0.04 Hz, and the sleep evaluation prediction model formula constructed by the sleep evaluation prediction model formula constructing unit 6. Specifically, the sleep improvement plan proposing unit 7 presents a sleep improvement plan for obtaining good sleep by improving the daytime lifestyle so as to make the energy value of the very low frequency components (VLF) equal to or greater than a certain value. Note that the energy value of the very low frequency components (VLF) is extremely reduced by emotionality or stress, so that the sleep improvement plan proposing unit 7 proposes a lifestyle that makes the energy value of the very low frequency components (VLF) equal to or greater than a certain value as a sleep improvement plan, taking into consideration phenomena that have influenced the very low frequency components (VLF) based on the record of the user's daily life recorded using the so-called VAS method, including psychological and physical aspects. The sleep improvement plan proposing section 7 outputs the sleep improvement plan, and causes it to be displayed on a display device (not shown), printed by a printing device, or stored as data in a storage device.

[0026] The heart rate measuring unit 11 measures the heart rate during the day (when waking up), particularly before going to bed, and measures the user's heart rate signal (pulse wave) using, for example, an existing heart rate measuring device in which a sensor is inserted into a fingertip or a wearable sensor such as a wristwatch type. The heart rate measuring unit 11 supplies the measured heart rate signal to the autonomic nerve component calculating unit 12.

[0027] The autonomic nerve component calculation unit 12 calculates the autonomic nerve component based on the heartbeat signal measured by the heartbeat measurement unit 11. The heartbeat measurement unit 11 and the autonomic nerve component calculation unit 12 are usually configured as devices separate from the bed-type device, and from the viewpoint of ease of handling, it is particularly preferable to configure them as wearable-type sensors. Such a wearable-type sensor can transmit the heartbeat signal or information on the autonomic nerve component calculated therefrom to the outside via wireless communication such as Bluetooth (registered trademark). More specifically, the wearable-type sensor can transmit the heartbeat signal or information on the autonomic nerve component calculated therefrom to a cloud server or the like constituting the sleep evaluation prediction model formula construction unit 6 or the sleep improvement suggestion unit 7 via wireless communication. The wearable-type sensor may also transmit the heartbeat signal or information on the autonomic nerve component calculated therefrom to a mobile terminal carried by the user, and then transmit the information from the mobile terminal to a cloud server or the like. The autonomic nerve component calculation unit 12 can calculate the energy values ​​of the sympathetic component (LF), the parasympathetic component (HF), and the very low frequency component (VLF) of 0.003 to 0.04 Hz as the autonomic nerve components, and the autonomic nerve component total power (TP) which is the sum of these, but it is sufficient to calculate at least the energy value of the very low frequency component (VLF). The autonomic nerve components calculated by this autonomic nerve component calculation unit 12 are supplied to the sleep evaluation prediction model formula construction unit 6 and the sleep improvement suggestion unit 7.

[0028] Such a sleep improvement device calculates sleep evaluation indexes based on the following principle.

[0029] The sleep improvement device constructs a sleep evaluation prediction model formula specific to the user based on the user's daytime living conditions and performance data of sleep evaluation indexes, and presents sleep improvement suggestions that will enable better sleep by improving the user's daytime lifestyle.

[0030] Current sleep evaluation is based on the proportion of time spent in sleep stages (awake, REM sleep, light sleep, deep sleep) according to the international sleep depth assessment standard using so-called polysomnography (PSG). Specifically, when weighting coefficients α=20, β=8, and γ=1, the sleep evaluation index is calculated as follows: sleep evaluation index=20×deep sleep time+6×light sleep time+REM sleep time. In the sleep improvement device, biosignals are detected non-invasively and unrestrainedly, and the time for each sleep stage is calculated by the sleep stage determination unit 4, and the sleep evaluation index for model construction is calculated by the sleep evaluation index calculation unit 5.

[0031] Next, in the sleep improvement device, the sleep evaluation prediction model equation construction unit 6 constructs a sleep evaluation prediction model equation specific to the user using the quantified daytime activity amount of the user. Specifically, as described above, the sleep evaluation prediction model equation construction unit 6 uses the autonomic nerve components quantified by the autonomic nerve component calculation unit 12. Such quantification of the autonomic nerve components is based on the following hypothesis.

[0032] It can be assumed that the sleep evaluation index is proportional to the daytime physical fatigue level, mental fatigue level, and the previous day's sleep condition. It can also be assumed that there is a relationship between the sleep evaluation index and the VAS (record of daily life), which is a sensory index of the user's sleep quality. In particular, when the amount of sustained activity is large, the energy values ​​of the above-mentioned autonomic nerve component total power (TP) and very low frequency component (VLF) (0.003 to 0.04 Hz, 5 minutes to 25 seconds) become large, and the sleep evaluation index becomes large, which indicates that the user can sleep well. Since the amount of sustained activity changes depending on the content and method of exercise, the very low frequency component (VLF) may become an improvement indicator by finding the content of exercise that suits the individual user.

[0033] In addition, it is known that when autonomic nerve disorders occur, the balance of the autonomic nerves shifts to sympathetic dominance, accelerating degeneration of the cardiovascular system, especially in the elderly. The decrease in heart rate variability is thought to be due to increased sympathetic tone and decreased parasympathetic tone. This is associated with mortality from heart failure, coronary artery disease, and acute myocardial infarction. The evaluation of total heart rate variability can be performed by determining the autonomic nerve components through power spectrum analysis of the frequency components of the periodic fluctuation of the heart rate. Autonomic nerve disorders are mainly improved by exercise. Diabetes is a typical example that affects blood vessels. Such autonomic nerves also affect the state of sleep, and when autonomic nerve disorders occur, the balance of the autonomic nerves shifts to sympathetic dominance, the tension components increase, and sleep becomes difficult. This is a phenomenon commonly seen in the elderly.

[0034] Furthermore, the very low frequency component (VLF) reflects vasomotor activity, the renin-angiotensin system (hormonal system), and thermoregulation. Here, when you enter deep sleep, your body temperature drops. If the energy value of the very low frequency component (VLF) is small, thermoregulation does not function, which can be assumed to cause poor sleep. In addition, for the sympathetic component (LF), the intermediate frequency component (0.05-0.20Hz) is a tension component that reflects the baroreceptor system. In addition, for the parasympathetic component (HF), the high frequency component (0.20-0.35Hz) is a relaxation component that reflects respiratory fluctuations. Therefore, the more the parasympathetic component (HF) is dominant, that is, the larger the parasympathetic component (HF) / sympathetic component (LF), the more relaxed the body will be and the better the sleep will be. Furthermore, the total power (TP) of the autonomic nerve components is the energy value of the very low frequency component (VLF) + sympathetic component (LF) + parasympathetic component (HF), and the larger this is, the greater the stress resistance will be. Therefore, the greater the total autonomic nerve power (TP), the better the sleep.

[0035] Therefore, good sleep is when the energy value of the very low frequency component (VLF) and the total power of the autonomic nervous component (TP) before going to bed are high, and it can be said that finding exercises and lifestyle changes in daily life that increase these values ​​above a certain level will lead to improved sleep.

[0036] To verify these hypotheses, Figure 3 shows an example where the measurement results of the autonomic nerve components obtained from the heart rate signal before going to bed for a certain subject are compared with the sleep evaluation index after going to bed. The measurements were performed using the sleep improvement device shown in Figure 1, and the cross-correlation coefficients between the autonomic nerve components and the sleep evaluation index were calculated, as shown in Figure 4. This shows that, among the autonomic nerve components, the energy value of the very low frequency component (VLF) in particular has a high correlation with the sleep evaluation index. From this correlation, a sleep evaluation prediction model equation for the subject was constructed, giving the result y = 0.0204x + 27.2. Here, y is the sleep evaluation index, and x is the energy value (mS 2 It goes without saying that this sleep evaluation prediction model formula will differ depending on the user.

[0037] In the sleep improvement device, a sleep evaluation prediction model equation is constructed based on such verification results, sleep evaluation indices for model construction are derived, and a sleep evaluation prediction model equation is constructed based on the obtained sleep evaluation indices for model construction.

[0038] Specifically, in the sleep improvement device, the autonomic nerve component calculation unit 12 calculates the autonomic nerve components of the user before going to bed for, for example, 10 days. At this time, the type of daily life such as the exercise content when the energy value of the very low frequency component (VLF) is large is recorded separately. For example, the exercise content varies depending on the individual, but may include exercise that stimulates blood vessels such as stretching and running. In addition, in the sleep improvement device, the sleep evaluation index calculation unit 5 calculates the sleep evaluation index for model construction based on the time of each sleep stage for, for example, 10 days in the same manner. Then, in the sleep improvement device, the sleep evaluation prediction model formula construction unit 6 constructs a sleep evaluation prediction model formula based on the obtained data.

[0039] In order to make the sleep evaluation prediction model formula an ideal prediction model formula that is not dependent on the user's physical problems or environmental conditions such as the bedroom, it is desirable not to perform measurements for constructing the prediction model formula on days on which the following phenomena occur: In other words, in a sleep improvement device, it is desirable to exclude from the measurement subjects the following cases: 1) when a physical problem occurs, such as sudden leg cramps or stomach pain during the night, 2) when the sleeping environment deteriorates, such as when the user's bedding is inappropriate and the user's body gets cold or when the user cannot sleep due to surrounding noise, 3) when the user lies in bed lazily even though he or she is not sleepy, and 4) when the user becomes emotionally excited after measuring the energy value of the very low frequency component (VLF) such as when he or she has an argument with another person (in this case, the very low frequency component (VLF) drops extremely).

[0040] In the sleep improvement device, after constructing the sleep evaluation prediction model formula in this way, the energy value of the very low frequency component (VLF) is measured and calculated using the autonomic nerve component calculation unit 12 twice thereafter, for example in the evening and before going to bed, and the sleep improvement plan proposal unit 7 applies the energy value of the very low frequency component (VLF) to the sleep evaluation prediction model formula to calculate a predicted sleep evaluation index at that time. Then, in the sleep improvement device, the sleep improvement plan proposal unit 7 proposes a sleep improvement plan based on the calculated predicted sleep evaluation index. For example, the sleep improvement plan proposal unit 7 grasps in advance a sleep evaluation index of a certain value or more at which the user sleeps well, and when the predicted sleep evaluation index based on the energy value of the very low frequency component (VLF) obtained from the sleep evaluation prediction model formula differs from the previously grasped sleep evaluation index by a certain value or more, the sleep improvement plan proposal unit 7 proposes a predetermined exercise, bathing, meditation for psychological improvement, etc. that will increase the energy value of the very low frequency component (VLF) to a certain value or more. Based on this proposal, if the user measures the energy value of the very low frequency component (VLF) in the evening, by exercising, taking a bath, meditating for psychological improvement, or the like before going to bed that day, it is possible to improve the very low frequency component (VLF) considerably by about 10-30%, and it is possible to get good sleep that day. Furthermore, by reflecting on the daily living situation and exercising or meditating for psychological improvement the next day, it is possible to improve sleep. After the sleep evaluation prediction model formula is constructed, it is no longer necessary to calculate the sleep evaluation index for constructing the model by the sleep evaluation index calculation unit 5, but it is desirable to record it daily and use it as reference information, such as for comparison with the predicted sleep index evaluation by the sleep improvement plan proposing unit 7.

[0041] By carrying out such processing, the sleep improvement device allows the user to grasp the quality of sleep in the evening and before going to bed, and encourages the user to improve the sleep of the day and to reflect on the daily life. In particular, the sleep state can be improved by finding ways to increase the energy value of the very low frequency component (VLF) and ways to improve the daily life situation, including psychological aspects. This allows the user to set a target value for reconsidering the daily life situation (psychological and physical), and motivates the user to improve the daily life. This method can also be an indicator for exercise therapy in the rehabilitation of lifestyle-related diseases such as diabetes.

[0042] The present invention is not limited to the above-described embodiment.

[0043] For example, instead of using the hollow tube described above, the biosignal detection unit 1 may be an air mat type detection means as shown in Fig. 6. That is, the biosignal detection unit 30 shown in Fig. 6 is configured by connecting an air tube 30b to one end of an air mat 30a filled with air, and further connecting a micro-differential pressure sensor 30c to the air tube 30b. The micro-differential pressure sensor 30c may be the same as that described in the case of the biosignal detection unit 1 using a hollow tube.

[0044] In addition, although a configuration has been described that includes a bed-type device, an independent heart rate measuring unit 11 and an autonomic nerve component calculating unit 12, and a cloud server, the present invention is not limited to such a device configuration, and various configurations can be adopted as long as the configuration allows each unit to perform its function.

[0045] Thus, it goes without saying that the present invention can be modified appropriately without departing from the spirit of the invention. [Explanation of symbols]

[0046] 1,30 Biological signal detection unit 1a Pressure detection tube 1b,30c Micro differential pressure sensor 2 Signal Amplification Section 3. Filter section 4. Sleep stage determination section 5. Sleep evaluation index calculation section 6. Sleep evaluation prediction model formula construction section 7 Sleep improvement proposal department 11 Heart rate measurement section 12 Autonomic nerve component calculation unit 21 Sleeper 22 Hard Sheet 23 Cushion seat 30a air mat 30b air tube

Claims

1. A sleep improvement device that contributes to improving a user's sleep state by providing a sleep improvement plan based on lifestyle improvements suited to the user, a heartbeat signal measuring means for measuring a heartbeat signal of the user; an autonomic nerve component calculation means for calculating an energy value of at least a very low frequency component (VLF) of 0.003 to 0.04 Hz among the autonomic nerve components of the user based on the heartbeat signal measured by the heartbeat signal measurement means; a biosignal detection means for detecting a biosignal of the user; a sleep stage determining means for determining each sleep stage of the user during sleep based on the biological signal detected by the biological signal detecting means and calculating a time for each sleep stage; a sleep evaluation index calculation means for calculating a sleep evaluation index for model construction based on the time of each sleep stage determined by the sleep stage determination means; a sleep evaluation prediction model formula constructing means for constructing a sleep evaluation prediction model formula specific to the user based on the sleep evaluation index for model construction calculated by the sleep evaluation index calculation means and the energy value of at least the very low frequency component (VLF) before going to bed calculated by the autonomic nerve component calculation means; and a sleep improvement plan suggesting means for calculating a predicted sleep evaluation index at that time based on the sleep evaluation prediction model equation constructed by the sleep evaluation prediction model equation constructing means and the energy value of at least the very low frequency component (VLF) thereafter calculated by the autonomic nerve component calculating means, and for suggesting a sleep improvement plan by lifestyle improvement such that the energy value of at least the very low frequency component (VLF) is made equal to or greater than a certain value based on the calculated predicted sleep evaluation index. A sleep improvement device characterized by:

2. A sleep improvement method that contributes to improving a user's sleep state by providing a sleep improvement plan based on lifestyle improvements suited to the user, a heartbeat signal measuring step of measuring a heartbeat signal of the user by a predetermined heartbeat signal measuring means; an autonomic nerve component calculation step in which a signal processing processor calculates an energy value of at least a very low frequency component (VLF) of 0.003 to 0.04 Hz among the autonomic nerve components of the user based on the heartbeat signal measured in the heartbeat signal measurement step; a biosignal detection step of detecting a biosignal of the user by a predetermined biosignal detection means; a sleep stage determination step in which the processor determines each sleep stage of the user during sleep based on the biological signal detected in the biological signal detection step, and calculates the time of each sleep stage; a sleep evaluation index calculation step of calculating a sleep evaluation index for model construction based on the time of each sleep stage determined in the sleep stage determination step; a sleep evaluation prediction model formula constructing step in which the processor constructs a sleep evaluation prediction model formula specific to the user based on the sleep evaluation index for model construction calculated in the sleep evaluation index calculation step and the energy value of at least the very low frequency component (VLF) before going to bed calculated in the autonomic nerve component calculation step; the processor calculates a predicted sleep evaluation index at that time based on the sleep evaluation prediction model equation constructed in the sleep evaluation prediction model equation construction step and the energy value of at least the very low frequency component (VLF) calculated in the autonomic nerve component calculation step thereafter, and further comprises a sleep improvement plan suggesting step of proposing a sleep improvement plan by lifestyle improvement such that the energy value of at least the very low frequency component (VLF) is equal to or greater than a certain value based on the calculated predicted sleep evaluation index. A method for improving sleep, comprising:

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