JP2024034109A - Control apparatus, grip member, and estimation system - Google Patents

Abstract

PROBLEM TO BE SOLVED: 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

COPYRIGHT: (C)2024,JPO&INPIT

Description

The present invention relates to a control device, a gripping member, an estimation system, and the like.

BACKGROUND OF THE INVENTION Due to the recent increase in demand for health, technology has been developed to monitor the user's daily health status using, for example, a wristwatch-type wearable device. Specifically, technology has been developed that uses sensors installed in wearable devices to acquire biological information such as a user's daily heart rate and blood oxygen saturation. For example, Patent Document 1 discloses a technique for acquiring biological information such as heartbeat, body temperature, blood pressure, and body tremors using a wearable device or the like.

International Publication No. 2018/168369

However, in the technique described in Patent Document 1, in order to acquire biometric information, it is necessary for the user to wear a wearable device. That is, with the technique described in Patent Document 1, biometric information cannot be acquired if the user forgets to wear the wearable device.

The present invention has been made in view of the above problems, and an object of the present invention is to estimate a user's health condition based on the user's daily activities.

In order to achieve the above object, the control device according to the present invention provides information on the tremor of the user's hand acquired by the first sensor from a first sensor provided on a gripping member that is a member gripped by the user. The device includes an acquisition unit that acquires one measurement value, and an estimation unit that estimates the health condition of the user based on the first measurement value.

According to the present invention, the user's health condition can be estimated based on the user's daily activities.

FIG. 1 is a functional block diagram schematically showing the configuration of an estimation system according to a first embodiment. FIG. 2 is a side view schematically showing the structure of a part of the door according to the first embodiment. FIG. 2 is a sequence diagram illustrating an example of a processing flow of the estimation system according to the first embodiment. FIG. 2 is a functional block diagram schematically showing the configuration of an estimation system according to a second embodiment. It is a functional block diagram schematically showing the configuration of an estimation system according to a third embodiment. It is a graph which shows the relationship between the log data of a 1st measurement value, and a 1st threshold value.

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

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

The doorknob 11 is a member provided on the door 10 and serves as a gripping member for 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 (biological information sensor) 12 , an acceleration sensor (first sensor) 13 , a control section 15 , and a communication section (gripping member transmitting section) 16 . Note that the doorknob 11 may include a storage section that stores data necessary for the operation of the control section 15 and the like.

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

The acceleration sensor 13 measures the acceleration when the user grasps the doorknob 11 and opens 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 acceleration measurement results for a predetermined period of time (for example, 3 seconds). Alternatively, the acceleration sensor 13 may measure the period (ie, frequency) of vibration. The acceleration sensor 13 outputs the calculated or measured output amplitude value (first measurement value) to the control unit 15. Note that in this embodiment, the acceleration sensor 13 calculates the value of the output amplitude.
Generally, the value of the output amplitude of an acceleration sensor increases depending on the magnitude of vibration transmitted to the sensor. The output frequency of the acceleration sensor is the number of vibrations per unit time. Therefore, it can be said that the output amplitude value and frequency of the acceleration sensor 13 provided on the doorknob 11 indicate whether or not the user's hand trembles, the magnitude of the tremor, 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 measured values regarding the tremor of the user's hands. Note that in this specification, a "measured value" may include not only a value measured by a sensor but also a value calculated from a measured value.

The control unit 15 acquires 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 through which the control unit 15 communicates with the server 20 . The communication unit 16 transmits various data output from the control unit 15 to the server 20. Note that in this embodiment, the control section 15 and the communication section 16 are incorporated into 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, in the door body).

The server 20 is a management device that manages various data received from the control unit 15. Further, the server 20 is an estimation device that estimates the user's health condition 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. Note that when the estimation system 100 includes the mobile terminal 30, the server 20 is also connected to the mobile terminal 30 via a communication network.

The installation position of the server 20 is not particularly limited. For example, the server 20 may be placed indoors near the door. Further, the server 20 may be a server (or a virtual server) existing in a cloud environment. In this embodiment, as an example, it is assumed that the server 20 is placed in a room partitioned off by a door 10. Note that the server 20 may be a device that centrally manages and controls other devices such as home devices. The server 20 includes a communication section (transmission section) 21, a control section 25 (obtainment section), and a storage section 24.

The communication unit 21 communicates with the communication unit 16 of the doorknob 11. Note that when the estimation system 100 includes the 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 network type between the communication unit 21 and the communication unit 31 of the mobile terminal 30 are not particularly limited either.

The control unit 25 is a CPU (central processing unit) that controls the server 20 in an integrated manner. The control unit 25 includes an estimation unit 22 and an identification unit 23. The control unit 25 reads a software program corresponding to the function of the estimation unit 22 and the identification unit 23 from the storage unit 24 and executes it. Thereby, the control unit 25 can realize the estimation unit 22 and the identification unit 23 as functional blocks.

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 specify whether the received fingerprint data indicates the fingerprint of the right or left hand. For example, the identification unit 23 may identify, from the shape of the fingerprint indicated by the received fingerprint data, whether the fingerprint data indicates the fingerprint of the right or left hand.

The estimation unit 22 estimates the user's health condition based on at least the first measurement value. In addition. In this embodiment, "estimate 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, the details of the abnormality (ie, symptoms and disease name) and/or the degree of the abnormality may be estimated if there is an abnormality. The estimation process in the estimation unit 22 will be described in detail later.

The control unit 25 may cause the storage unit 24 to store the estimation result of the estimation unit 22 (for example, information indicating whether or not there is an abnormality in the user's health condition). Further, when the estimation system 100 includes the 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.

Here, "information based on the estimation result" may be information indicating the estimation result itself, or may be information created secondarily from the estimation result. For example, "information based on the estimation result" may be a code according to the estimation result, or text, image (or video), and/or audio data of a notification created based on the estimation result. Moreover, "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 an abnormality in the health condition, the information based on the estimation result may be text indicating that the health condition is abnormal or recommending that you go to the hospital. It may be text indicating that.

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

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

Note that which finger's registered fingerprint data of the left or right hand (or both hands) is to be stored in the storage unit 24 is determined as appropriate depending on the placement position of the detection surface of the fingerprint sensor 12 on the doorknob 11. good. For example, assume that the detection surface of the fingerprint sensor 12 is placed at a position where the user's index finger hits when the user grips the doorknob 11. In this case, it is desirable that the storage unit 24 stores fingerprint data of the index fingers of the left and right hands as the registered fingerprint data. Each record of the identification data table may be further associated with identification information of the mobile terminal 30. The identification information of the mobile terminal 30 is, for example, a unique number of the mobile terminal 30, a MAC address, a mobile phone number, and/or an email address.

In this embodiment, as an example, the identification data table is a table in which registered fingerprint data is associated with user identification information. However, a fingerprint is also user identification information. Therefore, the registered fingerprint data may also serve as user identification information. That is, the storage unit 24 may store only the registered fingerprint data or data in which the registered fingerprint data and the identification information of the mobile terminal 30 are associated with each other instead of the identification data table. Furthermore, the identification data table is not limited to the table configuration described above. For example, the storage unit 24 may separately store a table that associates user identification information with registered fingerprint data and a table that associates user identification information with identification information of the mobile terminal 30. Further, the identification data table does not necessarily have to be in the form of a data table.

The storage unit 24 may further store the user's identification information and the received first measurement value in association with each other. 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 grasps the doorknob 11. The period for recording the log data and the period for retaining the records are not particularly limited. However, it is preferable that the period for recording the log data and the period for retaining the record be for a certain long period of time, such as one year or more. The recording timing, recording period, and retention period of log data are the same for various types of log data in subsequent embodiments.

The storage unit 24 may further store various threshold values necessary for the estimation unit 22 to estimate the health state. In the case of this embodiment, the first threshold value is stored in the storage unit 24. The first threshold is the threshold of the first measurement value. The first threshold value is such that the degree of hand tremor indicated by the first measurement value is within the range of the user's normal health condition or exceeds the degree of tremor (i.e., the degree of hand tremor is within the range of the user's normal health condition). This is the threshold for the degree of tremor (at which an abnormality has occurred). The first threshold value may be determined as appropriate depending on the arrangement position of the acceleration sensor 13, detection accuracy, and the like.

Note that the first threshold value may be set to a different value for each user, or may be a value common to all users. Note that when setting a different value for each user, the storage unit 24 stores the user identification information and the first threshold value corresponding to the user identification information in association with each other. In this case, the first threshold value may be determined according to user information (age, gender, height, weight, etc.) registered in the storage unit 24 in advance. This makes it possible, for example, to change the threshold for weak elderly people and young people. Further, a first threshold value for the right hand and a first threshold value for the left hand may be set separately. In general, it is thought that hand tremors are 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 values for each of the left and right hands, it is possible to more accurately determine whether the tremor of the user's hand is such that it can occur within the range of normal health conditions. Can be done.

The mobile terminal 30 is a terminal device carried by a user, such as a smartphone or a smart watch. The mobile terminal 30 is a device for notifying the user according to the estimation result of the server 20. Mobile terminal 30 includes a communication section 31, a control section 32, and an output section 33. Note that the mobile terminal 30 may include a storage section that stores data necessary for the operation of the mobile terminal 30.

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 integrated 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 as is or after performing various conversions according to the output format of the output unit 33 . The output unit 33 performs output under 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. Note that 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.

The server 20 may be able to communicate with a plurality of control units 15 . Further, the server 20 may be able to communicate with a plurality of mobile terminals 30. In this case, the identification unit 23 of the server 20 identifies the user who gripped each doorknob 11 based on the fingerprint data received from each control unit 15. Further, the estimation unit 22 executes estimation processing based on the first measurement value received from each control unit 15, and the control unit 25 sends information based on the estimation result to the mobile terminal 30 associated with each identified user. You can also send it.

《Sensor placement》
Next, details of the structure of the doorknob 11 and examples of arrangement of various sensors will be explained using FIG. 2. FIG. 2 is a side view schematically showing the structure of a part of the door 10. As shown in FIG. 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. Further, although mechanisms not related to the estimation system 100, such as the locking mechanism of the door 10, are not shown, the door 10 may have the structure and functions normally provided as a door. In the following description, the direction in which the user who opens the door 10 is located (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 located (the right side in FIG. 2) is referred to as the "inside". .

The door 10 has a doorknob 11 attached to a door body 10a. The doorknob 11 includes, for example, a gripping portion 11a that extends in the vertical direction, and a pair of attachment portions 11b that are continuous from the upper and lower ends of the gripping portion 11a. The doorknob 11 is attached to the door body 10a with a pair of attachment parts 11b. By attaching the grip portion 11a and the pair of attachment portions 11b to the door body 10a in such a shape, a predetermined space is secured between the grip portion 11a and the door body 10a. The user can insert his/her hand into this space and grasp the grip portion 11a.

A push-in portion 11c is provided on the inner surface of the grip portion 11a (the surface facing the door 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 grasps the grip portion 11a when opening the door 10, the push-in portion 11c is pushed in by the user's fingers. In the door 10 shown in FIG. 2, by pushing the push-in portion 11c in this way, the door 10 is unlocked and the door 10 can be opened.

A fingerprint sensor 12 is incorporated into the outer surface of the grip portion 11a of the doorknob 11. In this embodiment, the detection surface of the fingerprint sensor 12 is arranged at a position where the user's thumb is expected to touch when the user grips the grip portion 11a of the doorknob 11. Thereby, the fingerprint sensor 12 can detect the fingerprint of the thumb of the user who grips the doorknob 11. An acceleration sensor 13 is incorporated inside the grip portion 11a. If the acceleration sensor 13 can detect the acceleration when the user grips the grip portion 11a of the doorknob 11 (that is, the acceleration of pulling the door plus the magnitude and frequency of the vibration), is not particularly limited.

《Processing flow》
Next, a series of processing steps for the estimation system 100 to estimate the user's health condition will be explained. FIG. 3 is a sequence diagram showing an example of the processing flow of the estimation system 100 according to the first embodiment. A user opens and closes the door 10 at home, work, etc. as a daily action. When opening and closing the door 10, the grip portion 11a of the doorknob 11 is gripped by the user.

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 disposed on the door body 10a side of the grip portion 11a measures the acceleration, and calculates the output amplitude value of the acceleration (that is, the value related to the tremor of the user's hand) (step S11). Further, 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 user's fingerprint pressed against the detection surface (step S12). The acceleration sensor 13 and the fingerprint sensor 12 each output an output amplitude value and fingerprint data to the control unit 15. The control unit 15 transmits the input output amplitude value and fingerprint data to the server 20 (step S13). Note that step S11 and step S12 may be performed in any order and may be performed in parallel.

The control unit 25 of the server 20 receives the output amplitude value and fingerprint data from the control unit 15 (step S21). The identification unit 23 of the control unit 25 identifies the user who grips the doorknob 11 by performing fingerprint authentication based on the fingerprint data (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. Note that at this time, the identification unit 23 may specify whether the received fingerprint data belongs to the right or left hand. If some of 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 gripped the doorknob 11 ( In other words, it is determined that the user identification was successful). On the other hand, if there is no registered fingerprint data that matches the received fingerprint data, the control unit 25 determines that user identification has failed.

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. Note that if the user identification fails in the identification unit 23 (NO in step S23), the server 20 does not execute the subsequent steps and ends the series of processing. Note that 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.

In this embodiment, the estimation unit 22 refers to the first threshold value stored in the storage unit 24 and compares the output amplitude value with the first threshold value. Note that if the first threshold values corresponding to the left and right hands of the user are set, the estimation unit 22 refers to the first threshold values corresponding to the hand specified by the identification unit 23. If the output amplitude value is less than or equal to the first threshold, the estimation unit 22 estimates that there is no abnormality in the user's health condition. On the other hand, if the output amplitude value is larger than the first threshold value, the estimation unit 22 determines that the user's health condition is abnormal.

As a result of the estimation, if it is estimated that the user's health condition is abnormal (YES in step S25), the control unit 25 transmits information based on the estimation result to the mobile terminal 30 (step S26). Note that 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 in step S26 and ends the series of processes.

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 according to 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 display the above-mentioned notification, ``An abnormality was observed in the movement of your hand'' or ``You may be unwell.'' You may also display a warning message such as

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 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 your health condition" on the display. Furthermore, in the case where the estimation system 100 does not include the mobile terminal 30, the estimation system 100 does not need to perform step S26, step S31, and step S32.

According to the process shown in FIG. 3, the user's health condition is estimated when the user grips the doorknob 11. If it is estimated that the user's health condition is abnormal, a notification is output from the user's mobile terminal 30. In this way, the estimation system 100 can obtain a first measurement value that can estimate the user's health condition from the user's daily action of “opening and closing the door.” Then, the user's health condition can be estimated based on the first measurement value. Therefore, according to the estimation system 100, the user's health condition can be estimated based on the user's daily activities.

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 tremor of the user's hands. It is generally said that human hand tremors are likely to appear as an early symptom of various diseases. For example, various diseases that are thought to cause hand tremors include essential tremor, Parkinson's disease, multiple sclerosis, hyperthyroidism, alcoholism, and drug-induced tremor. Further, even if the user does not have such a disease, if the user is physically unwell, the user may experience tremors in the hands and fingers. For example, even if you have a simple cold, it is quite possible that you may be unable to use your hands and tremble due to physical discomfort. The estimation system 100 according to the present embodiment estimates whether there is an abnormality in the user's health condition each time based on daily activities (opening/closing a door) that the user would perform every day. Therefore, if there is a possibility that the user is suffering from various disorders including the above-mentioned diseases, this possibility can be discovered at an early stage.

[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 that is data regarding the grip strength of the user's hand, which is acquired by the second sensor. The estimation system may then estimate the user's health condition 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 subsequent embodiments including this embodiment, descriptions of the same configurations and processes as in the first embodiment will not be repeated.

FIG. 4 is a functional block diagram schematically showing the configuration of the estimation system 200 according to this embodiment. The estimation system 200 differs from the estimation system 100 in that the doorknob 11 is equipped 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. There is.

The pressure sensor 14 measures the pressure applied to the gripped 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 strain gauge. The pressure sensor 14 outputs a value indicating the measurement result (second measurement value) to the control unit 15. In other words, the pressure sensor 14 can be said to measure the force with which the user grips the doorknob, that is, the user's grip strength. Therefore, the second measurement value is a value indicating the user's grip strength. Note that the pressure sensor 14 may measure the pressure for a predetermined period of time (for example, 3 seconds) when the user grasps the doorknob 11 and opens the door 10 . In this case, the pressure sensor 14 outputs the measurement result for the aforementioned predetermined time as the second measurement value. Therefore, by analyzing the measurement results of the pressure sensor 14 over time, it is possible to identify minute fluctuations in the user's grip strength.

The position of the pressure sensor 14 on the doorknob 11 is not particularly limited. For example, if 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. Note that the size of the detection surface of the pressure sensor 14 is not particularly limited, but if the doorknob 11 has the same shape as in FIG. ), the length may be approximately the width of four fingers from the index finger to the little finger. As a result, the pressure sensor 14 detects the pressure applied to the detection surface, that is, the user's grip force when the user grips the grip portion 11a (as well as fluctuations in grip force, from which part of the hand the grip portion 11a receives force, etc.). ) can be measured stably. In addition, in the case of this embodiment, the acceleration sensor 13 may be incorporated on the back side (that is, inside) of the detection surface of the pressure sensor 14 in the grip portion 11a. Thereby, the acceleration sensor 13 can also measure acceleration at the same time as the pressure sensor 14 measures the grip force.

The control unit 15 according to the present embodiment acquires the second measured 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 this 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.

The storage unit 24 according to this embodiment stores a second threshold in addition to the first threshold. The second threshold is a threshold of the second measurement value. The second threshold value is that the grip strength indicated by the second measurement value is within the range of the user's normal health condition or exceeds the range (that is, an abnormality has occurred in the user's health condition). This is the threshold of grip strength. The second threshold value may be determined as appropriate depending on the arrangement position of the pressure sensor 14, detection accuracy, and the like.

Note that the second threshold value may be set to a different value for each user, or may be a value common to all users. Note that when setting a different value for each user, the storage unit 24 stores the user identification information and the second threshold value 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, gender, height, weight, etc.) registered in the storage unit 24 in advance. This makes it possible, for example, to change the second threshold for weak elderly people and young people. Further, a second threshold value for the right hand and a second threshold value for the left hand may be separately set. In general, it is thought that grip strength is smaller 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 second threshold values for each of the left and right hands, it is possible to more accurately determine whether the user's grip strength is within the normal range of health. Note that the storage unit 24 may store the user's identification information and the received first measurement value and second measurement value in association with each other. That is, the storage unit 24 may record log data of the first measurement value and the second measurement value for each user.

《Function of estimation unit 26》
The basic function of the estimation section 26 is similar to that of the estimation section 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 mentioned above, the first measured value is data related to hand tremor, and the second measured value is measured value related to hand grip strength. The estimation unit 26 determines the user's health condition based on each of these two parameters. In addition, in the estimation unit 26, the determination of the health condition based on the first measurement value (hereinafter referred to as the first determination) and the determination of the health condition based on the second measurement value (hereinafter referred to as the second determination) are performed in a random order. , or in parallel. The estimation unit integrates these two determination results and finally estimates the estimation result of the user's health condition.

The estimation unit 26 may perform the first determination using a method similar to step S24 of 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 the 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. Note that when second thresholds corresponding to the left and right hands of the user are set, the estimation unit 26 refers to the second thresholds corresponding to the hand specified by the identification unit 23. If the second measured value is equal to or greater than the second threshold, the estimation unit 26 estimates that there is no abnormality in the user's health condition. On the other hand, if the second measured value is less than the second threshold, the estimation unit 26 determines that the user's health condition is abnormal.

When the first determination result and the second determination result are obtained, the estimation unit 26 derives the final estimation result. For example, when it is determined that "the user's health condition is abnormal" in both the first determination and the second determination, 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 an abnormality in both the degree of hand tremor and grip strength is likely to have an abnormality in his/her health condition. According to the estimation system 200, it is possible to discover that there is an abnormality in the user's health condition in such cases.

For example, if it is determined in either the first determination or the second determination 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. It may be determined that there is an abnormality. A user who is determined to have an abnormal degree of hand tremor or grip strength may have an abnormal health condition. According to the estimation system 200, it is possible to discover that there is an abnormality in the user's health condition in such cases.

Note that the estimation unit 26 may simply determine whether the individual results of the first determination and the second determination are greater than or equal to various threshold values, and may not determine whether there is an abnormality in the health condition. . In this case, the estimation unit 26 integrates the comparison result between the first measurement value and the first threshold value in the first determination and the comparison result between the second measurement value and the second threshold value in the second determination, and calculates the health status of the user. All you have to do is estimate.

According to this embodiment, the estimation system 200 can estimate the user's health condition according to the first measurement value and the second measurement value. Therefore, according to the estimation system 200, the user's health condition can be estimated with higher accuracy (or in more detail).

In this embodiment, the second measurement value used to estimate the health condition is a measurement value related to the grip strength of the user's hand. Like hand tremor, decreased hand grip strength is likely to appear as an early symptom of various diseases. For example, research shows that people with decreased grip strength have an increased risk of myocardial infarction and stroke, as well as increased mortality from heart-related diseases. Further, even if the user does not have such a disease, if the user is physically unwell, the grip strength may decrease. The estimation system 200 according to the present embodiment estimates the user's health condition by also taking into account the user's grip strength, so it is possible to discover more possibilities of diseases and disorders.

[Third embodiment]
The estimation system according to the present invention may acquire, from a third sensor provided on the gripping member, a third measured value regarding the force and/or rotation speed for rotating the gripping member, which is 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. Moreover, the estimation system may estimate the user's health condition based on the first measurement value, the second measurement value, and the third measurement value.

Hereinafter, an estimation system 300 according to a third embodiment of the present invention will be described. Note that in the drawings and description below, an example will be described in which the estimation system 300 estimates the user's health condition based on the first measurement value, the second measurement value, and the 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 the third measurement value into consideration.

FIG. 5 is a functional block diagram schematically showing the configuration of the estimation system 300 according to this embodiment. The estimation system 300 differs from the estimation systems 100 and 200 in that the doorknob 11 includes 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. It's different.

In this embodiment, the doorknob 11 is not the push-pull type doorknob according to the first and second embodiments, but is a rotary doorknob that opens and closes the door by being held and rotated by the user. For example, the doorknob 11 according to the present embodiment is realized by a doorknob having a rotary grip ball. The fingerprint sensor 12, the pressure sensor 14, and the acceleration sensor 13 are appropriately placed on the rotary doorknob so that each measurement can be performed appropriately. For example, the fingerprint sensor 12 may be placed at a position where the user's thumb touches the doorknob 11 when the user grips the doorknob 11.

The torque sensor 17 is a sensor that measures the force for rotating the doorknob (twisting the doorknob) and/or the rotational speed when the user grips and rotates the doorknob 11 . The torque sensor 17 is configured to measure torsion using, for example, a strain gauge and transmit the signal using a transformer or an optical device. Torque sensor 17 outputs a measured value (third measured value) to control section 15 . Note that the torque sensor 17 does not measure a value at a certain point (that is, a moment), but measures the rotational force for a predetermined period of time (for example, 3 seconds) when the user turns the doorknob 11 to open the door 10. It is also possible to measure and calculate the average value. Then, the torque sensor 17 may output this calculated average value as the third measurement value. The type, location, 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 and/or speed of rotation of the doorknob 11.

Upon acquiring the third measured value of the torque sensor 17, the control unit 15 according to the present embodiment transmits the third measured 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 this 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.

The storage unit 24 according to this embodiment stores the third threshold value. The third threshold is the threshold of the third measurement value. The third threshold value is such that the third measured value (i.e., the force and/or speed with which the user rotates the doorknob 11) is within the user's normal health range or exceeds the range (i.e., the user This is the threshold for whether there is an abnormality in the health status of the patient. The third threshold value may be determined as appropriate depending on the arrangement position of the torque sensor 17, detection accuracy, and the like.

Note that the third threshold value may be set to a different value for each user, or may be a value common to all users. Note that when setting a different value for each user, the storage unit 24 stores the user identification information and the third threshold value 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, gender, height, weight, etc.) registered in the storage unit 24 in advance. This makes it possible, for example, to change the third threshold for weak elderly people and young people. Further, a third threshold value for the right hand and a third threshold value for the left hand may be separately set. Generally, it is considered that the force and/or speed for rotating a doorknob is smaller when twisting the doorknob with the non-dominant hand than when twisting the doorknob with the dominant hand. Therefore, by storing the third threshold values 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 rotates the doorknob 11 is within the range of normal health conditions. be able to.

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

《Function of estimation unit 27》
The basic functions of the estimation section 27 are similar to those of the estimation section 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 it estimates the user's health condition based on the third measurement value as well as the first measurement value. ing. Note that, as described above, the estimation unit 27 may estimate the user's health condition based on the second measurement value as well. Below, a case will be described in which the estimation unit 27 uses these three pieces of data to estimate the health condition. In the estimation unit 27, the first determination, the second determination, and the determination of the health condition based on the third measurement value (hereinafter referred to as the third determination) may be performed in random order or in parallel. The estimation unit integrates these three determination results and finally estimates the estimation result of the user's health condition.

The estimation unit 27 may perform the first determination using a method similar to step S24 of the first embodiment. Furthermore, the estimation unit 27 performs the second determination and the third determination 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 measured value and the third threshold value. Note that when third threshold values corresponding to the left and right hands of the user are set, the estimation unit 27 refers to the third threshold values corresponding to the hand specified by the identification unit 23. If the third measured value is equal to or greater than the third threshold, the estimation unit 27 estimates that there is no abnormality in the user's health condition. On the other hand, if the third measured value is less than the third threshold, the estimation unit 27 determines that the user's health condition is abnormal.

When the results of the first determination, second determination, and third determination are obtained, the estimation unit 27 derives the final estimation result. For example, when it is determined that "the user's health condition is abnormal" in all of the first, second, and third determinations, the estimating unit 27 determines that "the user's health condition is abnormal" as the final estimation result. It may be determined that there is an abnormality in the condition. Users who are judged to have an abnormality, whether it is the degree of hand tremor, the grip strength, or the force and speed with which they twist a doorknob, are highly likely to have an abnormal health condition. . According to the estimation system 300, it is possible to discover that there is an abnormality in the user's health condition in such cases.

For example, when it is determined that "the user's health condition is abnormal" in any one of the first determination, the second determination, and the third determination, the estimation unit 26 may, as the final estimation result, It may also be determined that "the user's health condition is abnormal." A user who is determined to have an abnormality in at least one of the degree of hand tremor, grip strength, and the force and speed of twisting a doorknob may have an abnormal health condition. According to the estimation system 300, it is possible to discover that there is an abnormality in the user's health condition in such cases.

Note that the estimation unit 27 simply determines the magnitude of each of the first determination, second determination, and third determination, and does not determine whether there is an abnormality in the health condition. It may also be a thing. In this case, the estimation unit 27 may estimate the user's health condition by integrating the comparison results between various measured values and various threshold values in each determination.

According to this embodiment, the estimation system 300 can estimate the user's health condition according to the first measurement value and the third measurement value (and the second measurement value). Therefore, according to the estimation system 300, the user's health condition can be estimated with higher accuracy or in more detail.

The third measurement value used to estimate the health condition in this embodiment is a measurement value related to the force and/or speed at which the user rotates the doorknob 11. The strength and/or speed of such twisting motions, like the grip strength of the hand, may be reduced due to various diseases, physical ailments, and the like. The estimation system 300 according to the present embodiment estimates the user's health condition by also considering the user's twisting force and/or speed. Therefore, according to the estimation system 300, more possibilities of diseases and disorders can be found.

Note that in the estimation system 300, the configuration and processing related to the estimation system 200 are not essential. That is, the estimation system 300 does not need to include the pressure sensor 14. Then, the control unit 25 does not need to acquire the second measurement value. Further, the estimation unit 27 does not need to perform the second determination. In this case, the estimation unit 27 may combine the first determination and the third determination to derive the final estimation result.
[Fourth embodiment]

In the present invention, each of the first to third threshold values may be a value derived by the estimation unit according to the log data of the first to third measurement values. That is, the first to third threshold values may be values that fluctuate. A fourth embodiment of the present invention will be described below. 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.

In addition to the functions described in each of the above embodiments, the estimating unit 22, 26, or 27 according to the present embodiment each has a function of calculating a threshold value used for determination from log data. That is, the estimation unit 22 can calculate the first threshold value from the log data of the first measurement value. In addition to the above-mentioned calculation function of the estimation unit 22, the estimation unit 26 has a function of calculating the second threshold value from the log data of the 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 the third threshold value from the log data of the third measurement value. Further, like the estimation unit 26, the estimation unit 27 may have a function of calculating the second threshold value from the log data of the second measurement value.

For example, the estimation unit 22, 26, or 27 refers to various log data in the storage unit 24 and calculates the average value of various measured values for the most recent predetermined period (for example, the most recent one month or one year, etc.). calculate. For the first threshold, the estimator 22, 26, or 27 calculates the first threshold value by multiplying the average value of the first measurement values for the most recent predetermined period by a predetermined coefficient (such as 1.3) that is larger than 1. Set as threshold value. Further, the estimating unit 26 or 27 sets a value obtained by multiplying the average value of the second measurement values for the most recent predetermined period by a predetermined coefficient smaller than 1 (such as 0.7) as the second threshold value. Furthermore, the estimation unit 27 sets a value obtained by multiplying the average value of the third measurement values for the most recent predetermined period by a predetermined coefficient smaller than 1 (such as 0.7) as the third threshold value. Note that these predetermined coefficients may be determined as appropriate.

(a) and (b) of FIG. 6 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, the vertical axis of the graph is the output amplitude value of the acceleration sensor 13, and the horizontal axis is time. Moreover, each plot in the graph is the first measurement value for one record in the log of the first measurement value. In addition, in the graph of FIG. 6, each plot is connected with a polygonal line to make it easier to see the change in values. However, each plot is independent data recorded at each event of door opening/closing, and is not measured continuously over time.

In (a) of FIG. 6, as shown in the graph, the latest output amplitude value (that is, the first measurement value just measured) is greater than or equal to the first threshold value. On the other hand, in the graph of FIG. 6(b), the latest first measured value itself is the same value (n) as in FIG. 6(a), but compared to FIG. 6(a), the first measured value is Log data values are generally high. In such a case, when the first threshold value is calculated as described above, the value of the first threshold value becomes high. Therefore, as shown in the figure, in the case of (b) in FIG. 6, even if the output amplitude is the same value, the first threshold value is high, so it is determined that there is "no abnormality".

According to the estimation system 100, 200, or 300 according to the present embodiment, various measured values of the user who attempted to open the door 10 by grasping the doorknob 11 indicate that the user performed the same action during the most recent predetermined period. If the measured value is significantly different from the measured value, it is determined that the user's health condition is abnormal. Thereby, the estimation system 100, 200, or 300 can estimate the health state by considering the user's normal measurement values. Therefore, the accuracy of estimating the health status in the estimation system 100, 200, or 300 can be improved.

[Fifth embodiment]
The estimation system 200 or 300 according to the present invention may derive a comprehensive estimation result by digitizing the results of the first to third determinations and weighting each of the digitized results. Note that when converting the result of each determination into a numerical value, the numerical value may be determined based not only on the magnitude relationship with the threshold value but also on how far the numerical value is from the threshold value. Furthermore, the coefficients used in the above-mentioned weighting may be determined as appropriate. A fifth embodiment of the present invention will be described below. Note that a numerical value of the results of each determination is hereinafter referred to as a "score."

For example, the results of the first to third determinations can be expressed numerically as follows. First, in the first to third determinations, each individual determination results in "no abnormality" (i.e., if the first measurement value is less than or equal to the first threshold, if the second measurement value is greater than or equal to the second threshold) , and when the third measured value is greater than or equal to the third threshold), the estimation unit 26 or 27 sets the score of these determinations to "0". On the other hand, if it is determined that there is an abnormality in each of the first to third determinations, the estimation unit 26 or 27 sets the score of these determinations to "1".

Subsequently, the estimation unit 26 or 27 weights each score. Specifically, the score of each determination is multiplied by the weighting coefficient corresponding to each determination, and the results are summed. The estimating units 26 and 27 estimate the user's health condition according to the value of the multiplied and summed score (hereinafter referred to as the final score). For example, if the total score is greater than or equal to a predetermined value, an estimation result indicating that there is an abnormality in health condition is derived. Note that each weighting coefficient may be a value that can be changed as appropriate.

In addition, if it is determined that there is an abnormality in each of the first to third determinations, each of the individual determinations (i.e., if the first measurement value is greater than the first threshold value, or if the second measurement value is less than the second threshold value) , and if the third measured value is less than the third threshold), the estimation unit 26 or 27 calculates the difference between the received first to third measured values and the first to third thresholds corresponding to each measured value. The score for each determination may be determined depending on the absolute value of . At this time, the larger the absolute value of the difference, the larger the score may be given. In this case as well, the subsequent weighting of the estimation unit 26 or 27 and the method of determining the final estimation result are the same.

According to the estimation system 200 or 300 according to the present embodiment, the health condition can be estimated after considering the importance of the results of the first, second, and third determinations. can. Therefore, according to the estimation system 100, 200, or 300 according to the present embodiment, it is possible to perform estimation with higher accuracy or more suitable for the situation.

《Modified example of estimation process》
In the first to fifth embodiments described above, it is estimated whether or not there is an abnormality in the health condition based on the mere magnitude relationship between various measured values (first to third measured values) and the threshold value. However, the estimation systems 100, 200, and 300 according to the present invention may estimate the index value of the degree of health condition based on various measured values. For example, estimation systems 100, 200, and 300 may estimate health status at nine levels from 1 (worst) to 9 (best).

In addition, when calculating such index values in the estimation systems 100, 200, and 300, the estimation units 22, 26, and 27 calculate how much the various measured values deviate from the threshold values corresponding to the measured values. The above-mentioned index value may be determined depending on. For example, assume that the index value is estimated at the nine levels described above. In this case, for example, if the first measurement value is less than 0.7 times the first threshold value, the index value is set to 1, if it is 0.7 times or more and less than 1.0 times, the index value is set to 2, and if it is 1.0 times or more, the index value is set to 2. If it is less than 1.1 times, the index value may be set to 3, etc. Furthermore, 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 the notification.

[Sixth embodiment]
In the estimation systems 100, 200, and 300 according to the fourth embodiment, a trained model for estimating a health state may be created by machine learning. For example, when the estimation system 100, 200, or 300 receives at least one parameter among the first to third measured values, the estimation system 100, 200, or 300 outputs an index value indicating the presence or absence of an abnormality in the health condition or the degree of good health condition. You may also create a trained model. Furthermore, the estimation units 22, 26, or 27 of the estimation systems 100, 200, and 300 according to the fourth embodiment may estimate the user's health condition using the above-mentioned learned model. A sixth embodiment of the present invention will be described below.

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

The estimation system 300 according to this embodiment includes an arithmetic device such as a personal computer (PC). Note that the server 20 may also serve as a computing device. The arithmetic device has a storage unit, and an unlearned learning model is stored in the storage unit. The learning model algorithm is not particularly limited. For example, a neural network is an example of a learning model algorithm. A designer or user of the estimation system 300 obtains big data showing the relationship between measured values and health conditions. Here, "big data showing the relationship between measured values and health status" means, for example, that the first to third measured values are associated with the degree of good health status (for example, the nine levels mentioned above). This data is a data group that has been accumulated in countless numbers. The arithmetic device causes the learning model to perform machine learning (supervised learning) using part or all of the big data. In other words, part or all of the big data becomes training data for machine learning.

Note that the big data may include data published by hospitals and/or public institutions. In addition, big data is data that associates the first to third measurement values of a certain user measured with equipment substantially similar to the doorknob 10 and the index value of the good health condition self-reported by the certain user. May contain. Through the machine learning described above, it is possible to construct a learned model that outputs an index value indicating the degree of good health condition when the first to third measured values are input.

The trained model created in this way is downloaded to the server 20 directly or indirectly using a recording medium or the like. The storage unit 24 of the server 20 stores the trained model. Upon acquiring the first to third measured values, the estimation unit 27 of the server 20 inputs the first to third measured values into the learned model in the storage unit 24. The trained model outputs an index value indicating the degree of good health condition according to the input first to third measurement values. The estimation unit 27 uses this output result as the estimation result of the health state. That is, the control unit 25 notifies the mobile terminal 30 of information indicating the output result. According to the above processing, it is possible to estimate the degree of good health condition from the first to third measured values.

[Modified example]
《Example of 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 object such as a vehicle. For example, when the door 10 is a vehicle door, the doorknob 11 may be a grip-type doorknob. Furthermore, when the doorknob 11 is a doorknob for a vehicle door, the server 20 may be configured integrally with various in-vehicle devices. In this case, instead of the mobile terminal 30, the notification based on the estimation result of the estimating section 22, 26, or 27 may be output from an output section such as a display section and/or an audio output section as an in-vehicle device.

Moreover, the gripping member 11 may be a member other than a doorknob as long as it can mount essential members such as the acceleration sensor 13. For example, the gripping member may be a bowl, plate, chopsticks, spoon, fork, toothbrush, etc. that the user grips and uses on a daily basis. Further, the gripping member may be a handrail of a staircase, a bath, 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.

Further, 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 through the Internet.

In each of the above-described embodiments, fingerprint data and parameters indicating hand tremor, grip strength, force and/or speed of rotating the doorknob 11, etc. are detected and measured using separate sensors. However, as various sensors of the estimation system according to the present invention, sensors capable of simultaneously measuring a plurality of types of data may be used. For example, if a vector sensor capable of measuring translational forces in three directions is employed, the vector sensor can serve both as the acceleration sensor 13 and the pressure sensor 14. Alternatively, by employing a 6-axis sensor (that is, a sensor capable of measuring 3-axis acceleration and 3-axis gyro), it can serve as both the acceleration sensor 13 and the torque sensor 17. Thereby, the number of parts mounted on the gripping member can be reduced.

《Variation example of user identification》
Furthermore, in each of the embodiments described above, the user is identified by fingerprint authentication. However, estimation systems 100, 200, and 300 may identify users by means other than fingerprint authentication. For example, estimation systems 100, 200, and 300 may use facial recognition to identify users. In this case, a camera is attached to the door 10 or near the door 10 at an angle that allows the user to photograph the user's face when gripping 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 it 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 grips the doorknob 11 by comparing the received image (face image) with the face image 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 data that is correct for both left and right hands when performing fingerprint authentication and estimating the health condition.

Alternatively, the estimation systems 100, 200, and 300 may include a camera that photographs the user's hand holding the doorknob 11. In this case, when the camera detects the grip of the doorknob 11 by the user using a human sensor or the like, the camera photographs the user's hand and transmits the photographed image to the control unit 15 . The control unit 15 transmits the photographed image to the control unit 25 of the server 20. The control unit 25 identifies which hand, the left or the right, the user grips the doorknob 11 from the received captured image. 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 data that is correct for both left and right hands when performing fingerprint authentication and estimating the health condition.

《Modified example of sensor and estimation process》
Estimation systems 100, 200, and 300 may include sensors other than those described above. Then, the estimation unit 22, 26, or 27 may perform estimation processing taking into consideration the measured value of the sensor. For example, doorknobs 11 of estimation systems 100, 200, and 300 may include electrostatic sensors and/or temperature sensors. The electrostatic sensor and/or temperature sensor outputs a measured value to the control unit 15 similarly to the acceleration sensor 13 and the like. The control unit 15 transmits the acquired measurement value to the control unit 25 of the server 20. The control unit 25 takes these measured values into account during estimation processing in the estimation unit 22, 26, or 27 and derives an estimation result. For example, similar to the first measurement value, threshold values are stored in the storage unit 24 for these measurement values, and the results of comparison between the threshold value and the measurement value and the first to The health condition of the user may be determined by integrating the third measurement value and the comparison results of the first to third measurement values.

For example, the value of the electrostatic sensor is a value related to the amount and composition of sweat of the user who grips the doorknob 11. Further, the value of the temperature sensor is a value related to the body temperature of the user who grips the doorknob 11. Therefore, even if these measured values show abnormal values, it can be estimated that the user is suffering from some kind of disease or disorder. Therefore, the estimation systems 100, 200, and 300 can estimate the health state more accurately by taking these measured values into account when estimating the health state.

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 each of the embodiments and their modifications, but the concept of the present invention and the patent claims. Including all aspects within the scope. Moreover, each structure may be selectively combined as appropriate so as to achieve at least some of the problems and effects described above. For example, each configuration in the embodiments may be changed as appropriate depending on the specific usage of the present invention.

100, 200, 300...Estimation system, 10...Door, 11...Doorknob (grip member), 12...Fingerprint sensor (biological information sensor), 13...Acceleration sensor (first sensor), 14...Pressure sensor (second sensor) , 15... Control unit, 16... Communication unit (gripping member transmitting unit), 17... Torque sensor (third sensor), 20... Server (control device), 21... Communication unit (transmitting unit), 22, 26, 27... Estimation unit, 23...Identification unit, 24...Storage unit, 25...Control unit (acquisition unit), 30...Mobile terminal (terminal device), 31...Communication unit, 32...Control unit, 33...Output unit

Claims (10)

an acquisition unit that acquires a first measurement value regarding the tremor of the user's hand measured by the first sensor from a first sensor provided on a gripping member that is a member gripped by the user;
A control device comprising: an estimation unit that estimates a health condition of the user based on the first measurement value. The acquisition unit acquires user-specific biological information detected by a biological information sensor provided on the gripping member,
The control device includes an identification unit that identifies the user based on the biological information,
The estimation unit derives a first threshold based on log data of the first measurement value for the user identified by the identification unit, and the estimation unit derives a first threshold based on a comparison result between the first measurement value and the first threshold. The control device according to claim 1, wherein the control device estimates the health condition of the user. The acquisition unit acquires, from a second sensor provided on the gripping member, a second measurement value regarding the grip strength of the user's hand acquired by the second sensor,
The control device according to claim 1 or 2, wherein the estimator estimates the health condition of the user based on the second measurement value. The acquisition unit acquires user-specific biological information detected by a biological information sensor provided on the gripping member,
The control device includes an identification unit that identifies the user based on the biological information,
The estimating unit derives a second threshold based on log data of the second measured value for the user identified by the identifying unit, and based on a comparison result between the second measured value and the second threshold. The control device according to claim 3, wherein the control device estimates the health condition of the user. The acquisition unit acquires, from a third sensor provided on the gripping member, a third measured value regarding the force and/or speed of rotation of the gripping member, which is acquired by the third sensor,
The control device according to any one of claims 1 to 4, wherein the estimator estimates the user's health condition based on the third measurement value. The acquisition unit acquires user-specific biological information detected by a biological information sensor provided on the gripping member,
The control device includes an identification unit that identifies the user based on the biological information,
The estimating unit derives a third threshold based on log data of the third measured value for the user identified by the identifying unit, and based on a comparison result between the third measured value and the third threshold. The control device according to claim 5, wherein the control device estimates the health condition of the user. The estimation unit is
digitizing each of the estimation results based on the first measurement value, the estimation result based on the second measurement value, and/or the estimation result based on the third measurement value,
The control device according to any one of claims 3 to 6, characterized in that a comprehensive estimation result is derived by weighting each digitized estimation result. The control device according to any one of claims 1 to 8, further comprising a transmitting unit configured to transmit information based on the estimation result of the estimating unit to a terminal device owned by the user. A gripping member gripped by a user,
a first sensor that measures a first measurement value regarding tremor of the user's hand;
A gripping member comprising: a gripping member transmitter that transmits the first measurement value to the control device according to claim 1 . A control device according to any one of claims 1 to 8,
a terminal device that receives information based on the estimation result from the transmitter of the control device and outputs a notification according to the information;
An estimation system comprising: the gripping member according to claim 9.

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