Control apparatus, grip member, and estimation system

Abstract

To estimate the health condition of a user based on daily motions of the user.SOLUTION: A server 20 comprises: a control unit 25 which acquires, from an acceleration sensor 13 provided to a grip member 11 being a member gripped by a user, a first measurement value concerning a tremor of the user's hand measured by the acceleration sensor 13; and an estimation unit 22 which estimates the health condition of the user based on the first measurement value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a control device, a gripping member, an estimation system, and the like. [Background technology]

[0002] Due to the increasing demand for health in recent years, technology has been developed to grasp the daily health condition of a user using, for example, a wristwatch-type wearable device. Specifically, technology has been developed to acquire biometric information such as the user's daily heart rate and blood oxygen saturation using a sensor installed in the wearable device. For example, Patent Document 1 discloses a technology for acquiring biometric information such as heart rate, body temperature, blood pressure, and body tremors using a wearable device. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2018 / 168369 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 requires the user to wear a wearable device in order to acquire biometric information. In other words, the technology described in Patent Document 1 cannot acquire biometric information if the user forgets to wear the wearable device.

[0005] The present invention has been made in consideration of the above-mentioned problems, and has an object to estimate a health condition of a user based on the user's daily movements. [Means for solving the problem]

[0006] In order to achieve the above-mentioned object, the control device of the present invention includes an acquisition unit that acquires a first measurement value related to the tremor of the user's hand from a first sensor provided on a gripping member that is a member gripped by the user, and an estimation unit that estimates the health condition of the user based on the first measurement value. Effect of the Invention

[0007] According to the present invention, it is possible to estimate a user's health condition based on the user's daily movements. [Brief description of the drawings]

[0008] [Figure 1] 1 is a functional block diagram illustrating a schematic configuration of an estimation system according to a first embodiment. [Diagram 2] FIG. 2 is a side view showing a schematic structure of a part of the door according to the first embodiment. [Diagram 3] FIG. 4 is a sequence diagram showing an example of a processing flow of the estimation system according to the first embodiment. [Figure 4] FIG. 11 is a functional block diagram illustrating a schematic configuration of an estimation system according to a second embodiment. [Diagram 5] FIG. 11 is a functional block diagram illustrating a schematic configuration of an estimation system according to a third embodiment. [Figure 6] 11 is a graph showing the relationship between log data of a first measurement value and a first threshold value. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] [First embodiment] System Configuration Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a functional block diagram showing a schematic configuration of an estimation system 100 according to a first embodiment. The estimation system 100 is a system that estimates the health condition of a user who grasps a gripping member. In this specification, the term "grip member" refers to any member that a user can grasp with one or both hands. In this embodiment, an example will be described in which the gripping member is a doorknob 11. As shown in FIG. 1, the estimation system 100 includes the doorknob 11 and a server (control device) 20. The estimation system 100 may also include a mobile terminal 30.

[0010] The door 10 is a door that can be manually opened and closed by a user. The door 10 is equipped with a doorknob 11. The type, size, shape, internal structure, and the like of the door 10 are not particularly limited. For example, the door 10 may be a door that separates the inside and outside of a building, or a door that separates the inside and outside of a room in a building. The door 10 may be a hinged door or a sliding door.

[0011] The doorknob 11 is a member provided on the door 10 and is a gripping member of the door 10. As long as the doorknob 11 includes configurations corresponding to various functional blocks, the type, size, shape, and internal structure of the doorknob 11 are not particularly limited. For example, the doorknob 11 may be a push-pull type doorknob or a rotary type doorknob. In this embodiment, as an example, a case will be described in which the door 10 is a sliding door and the doorknob 11 is a push-pull type doorknob. The doorknob 11 includes a fingerprint sensor (biometric information sensor) 12, an acceleration sensor (first sensor) 13, a control unit 15, and a communication unit (grip member transmission unit) 16. The doorknob 11 may include a storage unit that stores data necessary for the operation of the control unit 15 and the like.

[0012] The fingerprint sensor 12 detects the fingerprint of a user who is holding the doorknob 11. The fingerprint sensor 12 outputs data indicating the detected fingerprint (fingerprint data) to the control unit 15. The specific type of the fingerprint sensor is not particularly limited. For example, the fingerprint sensor 12 may be a capacitance type sensor.

[0013] The acceleration sensor 13 measures the acceleration when a user grips the doorknob 11 to open the door 10. The acceleration sensor 13 may be, for example, a MEMS type acceleration sensor. The acceleration sensor 13 calculates the amplitude value of the acceleration output from the measurement results of the acceleration for a predetermined time period (for example, 3 seconds). Alternatively, the acceleration sensor 13 may measure the value of the vibration period (i.e., frequency). The acceleration sensor 13 outputs the calculated or measured output amplitude value (first measurement value) to the control unit 15. In this embodiment, the acceleration sensor 13 calculates the output amplitude value. Generally, the output amplitude value of an acceleration sensor increases according to the magnitude of vibration transmitted to the sensor. The output frequency of an acceleration sensor is the number of vibrations per unit time. Therefore, the output amplitude value and frequency of the acceleration sensor 13 provided on the doorknob 11 can be said to indicate the presence or absence of hand tremors, the magnitude of the tremors, and the number of tremors when the user pulls the door. In this way, the output amplitude value and frequency calculated by the acceleration sensor 13 can be said to be measurement values ​​related to the tremors of the user's hand. In this specification, the term "measurement value" may include not only values ​​measured by a sensor, but also values ​​calculated from the measured values.

[0014] The control unit 15 acquires the fingerprint data from the fingerprint sensor 12 and the first measurement value from the acceleration sensor 13. The control unit 15 outputs the acquired data to the communication unit 16. The communication unit 16 is a communication interface that enables the control unit 15 to communicate with the server 20. The communication unit 16 transmits various data output from the control unit 15 to the server 20. In this embodiment, the control unit 15 and the communication unit 16 are incorporated in the doorknob 11. However, the control unit 15 and the communication unit 16 may be incorporated in a location other than the doorknob 11 of the door 10 (for example, the door body).

[0015] The server 20 is a management device that manages various data received from the control unit 15. The server 20 is also an estimation device that estimates the health condition of the user based on at least the first measurement value. The server 20 is connected to the control unit 15 via a communication network. The connection method may be wired or wireless. When the estimation system 100 includes a mobile terminal 30, the server 20 is also connected to the mobile terminal 30 via the communication network.

[0016] The installation location of the server 20 is not particularly limited. For example, the server 20 may be placed in a room near a door. The server 20 may also be a server (or a virtual server) that exists in a cloud environment. In the present embodiment, as an example, the server 20 is placed in a room separated by a door 10. The server 20 may also be a device that performs overall management and control of other devices, such as home devices. The server 20 includes a communication unit (transmission unit) 21, a control unit 25 (acquisition unit), and a storage unit 24.

[0017] The communication unit 21 communicates with the communication unit 16 of the doorknob 11. When the estimation system 100 includes a mobile terminal 30, the communication unit 21 also communicates with the communication unit 31 of the mobile terminal 30. The communication method and type of network between the communication unit 21 and the communication unit 16 of the doorknob 11 are not particularly limited. For example, the communication unit 16 and the communication unit 21 may be connected by wireless communication such as Wi-Fi (registered trademark) or Bluetooth (registered trademark). The communication method and type of network between the communication unit 21 and the communication unit 31 of the mobile terminal 30 are also not particularly limited.

[0018] The control unit 25 is a CPU (Central Processing Unit) that performs overall control of the server 20. The control unit 25 includes an estimation unit 22 and a recognition unit 23. The control unit 25 reads out and executes software programs corresponding to the functions of the estimation unit 22 and the recognition unit 23 from the storage unit 24. This allows the control unit 25 to realize the estimation unit 22 and the recognition unit 23 as functional blocks.

[0019] The identification unit 23 identifies the user based on the fingerprint data detected by the fingerprint sensor 12. In this embodiment, the identification unit 23 identifies the user using fingerprint authentication. The specific method of fingerprint authentication is not particularly limited. For example, the identification unit 23 may perform fingerprint authentication of the user by comparing the fingerprint data detected by the fingerprint sensor 12 with fingerprint data registered in advance in the storage unit 24. Note that, for fingerprint authentication, the identification unit 23 may identify whether the received fingerprint data indicates a fingerprint of a left or right hand. For example, the identification unit 23 may identify whether the received fingerprint data indicates a fingerprint of a left or right hand from the shape of the fingerprint indicated by the fingerprint data.

[0020] The estimation unit 22 estimates the user's health condition based on at least the first measurement value. In this embodiment, "estimating the user's health condition" means determining whether or not there is an abnormality in the user's health condition. However, the estimation process in the present invention is not necessarily limited to this meaning. For example, in addition to determining whether or not there is an abnormality in the user's health condition, if there is an abnormality, the content of the abnormality (i.e., symptoms or disease name) and / or the degree of the abnormality may be estimated. The estimation process in the estimation unit 22 will be described in detail later.

[0021] The control unit 25 may store the estimation result of the estimation unit 22 (for example, information indicating whether or not there is an abnormality in the health condition of the user) in the storage unit 24. In addition, when the estimation system 100 includes a mobile terminal 30, the control unit 25 may transmit information based on the estimation result of the estimation unit 22 to the mobile terminal 30 via the communication unit 21.

[0022] Here, the "information based on the estimation result" may be information indicating the estimation result itself, or may be information secondarily created from the estimation result. For example, the "information based on the estimation result" may be a code according to the estimation result, or a notification text, image (or video), and / or audio data created based on the estimation result. Furthermore, the "information based on the estimation result" may be content that directly indicates the estimation result, or may be information that indirectly indicates the estimation result. For example, when the estimation unit 22 estimates that "there is something wrong with the health condition," the information based on the estimation result may be text indicating that the health condition is wrong, or text indicating that it is recommended to go to a hospital.

[0023] The storage unit 24 stores various data necessary for the operation of the server 20. The storage unit 24 is realized by a non-transitory recording medium. For example, the storage unit 24 may be realized by a combination of a Read Only Memory (ROM) and a Random Access Memory (RAM), etc.

[0024] The storage unit 24 stores an identification data table that associates a user's identification information with the user's fingerprint data (hereinafter referred to as registered fingerprint data) that has been detected and registered in advance. The user's identification information is, for example, the user's name and / or user ID. In this embodiment, as an example, the storage unit 24 stores the fingerprint data of the right thumb and the left thumb of each user as registered fingerprint data.

[0025] The registered fingerprint data of which finger of the left or right hand (or both) is to be stored in the memory unit 24 may be appropriately determined according to the arrangement position of the detection surface of the fingerprint sensor 12 on the doorknob 11. For example, assume that the detection surface of the fingerprint sensor 12 is arranged at a position where the index finger of the user touches when the user grips the doorknob 11. In this case, it is desirable that the memory unit 24 stores the fingerprint data of the index fingers of the left and right hands as the registered fingerprint data. Each record in the identification data table may further be associated with identification information of the mobile terminal 30. The identification information of the mobile terminal 30 is, for example, a unique number, a MAC address, a mobile phone number, and / or an email address of the mobile terminal 30.

[0026] In the present embodiment, as an example, the identification data table is one in which the identification information of the user is associated with the registered fingerprint data. However, a fingerprint is also the identification information of the user. Therefore, the registered fingerprint data may also serve as the identification information of the user. That is, the storage unit 24 may store only the registered fingerprint data, or data in which the registered fingerprint data is associated with the identification information of the mobile terminal 30, instead of the identification data table. In addition, the identification data table is not limited to the above-mentioned table configuration. For example, the storage unit 24 may separately store a table in which the user identification information is associated with the registered fingerprint data, and a table in which the user identification information is associated with the identification information of the mobile terminal 30. In addition, the identification data table does not necessarily have to be in the form of a data table.

[0027] The storage unit 24 may further store the user's identification information in association with the received first measurement value. That is, the storage unit 24 may record log data of the first measurement value for each user. This log data is recorded for each event in which the user grips the doorknob 11. The period for recording the log data and the period for retaining the record are not particularly limited. However, it is preferable that the period for recording the log data and the period for retaining the record are relatively long, such as one year or more. The timing of recording the log data and the recording period and retention period are the same for various log data in the following embodiments.

[0028] The memory unit 24 may further store various thresholds necessary for the estimation unit 22 to estimate the health condition. In this embodiment, the memory unit 24 stores a first threshold. The first threshold is a threshold for a first measurement value. The first threshold is a threshold for determining whether the degree of hand tremor indicated by the first measurement value is a degree of tremor that can occur within the range of the user's normal health condition, or is a degree of tremor that exceeds that degree (i.e., an abnormality has occurred in the user's health condition). The first threshold may be determined as appropriate depending on the arrangement position and detection accuracy of the acceleration sensor 13, etc.

[0029] The first threshold may be set to a different value for each user, or may be a common value for all users. When the first threshold is set to a different value for each user, the storage unit 24 stores the user identification information and the first threshold corresponding to the user identification information in association with each other. In this case, the first threshold may be determined according to user information (age, sex, height, weight, etc.) registered in advance in the storage unit 24. This makes it possible to change the threshold for, for example, elderly people and young people who have weak strength. Also, a first threshold for the right hand and a first threshold for the left hand may be set separately. In general, it is considered that the trembling of the hand is greater when opening and closing a door with the non-dominant hand than when opening and closing the door with the dominant hand. Therefore, by storing the first threshold for each of the left and right hands, it is possible to more accurately determine whether the trembling of the user's hand is within the range of a normal health condition.

[0030] The mobile terminal 30 is a terminal device carried by a user, such as a smartphone or a smartwatch. The mobile terminal 30 is a device for notifying the user of the estimation result of the server 20. The mobile terminal 30 includes a communication unit 31, a control unit 32, and an output unit 33. The mobile terminal 30 may include a storage unit that stores data necessary for the operation of the mobile terminal 30.

[0031] The communication unit 31 communicates with the communication unit 21 of the server 20. The control unit 32 is a CPU that controls the mobile terminal 30 in an overall manner. The control unit 32 receives information based on the estimation result from the server 20 via the communication unit 31. The control unit 32 outputs the information based on the estimation result to the output unit 33, either directly or after performing various conversions according to the output format of the output unit 33. The output unit 33 performs output according to the control of the control unit 32. As a result, the user is notified of the estimation result or information created based on the estimation result. The output format of the output unit 33 is not particularly limited. For example, the output unit 33 may be a speaker, a display, or both.

[0032] The server 20 may be capable of communicating with a plurality of control units 15. The server 20 may also be capable of communicating with a plurality of mobile terminals 30. In this case, the identification unit 23 of the server 20 identifies the users holding each doorknob 11 based on the fingerprint data received from each control unit 15. The estimation unit 22 may also execute estimation processing based on the first measurement values ​​received from each control unit 15, and the control unit 25 may transmit information based on the estimation results to the mobile terminals 30 associated with each identified user.

[0033] Sensor placement Next, the details of the structure of the doorknob 11 and an example of the arrangement of various sensors will be described with reference to FIG. 2. FIG. 2 is a side view that shows a schematic structure of a part of the door 10. Although FIG. 2 shows only the doorknob 11 on one side of the door 10, a similar doorknob 11 may be provided on the opposite side of the door 10. Furthermore, although mechanisms that are not related to the estimation system 100, such as a lock mechanism of the door 10, are not shown, the door 10 may have a structure and function that are normally provided as a door. In the following description, the direction in which a user who opens the door 10 is present (the left side in FIG. 2) is referred to as the "outside", and the direction in which the door body 10a of the door 10 is present (the right side in FIG. 2) is referred to as the "inside".

[0034] The door 10 has a doorknob 11 attached to a door body 10a. The doorknob 11 includes, for example, a gripping portion 11a extending vertically and a pair of mounting portions 11b continuing from the upper and lower ends of the gripping portion 11a. The doorknob 11 is attached to the door body 10a by the pair of mounting portions 11b. By attaching the gripping portion 11a and the pair of mounting portions 11b to the door body 10a in such a shape, a predetermined space is secured between the gripping portion 11a and the door body 10a. A user can insert his hand into this space to grip the gripping portion 11a.

[0035] A push-in portion 11c is provided on the inner surface of the grip portion 11a (the surface facing the door main body 10a in FIG. 2). When the user is not gripping the grip portion 11a, the push-in portion 11c protrudes. Here, "protruding" means protruding inward from the grip portion 11a. When the user grips the grip portion 11a to open the door 10, the push-in portion 11c is pushed in by the user's fingers. In the door 10 shown in FIG. 2, the push-in portion 11c is pushed in this way to unlock the door 10, and the door 10 can be opened.

[0036] A fingerprint sensor 12 is incorporated on the outer surface of the gripping portion 11a of the doorknob 11. In this embodiment, the detection surface of the fingerprint sensor 12 is disposed at a position where the thumb is expected to touch when the user grips the gripping portion 11a of the doorknob 11. This allows the fingerprint sensor 12 to detect the fingerprint of the thumb of the user gripping the doorknob 11. An acceleration sensor 13 is incorporated inside the gripping portion 11a. The location of the acceleration sensor 13 is not particularly limited as long as it can detect the acceleration when the user grips the gripping portion 11a of the doorknob 11 (i.e., the acceleration of pulling the door plus the magnitude and frequency of the tremor).

[0037] <Processing flow> Next, a series of processing steps performed by the estimation system 100 to estimate the health condition of a user will be described. Fig. 3 is a sequence diagram showing an example of the processing steps performed by the estimation system 100 according to the first embodiment. A user opens and closes a door 10 at home, work, or the like as a daily activity. When opening or closing the door 10, the user holds the gripping portion 11a of the doorknob 11.

[0038] When the gripping portion 11a is gripped, the pushing portion 11c is pushed into the gripping portion 11a. At this time, the acceleration sensor 13 arranged on the door body 10a side of the gripping portion 11a measures the acceleration and calculates an output amplitude value of the acceleration (i.e., a value related to the trembling of the user's hand) (step S11). Also, when the user grips the doorknob 11, the pad of the user's finger is pressed against the detection surface of the fingerprint sensor 12. The fingerprint sensor 12 detects the fingerprint of the user pressed against the detection surface (step S12). The acceleration sensor 13 and the fingerprint sensor 12 output the output amplitude value and the fingerprint data, respectively, to the control unit 15. The control unit 15 transmits the inputted output amplitude value and the fingerprint data to the server 20 (step S13). Note that the order of steps S11 and S12 may be random and may be performed in parallel.

[0039] The control unit 25 of the server 20 receives the output amplitude value and the fingerprint data from the control unit 15 (step S21). The identification unit 23 of the control unit 25 performs fingerprint authentication based on the fingerprint data to identify the user who has gripped the doorknob 11 (step S22). Specifically, the identification unit 23 compares the registered fingerprint data in the identification data table of the storage unit 24 with the received fingerprint data. At this time, the identification unit 23 may identify whether the received fingerprint data is of a finger on the left or right hand. If the registered fingerprint data matches the received fingerprint data, the identification unit 23 identifies the user associated with the matching registered fingerprint data as the user who has gripped the doorknob 11 (i.e., determines that the user identification has been successful). On the other hand, if the registered fingerprint data does not match the received fingerprint data, the control unit 25 determines that the user identification has failed.

[0040] If the user identification is successful in the identification unit 23 (YES in step S23), the estimation unit 22 estimates the user's health condition based on the output amplitude value (step S24). The health condition estimation process performed by the estimation unit 22 will be described in detail below. If the user identification is unsuccessful in the identification unit 23 (NO in step S23), the server 20 does not execute the subsequent steps and ends the series of processes. If the user identification is successful, the control unit 25 may add the received output amplitude value to the log data of the first measurement value of the identified user at any timing after this step.

[0041] In this embodiment, the estimation unit 22 refers to the first threshold value stored in the storage unit 24 and compares the magnitude of the output amplitude value with the first threshold value. When first threshold values ​​corresponding to the left and right hands of the user are set, the estimation unit 22 refers to the first threshold value corresponding to the hand identified by the identification unit 23. When the output amplitude value is equal to or less than the first threshold value, the estimation unit 22 estimates that there is no abnormality in the user's health condition. On the other hand, when the output amplitude value is greater than the first threshold value, the estimation unit 22 determines that there is an abnormality in the user's health condition.

[0042] If the estimation result indicates that there is an abnormality in the user's health condition (YES in step S25), the control unit 25 transmits information based on the estimation result to the mobile terminal 30 (step S26). The user's mobile terminal 30 can be identified by referring to the record for the user in the identification data table. On the other hand, if it is determined that there is no abnormality in the user's health condition (NO in step S25), the control unit 25 does not perform the process of step S26 and ends the series of processes.

[0043] The control unit 32 of the mobile terminal 30 receives information based on the estimation result via the communication unit 31 (step S31). The control unit 32 causes the output unit 33 to output a notification corresponding to the information based on the estimation result (step S32). For example, if the mobile terminal 30 is equipped with a display as the output unit 33, the control unit 32 may cause the display to display, as the above-mentioned notification, a warning message such as "Abnormality was observed in the hand movement" or "You may be feeling unwell."

[0044] Note that even if the estimation unit 22 estimates that there is no abnormality in the user's health condition (NO in step S25), information based on the estimation result may be transmitted to the mobile terminal 30. Then, the mobile terminal 30 may output a notification according to the information based on the estimation result. For example, the control unit 32 of the mobile terminal 30 may display a message saying "There is no abnormality in the health condition" on the display. Furthermore, in a configuration in which the estimation system 100 does not include the mobile terminal 30, the estimation system 100 may not execute steps S26, S31, and S32.

[0045] According to the process shown in FIG. 3, the health condition of a user is estimated when the user grips the doorknob 11. Then, if it is estimated that there is an abnormality in the user's health condition, a notification is output from the mobile terminal 30 of the user. In this way, the estimation system 100 can obtain a first measurement value that can estimate the health condition of the user from the user's daily behavior of "opening and closing a door". Then, the health condition of the user can be estimated based on the first measurement value. Therefore, according to the estimation system 100, the health condition of the user can be estimated based on the user's daily behavior.

[0046] Furthermore, in this embodiment, the first measurement value used by the estimation unit 22 to estimate the health condition is a measurement value related to the tremor of the user's hand. In general, it is said that the tremor of a human hand is likely to appear as an early symptom of various diseases. For example, various diseases such as essential tremor, Parkinson's disease, multiple sclerosis, hyperthyroidism, alcoholism, and drug-induced tremor are considered to be causes of hand tremor. Even if the user does not have such a disease, if the user has a physical condition, the user's fingers may tremble. For example, even if the user has a simple cold, it is quite conceivable that the user's hand may not be strong due to a physical condition, causing trembling. The estimation system 100 according to this embodiment estimates the presence or absence of an abnormality in the health condition each time from the daily actions (opening and closing a door) that the user will perform every day. Therefore, if the user has a possibility of having various disorders including the above-mentioned diseases, the possibility can be found early.

[0047] Second Embodiment The estimation system according to the present invention may acquire, from a second sensor provided on the gripping member, a second measurement value, which is data related to the grip strength of the user's hand acquired by the second sensor. Then, the estimation system may estimate the health condition of the user based on the first measurement value and the second measurement value. Hereinafter, an estimation system 200 according to a second embodiment of the present invention will be described. Note that in the following embodiments including this embodiment, the description of the same configuration and processing as in the first embodiment will not be repeated.

[0048] 4 is a functional block diagram showing a schematic configuration of an estimation system 200 according to this embodiment. The estimation system 200 differs from the estimation system 100 in that the doorknob 11 is provided with a pressure sensor (second sensor) 13 and that the control unit 25 of the server 20 includes an estimation unit 26 instead of the estimation unit 22.

[0049] The pressure sensor 14 measures the pressure applied to the gripping portion of the doorknob 11 when the user grips the doorknob 11. The pressure sensor 14 can be realized by, for example, a thin-film type strain gauge. The pressure sensor 14 outputs a value indicating the measurement result (second measurement value) to the control unit 15. In other words, it can be said that the pressure sensor 14 measures the force with which the user grips the doorknob, that is, the grip strength of the user. Therefore, the second measurement value is a value indicating the grip strength of the user. Note that the pressure sensor 14 may measure the pressure for a predetermined time (for example, 3 seconds) when the user grips the doorknob 11 to open the door 10. In this case, the pressure sensor 14 outputs the measurement result for the above-mentioned predetermined time as the second measurement value. Therefore, by analyzing the measurement result of the pressure sensor 14 over time, it is possible to identify minute fluctuations in the grip strength of the user.

[0050] The position of the pressure sensor 14 in the doorknob 11 is not particularly limited. For example, when the doorknob 11 has a shape as shown in FIG. 2 of the first embodiment, the pressure sensor 14 may be incorporated in the push-in portion 11c. The size of the detection surface of the pressure sensor 14 is not particularly limited, but when the doorknob 11 has a shape similar to that of FIG. 2, the detection surface of the pressure sensor 14 may have a length of about the width of four fingers from the index finger to the little finger in the height direction of the door 10 (the vertical direction in FIG. 2). This allows the pressure sensor 14 to stably measure the pressure applied to the detection surface, that is, the grip force of the user when the user grips the grip portion 11a (and the fluctuation of the grip force, which part of the hand the grip portion 11a receives the force from, etc.). In this embodiment, the acceleration sensor 13 may be incorporated in the grip portion 11a on the back side (i.e., inside) of the detection surface of the pressure sensor 14. This allows the acceleration sensor 13 to measure the acceleration at the same time as the grip force is measured by the pressure sensor 14.

[0051] The control unit 15 according to this embodiment acquires the second measurement value of the pressure sensor 14 and transmits it to the server 20 via the communication unit 16. When the communication unit 21 of the server 20 receives the second measurement value, it outputs it to the control unit 25. The estimation unit 26 of the control unit 25 estimates the user's health condition based on the first measurement value and the second measurement value. The processing of the estimation unit 26 will be described in detail later.

[0052] The storage unit 24 according to this embodiment stores a second threshold in addition to the first threshold. The second threshold is a threshold for the second measurement value. The second threshold is a threshold for determining whether the grip strength indicated by the second measurement value is within the range of the user's normal health condition or exceeds the range (i.e., the user's health condition is abnormal). The second threshold may be appropriately determined depending on the arrangement position and detection accuracy of the pressure sensor 14, etc.

[0053] The second threshold may be set to a different value for each user, or may be a common value for all users. When the second threshold is set to a different value for each user, the storage unit 24 stores the user identification information and the second threshold corresponding to the user identification information in association with each other. In this case, the second threshold may be determined according to user information (age, sex, height, weight, etc.) registered in advance in the storage unit 24. This makes it possible to change the second threshold for, for example, elderly people and young people who have weak strength. Also, a second threshold for the right hand and a second threshold for the left hand may be set separately. In general, it is considered that the grip strength is smaller when opening and closing a door with the non-dominant hand than when opening and closing a door with the dominant hand. Therefore, by storing the second threshold for each of the left and right hands, it is possible to more accurately determine whether the user's grip strength is within the range of a normal health condition. The storage unit 24 may store the user's identification information in association with the received first measurement value and second measurement value. That is, the storage unit 24 may record log data of the first measurement value and the second measurement value for each user.

[0054] <<Functions of Estimation Unit 26>> The basic function of the estimation unit 26 is similar to that of the estimation unit 22. However, the estimation unit 26 differs from the estimation unit 22 of the first embodiment in that the estimation unit 26 estimates the user's health condition based on the second measurement value as well as the first measurement value. As described above, the first measurement value is data related to hand tremors, and the second measurement value is a measurement value related to the grip strength of the hand. The estimation unit 26 judges the user's health condition based on each of these two parameters. Note that in the estimation unit 26, the judgment of the health condition based on the first measurement value (hereinafter referred to as the first judgment) and the judgment of the health condition based on the second measurement value (hereinafter referred to as the second judgment) may be performed in random order or in parallel. The estimation unit combines the results of these two judgments to finally estimate the estimation result of the user's health condition.

[0055] The estimation unit 26 may perform the first determination in a manner similar to step S24 in the first embodiment. The estimation unit 26 performs the second determination immediately before or after step S24, or in parallel with step S24, for example, by a method described below. Specifically, the estimation unit 26 refers to the second threshold value stored in the storage unit 24 and compares the second measurement value with the second threshold value. In addition, when second threshold values ​​corresponding to the left and right hands of the user are set, the estimation unit 26 refers to the second threshold value corresponding to the hand identified by the identification unit 23. When the second measurement value is equal to or greater than the second threshold value, the estimation unit 26 estimates that there is no abnormality in the user's health condition. On the other hand, when the second measurement value is less than the second threshold value, the estimation unit 26 determines that there is an abnormality in the user's health condition.

[0056] When the results of the first and second judgments are available, the estimation unit 26 derives a final estimation result. For example, when both the first and second judgments determine that "the user's health condition is abnormal," the estimation unit 26 may determine that "the user's health condition is abnormal" as the final estimation result. A user who is determined to have abnormalities in both the degree of hand tremor and grip strength is likely to have an abnormality in his / her health condition. The estimation system 200 can discover that the user's health condition is abnormal in such a case.

[0057] Also, for example, when it is determined that "the user's health condition is abnormal" in either the first determination or the second determination, the estimation unit 26 may determine that "the user's health condition is abnormal" as a final estimation result. A user who is determined to have an abnormality in the degree of hand tremor or grip strength may have an abnormality in his / her health condition. According to the estimation system 200, it is possible to discover that the user's health condition is abnormal in such a case.

[0058] The estimation unit 26 may simply determine whether the results of each of the first and second judgments are equal to or greater than various thresholds, without determining whether there is an abnormality in the health condition. In this case, the estimation unit 26 may estimate the user's health condition by combining the comparison result of the first measurement value and the first threshold value in the first judgment and the comparison result of the second measurement value and the second threshold value in the second judgment.

[0059] According to the present embodiment, the estimation system 200 can estimate the user's health condition based on the first measurement value and the second measurement value. Therefore, the estimation system 200 can estimate the user's health condition with higher accuracy (or in more detail).

[0060] The second measurement value used to estimate the health condition in this embodiment is a measurement value related to the grip strength of the user's hand. A decrease in the grip strength of the hand is likely to appear as an early symptom of various diseases, as is the case with hand tremors. For example, research results have shown that people with decreased grip strength have an increased risk of myocardial infarction and stroke, and also an increased mortality rate due to heart-related diseases. In addition, even if the user does not have such a disease, if the user's body is in poor condition, the grip strength may decrease. The estimation system 200 according to this embodiment estimates the user's health condition taking into account the user's grip strength, so that it is possible to find more possible diseases and disorders.

[0061] Third Embodiment The estimation system according to the present invention may acquire, from a third sensor provided on the grip member, a third measurement value relating to the force rotating the grip member and / or the rotation speed acquired by the third sensor. The estimation system may then estimate the user's health condition based on the first measurement value and the third measurement value. The estimation system may also estimate the user's health condition based on the first measurement value, the second measurement value, and the third measurement value.

[0062] Hereinafter, an estimation system 300 according to a third embodiment of the present invention will be described. In the following drawings and explanation, an example will be described in which the estimation system 300 estimates a user's health state based on a first measurement value, a second measurement value, and a third measurement value. That is, an example will be described in which the estimation system 300 has the functions of the estimation system 200 according to the second embodiment and further performs estimation taking into account the third measurement value.

[0063] 5 is a functional block diagram showing a schematic configuration of an estimation system 300 according to this embodiment. The estimation system 300 differs from the estimation systems 100 and 200 in that the doorknob 11 is provided with a torque sensor (third sensor) 17 and that the control unit 25 of the server 20 includes an estimation unit 27 instead of the estimation unit 22.

[0064] In this embodiment, the doorknob 11 is not a push-pull doorknob according to the first and second embodiments, but a rotary doorknob that opens and closes the door when the user grips and rotates it. For example, the doorknob 11 according to this embodiment is realized as a doorknob with a rotary knob. The fingerprint sensor 12, the pressure sensor 14, and the acceleration sensor 13 are appropriately arranged on the rotary doorknob so that each measurement is appropriately performed. For example, the fingerprint sensor 12 may be arranged at a position where the thumb touches when the user grips the doorknob 11.

[0065] The torque sensor 17 is a sensor that measures the force that rotates the doorknob (twists the doorknob) and / or the rotation speed when the user grips and rotates the doorknob 11. The torque sensor 17 is configured to measure the twist using, for example, a strain gauge and transmit the signal using a transformer or an optical device. The torque sensor 17 outputs a measurement value (third measurement value) to the control unit 15. Note that the torque sensor 17 may measure the rotation force for a predetermined time period (for example, 3 seconds) when the user rotates the doorknob 11 to open the door 10, instead of measuring the rotation force at a certain point (i.e., an instant), and calculate the average value. Then, the torque sensor 17 may output the calculated average value as the third measurement value. The type, arrangement position, size, and shape of the torque sensor 17 are not particularly limited as long as the torque sensor 17 can measure parameters related to the force that rotates the doorknob 11 and / or the rotation speed.

[0066] When the control unit 15 according to this embodiment acquires the third measurement value of the torque sensor 17, it transmits the third measurement value to the server 20 via the communication unit 16. When the communication unit 21 of the server 20 receives the third measurement value, it outputs it to the control unit 25. The estimation unit 27 of the control unit 25 estimates the user's health condition based on the first measurement value and the third measurement value (and the second measurement value). The processing of the estimation unit 27 will be described in detail later.

[0067] The memory unit 24 according to this embodiment stores a third threshold value. The third threshold value is a threshold value of a third measurement value. The third threshold value is a threshold value for determining whether the third measurement value (i.e., the force and / or speed with which the user turns the doorknob 11) is within the range of the user's normal health condition or exceeds the range (i.e., an abnormality has occurred in the user's health condition). The third threshold value may be appropriately determined depending on the arrangement position and detection accuracy of the torque sensor 17, etc.

[0068] The third threshold may be set to a different value for each user, or may be a common value for all users. When the third threshold is set to a different value for each user, the storage unit 24 stores the user identification information and the third threshold corresponding to the user identification information in association with each other. In this case, the third threshold may be determined according to user information (age, sex, height, weight, etc.) registered in advance in the storage unit 24. This makes it possible to change the third threshold for, for example, elderly people and young people who have weak strength. Also, a third threshold for the right hand and a third threshold for the left hand may be set separately. In general, it is considered that the force and / or speed at which the doorknob is turned is smaller when the doorknob is turned with the non-dominant hand than when the doorknob is turned with the dominant hand. Therefore, by storing the third thresholds for each of the left and right hands, it is possible to more accurately determine whether the force and / or speed at which the user turns the doorknob 11 is within the range of a normal health condition.

[0069] The storage unit 24 may further store the user's identification information in association with the received first and third measurement values ​​(and the second measurement value). That is, the storage unit 24 may record log data of the first and third measurement values ​​(and the second measurement value) for each user.

[0070] <<Functions of Estimation Unit 27>> The basic function of the estimation unit 27 is similar to that of the estimation unit 22. However, the estimation unit 27 differs from the estimation unit 22 of the first embodiment and the estimation unit 26 of the second embodiment in that the estimation unit 27 estimates the user's health state based on the third measurement value as well as the first measurement value. As described above, the estimation unit 27 may estimate the user's health state based on the second measurement value as well. In the following, a case will be described in which the estimation unit 27 estimates the health state using these three pieces of data. In the estimation unit 27, the first judgment, the second judgment, and the judgment of the health state based on the third measurement value (hereinafter referred to as the third judgment) may be performed in random order or in parallel. The estimation unit integrates the results of these three judgments to finally estimate the estimation result of the user's health state.

[0071] The estimation unit 27 may perform the first judgment in a manner similar to step S24 in the first embodiment. The estimation unit 27 performs the second judgment and the third judgment immediately before or after step S24, or in parallel with step S24. The estimation unit 27 refers to the third threshold value stored in the storage unit 24 and compares the third measurement value with the third threshold value. In addition, when the third threshold values ​​corresponding to the left and right hands of the user are set, the estimation unit 27 refers to the third threshold value corresponding to the hand identified by the identification unit 23. When the third measurement value is equal to or greater than the third threshold value, the estimation unit 27 estimates that there is no abnormality in the health condition of the user. On the other hand, when the third measurement value is less than the third threshold value, the estimation unit 27 determines that there is an abnormality in the health condition of the user.

[0072] When the results of the first, second, and third judgments are all available, the estimation unit 27 derives a final estimation result. For example, when the first, second, and third judgments all determine that "the user's health condition is abnormal," the estimation unit 27 may determine that "the user's health condition is abnormal" as the final estimation result. A user who is determined to have "abnormalities" in the judgments about the degree of hand tremor, the judgment about grip strength, and the judgment about the force and speed of twisting a doorknob is highly likely to have an abnormality in his / her health condition. The estimation system 300 can discover that the user's health condition is abnormal in such cases.

[0073] Also, for example, when it is determined that "the user's health condition is abnormal" in any one of the first, second, and third determinations, the estimation unit 26 may determine that "the user's health condition is abnormal" as a final estimation result. A user who is determined to have an abnormality in at least one of the degree of hand tremor, grip strength, and force and speed of twisting a doorknob may have an abnormality in his / her health condition. The estimation system 300 can discover that the user's health condition is abnormal in such a case.

[0074] The estimation unit 27 may simply determine whether each of the first, second, and third judgments is greater than or less than the various thresholds, without determining whether there is an abnormality in the health state. In this case, the estimation unit 27 may estimate the user's health state by integrating the results of comparisons between the various measurement values ​​in each judgment and the various thresholds.

[0075] According to the present embodiment, the estimation system 300 can estimate the user's health condition based on the first and third measurement values ​​(and the second measurement value). Therefore, the estimation system 300 can estimate the user's health condition with higher accuracy or in more detail.

[0076] The third measurement value used in the present embodiment for estimating the health condition is a measurement value related to the force and / or speed with which the user turns the doorknob 11. The strength and / or speed of such a twisting motion may decrease due to various diseases, physical disorders, etc., as with the grip strength of the hand. The estimation system 300 according to the present embodiment estimates the health condition of the user by taking into account the twisting force and / or speed of the user. Therefore, the estimation system 300 can find more possible diseases and disorders.

[0077] Note that the configuration and processing related to the estimation system 200 are not essential to the estimation system 300. That is, the estimation system 300 does not need to include the pressure sensor 14. The control unit 25 does not need to acquire the second measurement value. The estimation unit 27 does not need to execute the second determination. In this case, the estimation unit 27 only needs to combine the first and third determinations to derive a final estimation result. [Fourth embodiment]

[0078] In the present invention, the first to third thresholds may be values ​​derived by the estimation unit according to the log data of the first to third measurement values, respectively. That is, the first to third thresholds may be variable values. Hereinafter, a fourth embodiment of the present invention will be described. Note that the configuration according to this embodiment is applicable to any of the estimation systems 100, 200, and 300 described in the first to third embodiments.

[0079] In addition to the functions described in the above embodiments, the estimation unit 22, 26, or 27 according to this embodiment has a function of calculating a threshold value used for judgment from log data. That is, the estimation unit 22 can calculate a first threshold value from log data of a first measurement value. The estimation unit 26 has a function of calculating a second threshold value from log data of a second measurement value in addition to the above-mentioned calculation function of the estimation unit 22. The estimation unit 27 has a function of calculating a third threshold value from log data of a third measurement value in addition to the above-mentioned calculation function of the estimation unit 22. The estimation unit 27 may have a function of calculating a second threshold value from log data of a second measurement value, similar to the estimation unit 26.

[0080] For example, the estimation unit 22, 26, or 27 refers to various log data in the storage unit 24 and calculates the average values ​​of various measurement values ​​for a recent predetermined period (for example, the most recent month or year). The estimation unit 22, 26, or 27 multiplies the average value of the first measurement value for the most recent predetermined period by a predetermined coefficient (such as 1.3) that is greater than 1 to obtain the first threshold value. The estimation unit 26 or 27 multiplies the average value of the second measurement value for the most recent predetermined period by a predetermined coefficient (such as 0.7) that is less than 1 to obtain the second threshold value. The estimation unit 27 multiplies the average value of the third measurement value for the most recent predetermined period by a predetermined coefficient (such as 0.7) that is less than 1 to obtain the third threshold value. These predetermined coefficients may be determined as appropriate.

[0081] 6(a) and 6(b) are graphs showing an example of the relationship between the log data of the output amplitude value, which is the first measurement value, and the first threshold value. As shown in the figure, the vertical axis of the graph is the output amplitude value of the acceleration sensor 13, and the horizontal axis is time. Each plot in the graph is the first measurement value for one record in the log of the first measurement value. In the graph of FIG. 6, each plot is connected by a broken line to make the transition of the values ​​easier to see. However, each plot is independent data recorded each time an event such as opening and closing of the door occurs, and is not measured continuously in time.

[0082] In FIG. 6(a), as shown in the graph, the latest output amplitude value (i.e., the first measurement value just measured) is equal to or greater than the first threshold value. On the other hand, in the graph of FIG. 6(b), the latest first measurement value itself is the same value (n) as in FIG. 6(a), but the log data value of the first measurement value is higher overall compared to FIG. 6(a). In such a case, if the first threshold value is calculated as described above, the value of the first threshold value will be higher. Therefore, as shown in the graph, in the case of FIG. 6(b), even if the output amplitude value is the same, the first threshold value is higher, and therefore it is determined that there is no abnormality.

[0083] According to the estimation system 100, 200, or 300 of this embodiment, when various measurement values ​​of a user attempting to open the door 10 by gripping the doorknob 11 are significantly different from the measurement values ​​when the user performed a similar action within a recent predetermined period, it is determined that the user has an abnormality in his / her health condition. This allows the estimation system 100, 200, or 300 to estimate the health condition by taking into account the measurement values ​​of the user in normal times. Therefore, the estimation accuracy of the health condition in the estimation system 100, 200, or 300 can be improved.

[0084] Fifth embodiment The estimation system 200 or 300 according to the present invention may digitize the results of the first to third judgments and weight each of the digitized results to derive an overall estimation result. When digitizing the results of each judgment, the numerical value may be determined based on how far the numerical value is from the threshold value, not just on the magnitude relationship with the threshold value. The coefficient for the weighting described above may be determined appropriately. A fifth embodiment of the present invention will be described below. The numerical value of each judgment result will be referred to as a "score" hereinafter.

[0085] For example, the results of the first to third judgments can be quantified as follows. First, if the first to third judgments each individually result in a judgment of "no abnormality" (i.e., if the first measurement value is equal to or less than the first threshold value, the second measurement value is equal to or greater than the second threshold value, and the third measurement value is equal to or greater than the third threshold value), the estimation unit 26 or 27 assigns a score of "0" to these judgments. On the other hand, if the first to third judgments each individually result in a judgment of "abnormality", the estimation unit 26 or 27 assigns a score of "1" to these judgments.

[0086] Next, the estimation unit 26 or 27 weights each score. Specifically, the scores of each judgment are multiplied by a weighting coefficient according to each judgment and summed up. The estimation units 26 and 27 estimate the user's health condition according to the value of the score obtained by multiplication and summing up (hereinafter referred to as the final score). For example, when the summed up score is equal to or greater than a predetermined value, the estimation unit 26 or 27 derives the estimation result that "there is something wrong with the health condition". Note that each of the weighting coefficients may be a value that can be changed as appropriate.

[0087] If each of the first to third judgments is judged as "abnormal" by itself (i.e., if the first measurement value is greater than the first threshold value, the second measurement value is less than the second threshold value, and the third measurement value is less than the third threshold value), the estimation unit 26 or 27 may determine the score for each judgment depending on the absolute value of the difference between the received first to third measurement values ​​and the first to third threshold values ​​corresponding to each measurement value. At this time, the larger the absolute value of the difference, the larger the score may be assigned. In this case, the weighting by the estimation unit 26 or 27 thereafter and the method of determining the final estimation result are the same.

[0088] According to the estimation system 200 or 300 of the present embodiment, the health state can be estimated by taking into consideration the importance of each of the results of the first, second, and third judgments. Therefore, according to the estimation system 100, 200, or 300 of the present embodiment, it is possible to perform estimation with higher accuracy or more suited to the situation.

[0089] <<Modification of Estimation Process>> In the first to fifth embodiments described above, whether or not there is an abnormality in the health state is estimated based on the simple magnitude relationship between various measurement values ​​(first to third measurement values) and a threshold value. However, the estimation systems 100, 200, and 300 according to the present invention may estimate an index value of the degree of goodness of the health state based on various measurement values. For example, the estimation systems 100, 200, and 300 may estimate the health state on a nine-level scale from 1 (worst) to 9 (best).

[0090] Furthermore, when such index values ​​are calculated in the estimation systems 100, 200, and 300, the estimation units 22, 26, and 27 may determine the aforementioned index values ​​depending on how far the various measured values ​​deviate from the thresholds corresponding to the measured values. For example, the index values ​​are estimated at the aforementioned nine levels. In this case, for example, when the first measured value is less than 0.7 times the first threshold, the index value may be set to 1, when the first measured value is 0.7 times or more and less than 1.0 times the first threshold, the index value may be set to 2, when the first measured value is 1.0 times or more and less than 1.1 times the first threshold, the index value may be set to 3, and so on. When estimating the index value, the server 20 may transmit the index value to the mobile terminal 30, and the mobile terminal 30 may include information indicating the index value in a notification.

[0091] Sixth Embodiment In the estimation systems 100, 200, and 300 according to the fourth embodiment, a trained model for estimating a health condition may be created by machine learning. For example, the estimation system 100, 200, or 300 may create a trained model that outputs an index value indicating the presence or absence of an abnormality in the health condition or the degree of goodness of the health condition when at least one parameter among the first to third measurement values ​​is input. In addition, the estimation unit 22, 26, or 27 of the estimation systems 100, 200, and 300 according to the fourth embodiment may estimate the health condition of the user using the trained model. Hereinafter, a sixth embodiment of the present invention will be described.

[0092] First, an example of a method for creating a trained model will be described. In the following, as an example, a case where a trained model is created by supervised learning will be described. However, the method for constructing a trained model is not limited to this. In the following, as an example, a case where the estimation system 300 constructs a learning model in which "the first to third measurement values ​​are input parameters (explanatory variables) and an index value indicating the degree of good health is the output parameter (objective variable)" will be described. However, the method described below is also applicable to the estimation systems 100 and 200. In addition, instead of the "degree of good health", an index value indicating the degree of deterioration of a specific disease may be the output parameter.

[0093] The estimation system 300 according to the present embodiment includes a calculation device such as a personal computer (PC). The server 20 may also function as the calculation device. The calculation device has a storage unit, and an unlearned learning model is stored in the storage unit. The algorithm of the learning model is not particularly limited. For example, a neural network is an example of the algorithm of the learning model. The designer or user of the estimation system 300 acquires big data indicating the relationship between the measurement value and the health condition. Here, the "big data indicating the relationship between the measurement value and the health condition" is, for example, a data group in which an infinite number of data are accumulated in which the first to third measurement values ​​are associated with the degree of goodness of the health condition (for example, the above-mentioned nine levels). The calculation device uses a part or all of the big data to cause the learning model to perform machine learning (supervised learning). That is, a part or all of the big data becomes teacher data for machine learning.

[0094] The big data may include data published by hospitals and / or public institutions. The big data may also include data that associates the first to third measurement values ​​of a certain user measured by equipment substantially similar to the doorknob 10 with an index value of the degree of good health condition self-reported by the certain user. By using the machine learning described above, it is possible to construct a trained model that outputs an index value indicating the degree of good health condition when the first to third measurement values ​​are input.

[0095] The trained model created in this manner is downloaded to the server 20 directly or indirectly using a recording medium or the like. The memory unit 24 of the server 20 stores the trained model. When the estimation unit 27 of the server 20 acquires the first to third measurement values, it inputs the first to third measurement values ​​to the trained model in the memory unit 24. The trained model outputs an index value indicating the degree of goodness of the health condition according to the input first to third measurement values. The estimation unit 27 regards this output result as the estimation result of the health condition. In other words, the control unit 25 notifies the mobile terminal 30 of information indicating the output result. According to the above process, the degree of goodness of the health condition can be estimated from the first to third measurement values.

[0096] [Modifications] <<Modification of the entire system>> The door 10 may not be a door of a building, but may be a door attached to a moving body such as a vehicle. For example, if the door 10 is a door for a vehicle, the doorknob 11 may be a grip-type doorknob. If the doorknob 11 is a doorknob for a vehicle door, the server 20 may be integrated with various in-vehicle devices. In this case, a notification based on the estimation result of the estimation unit 22, 26, or 27 may be output from an output unit such as a display unit and / or an audio output unit as an in-vehicle device, instead of the mobile terminal 30.

[0097] Furthermore, the gripping member 11 may be a member other than a doorknob, so long as it can be equipped with essential members such as the acceleration sensor 13. For example, the gripping member may be a bowl, plate, chopsticks, spoon, fork, toothbrush, or the like that is routinely held by a user. The gripping member may also be a handrail on a staircase, in a bath, in a toilet, or the like. In these cases, the various sensors may be appropriately arranged according to the shape of the gripping member 11 and / or the shape of the user's hand when gripping the gripping member 11.

[0098] Furthermore, a part of the processing executed by the server 20 in the estimation systems 100, 200, and 300 may be executed by the user's mobile terminal 30. In this case, the various data stored in the storage unit 24 may be stored in the storage unit of the mobile terminal 30 or in a cloud server connected via the Internet.

[0099] In the above-described embodiments, the fingerprint data and parameters indicating hand tremors, grip strength, force and / or speed of turning the doorknob 11, etc. are detected and measured by separate sensors. However, sensors capable of simultaneously measuring a plurality of types of data may be used as various sensors of the estimation system according to the present invention. For example, if a vector sensor capable of measuring translational forces in three directions is employed, the vector sensor can function both as the acceleration sensor 13 and the pressure sensor 14. Alternatively, if a six-axis sensor (i.e., a sensor capable of measuring three-axis acceleration and three-axis gyro) is employed, the acceleration sensor 13 and the torque sensor 17 can be used. This allows the number of parts mounted on the gripping member to be reduced.

[0100] User Identification Modifications In addition, in each of the above-described embodiments, the user is identified by fingerprint authentication. However, the estimation systems 100, 200, and 300 may identify the user by means other than fingerprint authentication. For example, the estimation systems 100, 200, and 300 may identify the user by face authentication. In this case, a camera is attached to the door 10 or near the door 10 at an angle that allows the face of the user to be photographed when the user grasps the doorknob 11. When the camera detects the approach of a person using a human sensor or the like, it photographs the person's face and outputs the photograph to the control unit 15. The control unit 15 transmits the image obtained from the camera to the control unit 25 of the server 20. The identification unit 23 of the server 20 identifies the user who grasps the doorknob 11 by collating the received image (face image) with the face images of each user registered in advance in the storage unit 24 (face authentication). In this case, it may also be determined from the camera image whether the user opened the door with the left or right hand. In this way, by identifying whether the hand gripping the doorknob 11 is the right hand or the left hand from an image or the like, it is possible to refer to the correct left-right data when performing fingerprint authentication and estimating the health condition.

[0101] Alternatively, the estimation systems 100, 200, and 300 may include a camera that captures an image of the user's hands holding the doorknob 11. In this case, when the camera detects the user's gripping of the doorknob 11 using a motion sensor or the like, it captures an image of the user's hands and transmits the captured image to the control unit 15. The control unit 15 transmits the captured image to the control unit 25 of the server 20. The control unit 25 identifies whether the user is holding the doorknob 11 with their left or right hand from the received captured image. In this way, by identifying whether the hand holding the doorknob 11 is the right hand or the left hand from the image or the like, the correct left and right data can be referenced when performing fingerprint authentication and estimating the health condition.

[0102] <Modifications of Sensor and Estimation Process> The estimation systems 100, 200, and 300 may include sensors other than the above-mentioned sensors. The estimation process in the estimation unit 22, 26, or 27 may be performed taking into account the measurement value of the sensor. For example, the doorknob 11 of the estimation systems 100, 200, and 300 may include an electrostatic sensor and / or a temperature sensor. The electrostatic sensor and / or the temperature sensor outputs a measurement value to the control unit 15, similar to the acceleration sensor 13, etc. The control unit 15 transmits the acquired measurement value to the control unit 25 of the server 20. The control unit 25 derives an estimation result by taking into account these measurement values ​​during the estimation process in the estimation unit 22, 26, or 27. For example, similar to the first measurement value, etc., a threshold value may be stored in the storage unit 24 for these measurement values, and the health condition of the user may be determined by integrating the comparison result between the threshold value and the measurement value and the comparison results between the first to third measurement values ​​and the first to third measurement values ​​described in each of the above-mentioned embodiments.

[0103] For example, the value of the electrostatic sensor is a value related to the amount and composition of sweat of the user holding the doorknob 11. Moreover, the value of the temperature sensor is a value related to the body temperature of the user holding the doorknob 11. Therefore, even if these measured values ​​indicate abnormal values, it can be estimated that the user has some kind of disease or illness. Therefore, the estimation systems 100, 200, and 300 can estimate the health condition more accurately by taking these measured values ​​into account when estimating the health condition.

[0104] Although the embodiments of the present invention have been described above, the present invention is not limited to the estimation systems 100, 200, and 300 according to the above-mentioned embodiments and their modifications, but includes all aspects included in the concept of the present invention and the scope of the claims. In addition, each configuration may be appropriately and selectively combined so as to achieve at least a part of the above-mentioned problems and effects. For example, each configuration in the above-mentioned embodiments may be appropriately changed depending on the specific use mode of the present invention. [Explanation of symbols]

[0105] 100, 200, 300... estimation system, 10... door, 11... doorknob (gripping member), 12... fingerprint sensor (biometric information sensor), 13... acceleration sensor (first sensor), 14... pressure sensor (second sensor), 15... control unit, 16... communication unit (gripping member transmission unit), 17... torque sensor (third sensor), 20... server (control device), 21... communication unit (transmission unit), 22, 26, 27... estimation unit, 23... identification unit, 24... memory unit, 25... control unit (acquisition unit), 30... mobile terminal (terminal device), 31... communication unit, 32... control unit, 33... output unit

Claims

1. an acquisition unit that acquires, from a first sensor provided on a gripping member that is a member gripped by a user, a first measurement value related to the tremor of the user's hand measured by the first sensor; an estimation unit that estimates a health state of the user based on the first measurement value.

2. the acquisition unit acquires biometric information specific to the user detected by a biometric information sensor provided on the gripping member, the control device includes an identification unit that identifies the user based on the biometric information; 2. The control device according to claim 1, wherein the estimation unit derives a first threshold value based on log data of the first measurement value for the user identified by the identification unit, and estimates the health state of the user based on a comparison result between the first measurement value and the first threshold value.

3. the acquisition unit acquires, from a second sensor provided on the grip member, a second measurement value related to the grip strength of the user's hand acquired by the second sensor; The control device according to claim 1 , wherein the estimation unit estimates the health state of the user based on the second measurement value.

4. the acquisition unit acquires biometric information specific to the user detected by a biometric information sensor provided on the gripping member, the control device includes an identification unit that identifies the user based on the biometric information; 4. The control device according to claim 3, wherein the estimation unit derives a second threshold value based on log data of the second measurement value for the user identified by the identification unit, and estimates the health state of the user based on a comparison result between the second measurement value and the second threshold value.

5. the acquisition unit acquires, from a third sensor provided on the gripping member, a third measurement value acquired by the third sensor, relating to a force causing the gripping member to rotate and / or a rotation speed; The control device according to claim 3 , wherein the estimation unit estimates the health state of the user based on the third measurement value.

6. the acquisition unit acquires biometric information specific to the user detected by a biometric information sensor provided on the gripping member, the control device includes an identification unit that identifies the user based on the biometric information; 6. The control device according to claim 5, wherein the estimation unit derives a third threshold value based on log data of the third measurement value for the user identified by the identification unit, and estimates the health state of the user based on a comparison result between the third measurement value and the third threshold value.

7. The estimation unit quantifying the result of the estimation based on the first measurement value, the result of the estimation based on the second measurement value, and / or the result of the estimation based on the third measurement value; 6. The control device according to claim 5, wherein the results of each of the estimations converted into numerical values ​​are weighted to derive a comprehensive estimation result.

8. The control device according to claim 1 , further comprising a transmitting unit that transmits information based on the estimation result of said estimating unit to a terminal device carried by said user.

9. A gripping member to be gripped by a user, a first sensor for measuring a first measurement related to the user's hand tremor; A gripping member that transmits the first measurement value to the control device of claim 1. A gripping member comprising: a transmitter.

10. The control device according to claim 1 ; a terminal device that receives information based on the estimation result from the transmission unit of the control device and outputs a notification according to the information; An estimation system, comprising: a gripping member according to claim 9.

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