JPWO2018070389A1 - Biological state estimation system and biological state estimation method - Google Patents

Abstract

The living body state estimation system detects a physical quantity generated by contact with a living body and touches the furniture based on the furniture to which the state detection means for outputting the sensor information is attached, the living body information collection device that collects the sensor information, and the sensor information. A biological state estimation device that estimates the state of the living body, and the biological state estimation device includes a storage device that stores sensor information and an estimation control device that estimates the state of the biological body based on the sensor information. Based on the sensor information, the control device generates state information indicating the state of the physical quantity with respect to time and stores the state information in the storage device, and extracts a feature related to the physical quantity from the state information to estimate the state of the living body, A biological state estimation unit that stores estimation information as an estimation result in a storage device.

Description

  The present invention relates to a living body state estimating system, a sensor unit and furniture, and a living body state estimating method for estimating a living body state.

  Conventionally, a body movement of a living body is caused by a combination of a plurality of joint movements. For example, it is known that it is important to grasp characteristics exhibited by muscle strength in each joint constituting the body movement. In such a living body, due to lack of exercise, aging or injury, muscle strength decreases due to a decrease in muscle mass, resulting in a decrease in balance, motor function, physical ability, etc. The possibility of causing is increased.

  Usually, when measuring the state of a living body such as muscle strength, various special devices are attached to the body and the load is measured, and a doctor or a physical therapist or other specialists based on the measured values Judgment. In this case, in order to measure the muscle strength, it is necessary to go to the hospital or go to the hospital, it is difficult to measure daily muscle strength, etc., and it is possible to evaluate the possibility of falling due to the decrease in muscle strength, etc. It was difficult.

  Therefore, recently, in order to enable measurement of daily muscle strength and the like, an evaluation apparatus that can easily measure and evaluate the muscle strength of a living body by climbing on a load detection unit provided on the step. Has been proposed (for example, Patent Document 1).

JP 2014-28129 A

  However, in the method described in Patent Document 1, an evaluation apparatus for measuring and evaluating muscle strength and the like is provided in the home, and a living body uses the evaluation apparatus to measure and evaluate muscle strength and the like. There has been a problem that it is necessary to perform the operation of measuring each time. In some cases, it is necessary to attach the evaluation device to the body of a living body.

  Therefore, the present invention has been made in view of the above-described problems in the prior art, and estimates the state of a living body without attaching a special instrument or the like to the living body and without being conscious of the living body. It is an object of the present invention to provide a biological state estimation system, a sensor unit, furniture, and a biological state estimation method that can be performed.

  The living body state estimation system according to the present invention detects a physical quantity generated by contact of a living body, and furnishes to which a state detection unit that outputs a detection result as sensor information is attached, and the sensor information output from the state detection unit. A living body information collecting device that collects and a living body state estimating device that estimates the state of the living body in contact with the furniture based on the sensor information collected by the living body information collecting device; A storage device that stores the sensor information; and an estimation control device that estimates the state of the living body based on the sensor information. The estimation control device indicates the state of the physical quantity with respect to time based on the sensor information. A state information generation unit that generates state information to be displayed and stores the generated state information in the storage device; and the state information Extracting a feature relating to the physical quantity, estimating a state of the living body contacting the furniture based on the feature, and a biological state estimating unit storing the estimated information indicating the estimated state of the living body in the storage device; I have it.

  As described above, according to the present invention, it is possible to estimate the state of the living body based on the load applied when the living body comes into contact with the furniture without attaching a special instrument or the like to the living body. The state of the living body can be estimated without being conscious of the living body.

It is a block diagram which shows an example of a structure of the biological condition estimation system which concerns on Embodiment 1. FIG. It is a functional block diagram which shows an example of a structure of the estimation control apparatus in FIG. It is the schematic for demonstrating attachment of the load sensor with respect to the chair as furniture shown in FIG. 3 is a flowchart illustrating an example of a flow of biological state estimation processing according to the first embodiment. 6 is a functional block diagram illustrating an example of a configuration of an estimation control device according to Embodiment 2. FIG. It is the schematic for demonstrating the state of the load with respect to the seat surface at the time of the biological body with sufficient muscular strength of a thigh sitting on a chair. It is the schematic for demonstrating further the state of the load with respect to the seat surface at the time of the biological body with sufficient muscle strength of a thigh sitting on a chair. It is the schematic for demonstrating the state of the load with respect to the seat surface at the time of the biological body with weak muscular strength of a thigh sitting on a chair. It is the schematic for further demonstrating the state of the load with respect to the seat surface at the time of the biological body with weak muscular strength of a thigh sitting on a chair. 12 is a flowchart illustrating a first example of a flow of muscle strength estimation processing according to the second embodiment. 12 is a flowchart illustrating a second example of the flow of muscle strength estimation processing according to the second embodiment. 12 is a flowchart illustrating a third example of the flow of muscle strength estimation processing according to the second embodiment. 12 is a flowchart illustrating a fourth example of the flow of muscle strength estimation processing according to the second embodiment. It is a block diagram which shows an example of a structure of the biological condition estimation system which concerns on Embodiment 3. FIG. 10 is a flowchart illustrating an example of a flow of muscle strength estimation processing according to the third embodiment. It is the schematic for demonstrating the state of the load with respect to the seat surface at the time of the biological body with sufficient thigh muscle strength standing up from a chair. It is the schematic for demonstrating the state of the load with respect to the seat surface at the time of the biological body with weak thigh strength rising from a chair. 12 is a flowchart illustrating a first example of a flow of muscle strength estimation processing according to the fourth embodiment. 14 is a flowchart illustrating a second example of the flow of muscle strength estimation processing according to the fourth embodiment. 14 is a flowchart illustrating a third example of the flow of muscle strength estimation processing according to the fourth embodiment. FIG. 10 is a functional block diagram illustrating an example of a configuration of a collection control device according to a fifth embodiment. FIG. 10 is a functional block diagram illustrating an example of a configuration of an estimation control device according to a sixth embodiment. FIG. 20 is a functional block diagram illustrating an example of a configuration of an estimation control device according to a seventh embodiment. FIG. 20 is a functional block diagram illustrating an example of a configuration of an estimation control device according to an eighth embodiment. FIG. 25 is a schematic diagram illustrating an example of a configuration of a bed as furniture according to a ninth embodiment. FIG. 20 is a functional block diagram illustrating an example of a configuration of an estimation control device according to a ninth embodiment. FIG. 25 is a schematic diagram illustrating an example of load state information used in the body posture estimation process according to the ninth embodiment. It is the schematic which shows an example of a structure of the western style toilet bowl as furniture concerning Embodiment 10. FIG. FIG. 38 is a functional block diagram showing an example of a configuration of an estimation control apparatus according to Embodiment 11. FIG. 38 is a functional block diagram illustrating an example of a configuration of an estimation control device according to Embodiment 12. It is the schematic for demonstrating a tremor. 29 is a flowchart illustrating an example of a tremor estimation process according to the twelfth embodiment. FIG. 38 is a functional block diagram illustrating an example of a configuration of an estimation control apparatus according to Embodiment 13. It is a block diagram which shows an example of a structure of the biological condition estimation system which concerns on Embodiment 14. FIG. It is a functional block diagram which shows an example of a structure of the estimation control apparatus in FIG. It is the schematic which shows the 1st example for demonstrating the sensor information at the time of using an acceleration sensor. It is the schematic which shows the 2nd example for demonstrating the sensor information at the time of using an acceleration sensor. It is the schematic for demonstrating estimation of the gravity center of the biological body in the 1st example of FIG.

Embodiment 1 FIG.
Hereinafter, the biological state estimation system according to Embodiment 1 of the present invention will be described. In the first embodiment, a load sensor as a state detection unit is attached to furniture such as a chair or a bed, and various biological state estimation processes are performed based on the detection result by the load sensor.

[Configuration of biological state estimation system]
FIG. 1 is a block diagram illustrating an example of the configuration of the biological state estimation system 100 according to the first embodiment. As shown in FIG. 1, the biological state estimation system 100 includes a biological information collection device 1, a biological state estimation device 2, and furniture 3. The biological information collection device 1 and the biological state estimation device 2 are connected to a network 4 such as the Internet. Wired or wireless connection.

(Biological information collection device)
The biological information collection device 1 includes a collection control device 10, an input / output interface (I / F) 11, a display device 12, and an input device 13. The biological information collection device 1 collects sensor information as physical quantities detected by load sensors 31A to 31D described later.

  The collection control device 10 uses a RAM (Random Access Memory) (not shown) as a work memory, and controls the entire operation of the biological information collection device 1 according to a program stored in advance in a ROM (Read Only Memory) (not shown). The collection control device 10 is configured by, for example, software such as a microcomputer or a CPU (Central Processing Unit) or hardware such as a circuit device that implements various processes.

  The collection control device 10 receives sensor information, which is information indicating detection results, from load sensors 31A to 31D attached to the furniture 3 described later, and supplies the received sensor information to the network 4 via the input / output I / F 11. To do. In addition, the collection control apparatus 10 obtains estimation information indicating an estimation result obtained by the biological state estimation process of the biological state estimation apparatus 2 obtained from the biological state estimation apparatus 2 described later based on sensor information, and the network 4 and the input / output I / Receive via F11.

  The input / output I / F 11 controls communication with the biological state estimation device 2 connected via the network 4 according to a predetermined protocol. Communication control in the input / output I / F 11 is performed based on a command from the collection control apparatus 10. The input / output I / F 11 performs communication control when transferring the sensor information supplied from the collection control device 10 to the biological state estimation device 2. Further, the input / output I / F 11 receives the estimation information supplied from the network 4 and supplies the received estimation information to the collection control device 10.

  The display device 12 includes, for example, an LCD (Liquid Crystal Display), an organic EL (Electro Luminescence) display, and the like, and displays sensor information obtained by the load sensors 31A to 31D, estimated information received from the biological state estimation device 2, and the like. To do. As the display device 12, for example, a touch panel display in which a touch panel having a touch sensor is stacked on an LCD or an organic EL display can be used.

  The input device 13 is provided with various buttons or keys used for operating the biological information collection device 1, and outputs an operation signal corresponding to an operation on each button or key. As such an input device 13, for example, a remote controller or keys and buttons provided in the biological information collecting device 1 can be used. As described above, when the display device 12 is a touch panel display, various buttons or keys may be displayed on the display device 12 as software buttons or software keys. In the biological information collecting apparatus 1, the display device 12 and the input device 13 are not essential components and may be provided as necessary.

(Biological state estimation device)
The biological state estimation device 2 is, for example, a server installed at a point different from the point where the biological information collection device 1 is installed, or is installed on the cloud, and the biological information is obtained via the network 4. The collector 1 is connected. The biological state estimation device 2 includes an estimation control device 20, an input / output interface (I / F) 21, and a storage device 22. The living body state estimation device 2 estimates the state of a living body in contact with the furniture 3 based on sensor information detected by the load sensors 31A to 31D.

  The estimation control device 20 uses a RAM (not shown) as a work memory, and controls the overall operation of the biological state estimation device 2 according to a program stored in advance in a ROM (not shown). The estimation control device 20 is configured by, for example, software executed on an arithmetic device such as a microcomputer or CPU, or hardware such as a circuit device that implements various processes described later.

  The estimation control device 20 receives the sensor information received from the biological information collection device 1 via the network 4 via the input / output I / F 21 and stores it in the storage device 22 described later. In addition, the estimation control device 20 performs a biological state estimation process for estimating the state of the living body in contact with the furniture 3 based on the sensor information received from the biological information collecting device 1. Then, the estimation control device 20 transmits estimation information indicating an estimation result obtained by the biological state estimation process to the biological information collection device 1 via the network 4.

  The estimation control device 20 stores various data and estimation information used in the biological state estimation processing in the storage device 22. Details of the biological state estimation process performed by the estimation control device 20 will be described later.

  The input / output I / F 21 controls communication with the biological information collecting apparatus 1 connected via the network 4 according to a predetermined protocol. Communication control in the input / output I / F 21 is performed based on a command from the estimation control device 20. The input / output I / F 21 performs communication control when transferring the estimated information supplied from the estimation control device 20 to the biological information collection device 1. Further, the input / output I / F 21 receives the sensor information supplied from the network 4 and supplies the received sensor information to the estimation control device 20.

  The storage device 22 is configured by, for example, a flash memory or an HDD (Hard Disk Drive). The storage device 22 performs sensor information received from the biological information collection device 1 based on the control of the estimation control device 20 described later, various data used in the biological state estimation processing by the estimation control device 20, and the biological state estimation processing. The estimated information obtained is stored.

  In this example, the whole biological state estimation device 2 has been described as being installed on a point different from the biological information collection device 1, for example, the cloud, but this is not limited to this example. For example, a part of the biological state estimation device 2 may be installed on the cloud. Specifically, for example, only the storage device 22 of the biological state estimation device 2 is installed on the cloud, and other configurations of the biological state estimation device 2 are the same as the points where the biological information collection device 1 is installed. You may make it install in.

(furniture)
The furniture 3 can be a living body such as a chair, a bed, or a toilet that can be contacted like a seat. A plurality of load sensors 31 </ b> A to 31 </ b> D are attached to the furniture 3. Hereinafter, a case where the furniture 3 is a chair will be described as an example.

  The load sensors 31 </ b> A to 31 </ b> D are attached to the legs that support the main body having a seating surface such as a chair as the furniture 3. Specifically, for example, the load sensor 31A is attached to the right front leg while the living body is seated on a chair, and the load sensor 31B is attached to the left front leg. The load sensor 31C is attached to the right rear leg, and the load sensor 31D is attached to the left rear leg.

  The load sensors 31A to 31D detect a load applied to a position corresponding to each attached leg as a physical quantity, and output the detection result as sensor information. In the following description, when it is not necessary to distinguish the load sensors 31A to 31D, they are simply referred to as “load sensor 31” as appropriate. The “load sensor 31” includes both a case where a single load sensor is shown and a case where a plurality of load sensors are shown.

  In this example, the case where four load sensors 31 </ b> A to 31 </ b> D are used as the load sensor 31 has been described. However, the present invention is not limited thereto, and in the first embodiment, at least three load sensors 31 may be used. This is to enable detection of the front / rear and left / right loads applied to the furniture 3. For example, when three load sensors 31 </ b> A to 31 </ b> C are used, the load sensors 31 </ b> A to 31 </ b> C are attached to any three of the four legs in the chair as the furniture 3.

  Furthermore, when there is no difference between the left and right in the state of the living body, the comparison between the left and right is not important. Therefore, in this case, two load sensors 31 may be used so as to be attached to the front and rear of the furniture 3.

[Configuration of estimation control device]
FIG. 2 is a functional block diagram showing an example of the configuration of the estimation control device 20 in FIG. As illustrated in FIG. 2, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a biological state estimation unit 40.

  The input processing unit 25 receives the sensor information received from the biological information collection device 1 via the input / output I / F 21 and outputs the received sensor information to the load state information generation unit 27. The output processing unit 26 receives the estimation information from the biological state estimation unit 40 and outputs the received estimation information to the input / output I / F 21.

  The load state information generation unit 27 stores the sensor information received from the input processing unit 25 in the storage device 22. Further, the load state information generation unit 27 generates load state information indicating a load state with respect to time based on the sensor information, and stores the load state information in the storage device 22. Details of the load state information will be described later.

  Based on the load state information generated by the load state information generation unit 27, the biological state estimation unit 40 performs a biological state estimation process for estimating the state of the living body contacting the furniture 3 to which the load sensor 31 is attached. Then, the biological state estimation unit 40 outputs estimation information indicating the result of the estimation process and stores it in the storage device 22.

[Furniture structure]
FIG. 3 is a schematic diagram for explaining the attachment of the load sensor 31 to the chair 3a as the furniture 3 shown in FIG. As shown in FIG. 3, the chair 3a as the furniture 3 is composed of a main body that comes into contact with a living body and a plurality of legs 3c that support the main body.

  The main body of the chair 3a is provided with a seating surface 3b on which a living body can sit. A sensor unit 30 including a load sensor 31 is attached to the leg 3c of the chair 3a. The sensor unit 30 is, for example, between the seat surface 3b and the leg 3c of the chair 3a (FIG. 3A) or at a position where the leg 3c of the chair 3a contacts the floor (FIG. 3B). It is attached.

(Sensor unit)
A plurality of sensor units 30 are provided corresponding to the number of load sensors 31 provided in the biological state estimation system 100. As shown in FIG. 3C, each sensor unit 30 includes a single load sensor 31, a plate member 32, and collars 33a and 33b. The plate-like member 32 is provided to attach the load sensor 31 to the leg 3c of the chair 3a, and a seating surface side fixing member (FIG. 3 (a)) or leg for fixing the leg 3c to the seating surface 3b. It is attached to the lower part of the part 3c (FIG. 3B) using a connecting member such as a screw. A load sensor 31 is attached to the plate-like member 32 through a collar 33a using a connecting member such as a screw. The load sensor 31 is attached to the leg-side fixing member on the leg 3c side via a collar 33b using a connection member such as a screw.

  For example, when the leg 3c of the furniture 3 is connected to the main body, the connecting member uses a member generally used, for example, an M6 screw having an outer diameter of 6 mm used for a general connection standard. And preferred. This is to make the sensor unit 30 versatile so that the sensor unit 30 can be easily attached to various furniture 3.

  By attaching each sensor unit 30 configured in this way to each leg 3c, the load sensor 31 is provided on the seat surface 3b corresponding to the leg 3c when a living body is seated on the seat 3b. A load applied to the position can be detected. Further, by adopting a structure in which the sensor unit 30 is attached to the leg portion 3c using a general-purpose connection member such as a screw, the sensor unit 30 is not only a chair 3a but also general furniture having leg portions such as a bed. On the other hand, it can be easily and reliably attached.

  In addition, the structure of the sensor unit 30 and the mounting structure of the sensor unit 30 to the chair 3a are not limited to this example, and any method can be used as long as the load applied to the position corresponding to the leg 3c of the chair 3a can be detected. It may be a simple structure.

[Operation of biological state estimation system]
Next, the operation of the biological state estimation system 100 according to the first embodiment will be described with reference to FIG. 1 and FIG.

  First, each load sensor 31 provided in the furniture 3 detects a load for each preset time, and outputs the detection result as sensor information. The sensor information output from each load sensor 31 is supplied to the biological information collecting apparatus 1. The biological information collection device 1 receives the sensor information supplied from each load sensor 31 and outputs the sensor information to the outside via the collection control device 10 and the input / output I / F 11. The sensor information output from the biological information collection device 1 is supplied to the biological state estimation device 2 via the network 4.

  The biological state estimation device 2 receives the sensor information supplied from the biological information collection device 1 via the input / output I / F 21 and supplies the sensor information to the estimation control device 20. The estimation control device 20 stores the supplied sensor information in the storage device 22 and generates load state information in the load state information generation unit 27. Moreover, the estimation control apparatus 20 performs the biological state estimation process based on the load state information in the biological state estimation unit 40. Then, the estimation control device 20 generates and outputs estimation information indicating the estimation result, and stores it in the storage device 22.

  The estimation information output from the estimation control device 20 is output from the biological state estimation device 2 via the input / output I / F 21. The estimation information output from the biological state estimation device 2 is supplied to the biological information collection device 1 via the network 4.

  The biological information collection device 1 receives the estimation information supplied from the biological state estimation device 2 via the input / output I / F 11 and supplies it to the collection control device 10. The collection control device 10 displays the estimated information supplied using, for example, the display device 12 or the like.

(Biological state estimation process)
Next, the biological state estimation process by the biological state estimation unit 40 will be described. The biological state estimation process is a process for estimating the state of the biological body in contact with the furniture 3. FIG. 4 is a flowchart showing an example of the flow of the biological state estimation process according to the first embodiment.

  As shown in FIG. 4, first, when performing the biological state estimation process, the load state information generation unit 27 acquires sensor information supplied at every set time (step S1). Next, the load state information generation unit 27 arranges the load states in time series based on the acquired sensor information, and generates load state information indicating the load state with respect to time for each of the load sensors 31A to 31D (step S2). ).

  Next, the biological state estimation unit 40 extracts the characteristics of the loads detected by the load sensors 31A to 31D from the load state information generated by the load state information generation unit 27. The characteristics obtained from the load state information at this time include, for example, the magnitude of the load applied to each of the load sensors 31A to 31D, the order in which the load on each of the load sensors 31A to 31D increases or decreases, and each of the load sensors 31A to 31A. This is the time during which the load applied to 31D is stable. The characteristics obtained in this way include information on the state of the living body in contact with the furniture 3.

  The biological state estimation unit 40 performs a biological state estimation process for estimating the state of the biological body in contact with the furniture 3 based on the characteristics obtained from the load state information (step S3). The living body state that can be estimated by the living body state estimating process is, for example, the muscle strength or tremor of the living body. And the biological state estimation part 40 produces | generates the estimation information which shows the estimated biological state (step S4).

  As described above, the living body state estimation system 100 according to the first embodiment includes the furniture 3 to which the plurality of load sensors 31 that detect the load due to the living body and output as sensor information are attached, and the plurality of load sensors 31. The living body information collecting apparatus 1 that collects the output sensor information and the living body state estimating apparatus 2 that estimates the state of the living body in contact with the furniture 3 based on the sensor information collected by the living body information collecting apparatus 1. Yes. The biological state estimation device 2 includes a storage device 22 that stores sensor information, and an estimation control device 20 that estimates the state of the biological body based on the sensor information. The estimation control device 20 uses time information based on the sensor information. The load state information generating unit 27 generates load state information indicating the state of the load on the load, stores the generated load state information in the storage device, and extracts a feature related to the load from the load state information, and contacts the furniture 3 with the living body And a biological state estimation unit 40 that stores estimated information indicating the estimated biological state in the storage device 22. Thereby, in this Embodiment 1, the state of the biological body which is contacting the furniture 3 can be estimated without attaching an instrument etc. with respect to the biological body, and without being conscious.

Embodiment 2. FIG.
Next, a biological state estimation system according to Embodiment 2 of the present invention will be described. In the second embodiment, as the biological state estimation process by the biological state estimation unit 40 in the first embodiment, the muscle strength estimation process for estimating the muscle strength of the living body in contact with the furniture 3 is performed. In the following description, the same reference numerals are given to portions common to the above-described first embodiment, and detailed description thereof is omitted.

  About the structure of the biometric information collection apparatus 1 and the furniture 3 which concern on this Embodiment 2, since it is the same as that of Embodiment 1, description is abbreviate | omitted. On the other hand, the biological state estimation device 2 is different from the first embodiment in that the estimation control device 20 performs muscle strength estimation processing as biological state estimation processing.

[Configuration of estimation control device]
FIG. 5 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the second embodiment. As illustrated in FIG. 5, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a muscle strength estimation unit 41.

  The muscular strength estimation unit 41 receives the load state information generated by the load state information generation unit 27, and based on the received load state information, muscular strength estimation that estimates the muscular strength of a living body that contacts the furniture 3 to which the load sensor 31 is attached. Process. The muscle strength estimation unit 41 outputs information indicating the muscle strength state of the living body obtained by the estimation process as estimated information and stores the information in the storage device 22.

(Muscle strength estimation processing)
Next, the muscle strength estimation process by the muscle strength estimation unit 41 according to the second embodiment will be described. The muscular strength estimation process is a process of estimating the muscular strength of a living body that is in contact with the furniture 3 such as the chair 3a. Below, the case where a living body sits on the chair 3a as the furniture 3 shown in FIG. 3 is taken as an example, and the process of estimating muscle strength of the lower limb of the living body, particularly the thigh, will be described.

  In general, when a living body is seated on the chair 3a, the state of the load applied to the seating surface 3b of the chair 3a differs depending on whether the muscle strength of the living body's thigh is sufficient or weak. Therefore, in the muscle strength estimation process according to the second embodiment, when the living body performs an operation of sitting on the chair 3a, the muscle strength of the living body is analyzed by analyzing the sensor information detected by each load sensor 31 in time series. Is estimated.

  Here, before describing the processing method of the muscle force estimation process, the state of the load on the seat surface 3b when the living body is seated on the chair 3a will be described.

(1) When muscle strength is sufficient FIG. 6: is the schematic for demonstrating the state of the load with respect to the seat surface 3b at the time of the biological body with sufficient muscle strength of a thigh sitting on the chair 3a. In FIG. 6, the horizontal axis indicates time, and the vertical axis indicates load.

  When sitting on the chair 3a from a state where a living body with sufficient muscular strength stands (FIG. 6A), generally, the living body first tilts the trunk forward so as to bow (FIG. 6B). ). Next, the living body searches for the height of the seating surface 3b while gradually bending the knee while maintaining the center of gravity line at the center of the body. When the buttocks come into contact with the seat surface 3b (FIG. 6C), the living body gradually makes the trunk (body) vertical while applying weight to the seat surface 3b (FIG. 6D).

That is, in this case, as shown in FIG. 6 (e), beginning sit vivo at some point t _1, the buttocks are in contact with the seat surface 3b of the chair 3a, firstly, a position corresponding to the front of the seat surface 3b load on the load sensor 31A and 31B provided on increases gradually, reaching the maximum load at the time t _2.

Next, a delay in the load on the front of the seat surface 3b, the load is increased relative to the load sensor 31C and 31D provided in a position corresponding to the rear of the seat surface 3b, the maximum load at a later time t ₃ than time t ₂ To reach. When the living body is completely seated against the chair 3a, decreased gradual load detected by the load sensor 31A-31D, it becomes constant value at time t _4, to maintain a stable state. In this case, the time T ₁ required from the time t ₁ at which the living body starts to sit to the time t ₄ at which it is completely seated is a relatively short time.

  FIG. 7 is a schematic diagram for further explaining the state of the load on the seating surface 3b when a living body with sufficient muscle strength of the thigh is seated on the chair 3a. In FIG. 7, the horizontal axis represents time, and the vertical axis represents the weight ratio load obtained by dividing the load on each load sensor 31 by the body weight of the living body. FIG. 7A shows temporal changes in the load detected by the load sensor 31A attached to the right front of the chair 3a and the load sensor 31C attached to the right rear. FIG.7 (b) shows the time change of the load detected with the load sensor 31B attached to the left front of the chair 3a, and the load sensor 31D attached to the left back.

  As shown in FIG. 7A, when the living body is seated on the chair 3a, the load increases as a whole. When the muscular strength of the living body is sufficient, when the peak value is detected among the loads on the two load sensors 31A and 31C attached to the right front and rear of the chair 3a, the load on the rear load sensor 31C is forward. Is greater than the load on the load sensor 31A. Moreover, as shown in FIG.7 (b), it has the same tendency also about the load with respect to the two load sensors 31B and 31D attached to the left front and back with respect to the chair 3a.

(2) When Muscle Strength is Weak FIG. 8 is a schematic diagram for explaining a state of a load on the seat surface 3b when a living body with weak muscle strength of the thigh sits on the chair 3a. In FIG. 8, the horizontal axis indicates time, and the vertical axis indicates load.

  When sitting on the chair 3a from a standing body (Fig. 8 (a)) such as an elderly person with a weak muscular strength, generally due to fear of falling forward by tilting the trunk forward The living body bends the knee without tilting the trunk forward and searches for the height of the seating surface 3b of the chair 3a. At this time, the load on the muscles in the front of the thigh cannot be withstood, and the knee bends rapidly, so that it sits on the seat surface 3b of the chair 3a (FIG. 8 (b)). After sitting on the chair 3a, it is difficult for the living body to maintain a stable posture due to aging, and it takes a long time to become stable or the posture is unstable (see FIG. 8 (c)).

That is, in this case, as shown in FIG. 8 (d), for seating a "thud" biological at some time t ₁ is the seating face 3b, all of the load provided in a position relative to the front and rear of the seat surface 3b The load on the sensors 31 </ b> A to 31 </ b> D increases rapidly and reaches the maximum load at time t ₂ (t ₃ ). The maximum load at this time is larger than the maximum load when a living body with sufficient muscle strength is seated. In addition, when a living body with weak muscle strength of the thigh such as an elderly person is seated in this way, the posture is not stable even after sitting, so the loads detected by the load sensors 31A to 31D are not stable. In this case, the time T ₁ required from the time point t ₁ at which the living body starts to sit to the time point at which it is completely seated is much longer than in the case of a living body with sufficient muscular strength, or it is difficult to measure time.

  FIG. 9 is a schematic diagram for further explaining the state of the load on the seating surface 3b when a living body with weak muscle strength of the thigh sits on the chair 3a. In FIG. 9, the horizontal axis represents time, and the vertical axis represents the weight ratio load obtained by dividing the load on each load sensor 31 by the body weight of the living body. FIG. 9A shows temporal changes in the load detected by the load sensor 31A attached to the right front of the chair 3a and the load sensor 31C attached to the right rear. FIG. 9B shows temporal changes in the load detected by the load sensor 31B attached to the left front of the chair 3a and the load sensor 31D attached to the left rear.

  As shown in FIG. 9A, when the living body sits on the chair 3a, the load increases as a whole. When the muscular strength of the living body is weak, when the peak value is detected among the loads on the two load sensors 31A and 31C attached to the right front and rear of the chair 3a, the load on the front load sensor 31A is the rear load. It is larger than the load on the sensor 31C. Moreover, as shown in FIG.9 (b), it has the same tendency also about the load with respect to the two load sensors 31B and 31D attached to the chair 3a on the left front back.

  As described above, when a living body having different muscle strength performs a sitting operation on the chair 3a, the load on the load sensors 31A to 31D provided on the chair 3a increases depending on whether or not the muscle strength of the thigh is sufficient. The load state such as the order and the maximum load size is different. Therefore, in this case, by analyzing the state of the load on the load sensors 31A to 31D in time series and extracting the order of increasing the load on each of the load sensors 31A to 31D and the magnitude of the maximum load as features, The muscle strength of the living body can be estimated.

In this example, the time T ₁ that organism required until fully seated since the start sitting in a chair 3a, varies depending on the state of the muscle. Therefore, in the second embodiment, the time T ₁ of the up to the point where the biological is fully seated from the time of starting to sit on a chair 3a, may also be characterized in estimating muscle.

  Furthermore, in this example, the relative magnitude of the front and rear loads at the time when the peak value of the load is detected when the living body is seated on the chair 3a differs depending on the state of the muscular strength. Therefore, the second embodiment can also be characterized by the relative magnitude of the front and rear loads at the time when the peak value of the load is detected.

  Thereby, the load state information generation part 27 produces | generates load state information based on the sensor information supplied for every setting time. Then, the muscle force estimation unit 41 determines from the load state information generated by the load state information generation unit 27, the order in which the loads on the load sensors 31A to 31D increase, the magnitude of the maximum load, or from the start of sitting to complete seating. Is extracted as a feature related to muscle strength estimation. Further, the muscular strength estimation unit 41 extracts the magnitude of the load at the time when the peak value of the load is detected in the load sensors 31A and 31C or the load sensors 31B and 31D attached to the front and rear of the furniture 3 as a feature. When the features relating to muscle strength estimation are extracted in this way, the muscle strength estimation unit 41 determines whether or not the muscle strength of the living body is sufficient based on the extracted features.

(A) When the order of loads on the load sensors 31A to 31D is a feature For example, consider a case where the order of increasing loads on the load sensors 31A to 31D is extracted as a feature. When the muscular strength of the living body is sufficient, in each of the load sensors 31A to 31D, after the load detected by the front load sensors 31A and 31B increases, the load detected by the rear load sensors 31C and 31D increases. To do. Then, after the maximum load is detected by the front load sensors 31A and 31B, the maximum load is detected by the rear load sensors 31C and 31D.

That is, in this case, the time t ₂ when the maximum load is detected in front of the load sensor 31A and 31B, and the time point t ₃ when maximum load is detected at the rear of the load sensor 31C and 31D do not match.

  On the other hand, when the muscular strength of the living body is weak, in each of the load sensors 31A to 31D, the load detected by the front load sensors 31A and 31B and the load detected by the rear load sensors 31C and 31D increase simultaneously. . The front load sensors 31A and 31B and the rear load sensors 31C and 31D reach the maximum load almost simultaneously.

That is, in this case, the time t ₂ the maximum load in front of the load sensor 31A and 31B is detected, and the time t ₃ when the maximum load is detected by the rear load sensor 31C and 31D coincide.

  Here, “the timing at which the maximum load is detected coincides” means that the maximum load is detected by each of the load sensors 31A to 31D within a preset time. The set time at this time is a time when it can be considered that the maximum load is detected at the same time, and specifically indicates a case where it is about 1 second, for example.

FIG. 10 is a flowchart showing a first example of the flow of muscle strength estimation processing according to the second embodiment. In this case, the muscular strength estimation unit 41 extracts the order of loads on the load sensors 31A to 31D as a feature (step S11). The muscle force estimation unit 41 includes a difference time T ₂ indicating a temporal difference between a time point t ₂ when the maximum load is detected by the load sensors 31A and 31B and a time point t ₃ when the maximum load is detected by the load sensors 31C and 31D. it is calculated, depending on whether the calculated difference time T ₂ that is included in the preset time, strength determines whether sufficient (step S12). When the difference time _{T 2} exceeds a set time (step S12; Yes), muscle force estimation unit 41, the biological muscle strength is determined to be sufficient (step S13). Further, when the difference time _{T 2} is within the set time (Step S12; No), muscle force estimation unit 41 determines that the biometric muscle is weak (step S14).

(B) Case where the magnitude of the maximum load with respect to each of the load sensors 31A to 31D is characterized In addition, for example, a case where the magnitude of the maximum load with respect to each of the load sensors 31A to 31D is extracted as a feature is considered. As described above, when the muscular strength of the living body is different, the sitting operation with respect to the chair 3a is different. Therefore, the magnitude | size of the maximum load with respect to each load sensor 31A-31D differs. Therefore, in this case, a threshold value is set in advance for the load, and the muscular strength of the living body is estimated based on a comparison result between the magnitude of the maximum load for each of the load sensors 31A to 31D and the set threshold value.

  FIG. 11 is a flowchart illustrating a second example of the flow of muscle strength estimation processing according to the second embodiment. In this case, the muscular strength estimation unit 41 extracts the magnitude of the load for each of the load sensors 31A to 31D as a feature (step S21). Next, the muscular strength estimation unit 41 determines whether or not the muscular strength is sufficient by comparing the magnitude of the maximum load detected by each of the load sensors 31A to 31D with a set threshold value (step S22).

  And when the magnitude | size of the maximum load is less than a setting threshold value (step S22; Yes), the muscular strength estimation part 41 judges that the muscular strength of a biological body is enough (step S23). Moreover, when the magnitude | size of the maximum load is more than a setting threshold value (step S22; No), the muscular strength estimation part 41 judges that the muscular strength of a biological body is weak (step S24). The threshold value at this time is a value that does not exceed the maximum load when a living body with sufficient muscular strength is seated in a normal motion, and is specifically set to a value of about 120% in weight ratio, for example. Is done.

(C) When characterized by the time of the sitting operation Further, for example, when the time from when the living body starts to sit until it is completely seated (hereinafter referred to as “sitting operation time” as appropriate) is extracted as a feature Think. As described above, when a biological muscle condition are different, the seating operation time T ₁ of the until fully seated since the start of the seating is different.

Therefore, in this case, after the load applied when the living body is seated by each of the load sensors 31A to 31D is detected, the living body is completely seated, and the magnitude of the load on each of the load sensors 31A to 31D is stable. setting a threshold for the seating operation time T ₁ of the till. Then, based on the comparison result of the sitting operation time T ₁ and the set threshold value, estimates the biological muscle.

FIG. 12 is a flowchart showing a third example of the flow of muscle strength estimation processing according to the second embodiment. In this case, muscle force estimation unit 41 extracts the seating operation time _{T 1} as the feature (step S31). Next, muscle force estimation unit 41, by comparing the seating operation time T ₁ and a set threshold, muscle determines whether sufficient (step S32). When seated operation time _{T 1} is less than the set threshold value (step S32; Yes), muscle force estimation unit 41, the biological muscle strength is determined to be sufficient (step S33). Also, if the seating operation time _{T 1} is not less than a set threshold (Step S32; No), muscle force estimation unit 41 determines that the biometric muscle is weak (step S34).

  Note that the threshold value at this time is a time during which a living body with sufficient muscular strength may be considered to be seated in a stable state when seated in a normal motion, and is specifically set to about 1 second, for example. . In addition, the “state in which the living body is stable” indicates a state in which the magnitude of the amplitude due to the load detected by each of the load sensors 31A to 31D is within a preset range. This refers to a state of not more than ± 2 times the standard deviation in amplitude for 1 second.

(D) When characterized by the relative magnitude of the front and rear loads at the time of the load peak Further, for example, the relative of the front and rear loads at the time when the load peak value is detected when the living body is seated. Consider the case where size is extracted as a feature. As described above, when the muscle strength of the living body is different, the relative magnitudes of the front and rear loads at the time of the load peak are different.

  Therefore, in this case, when the peak value of the load is detected in the load sensors 31A and 31C, the load detected by the load sensor 31A attached to the front and the load sensor 31B attached to the rear is compared. To do. Moreover, when the load peak value is detected in the load sensors 31B and 31D, the load detected by the load sensor 31B attached to the front and the load sensor 31D attached to the rear is compared. Based on the comparison result, the muscle strength of the living body is estimated.

  FIG. 13 is a flowchart showing a fourth example of the flow of muscle strength estimation processing according to the second embodiment. Here, a case where muscle strength is estimated based on loads detected by load sensors 31A and 31C attached to the front and rear of the right side of the furniture 3 will be described as an example.

  In this case, the muscular strength estimation unit 41 extracts the relative magnitude of the load with respect to the front and rear load sensors 31A and 31C at the time when the peak value of the load is detected as a feature (step S41). Next, the muscular strength estimation unit 41 determines whether or not the muscular strength is sufficient by comparing the load by the front load sensor 31A and the load by the rear load sensor 31C (step S42).

  When the load from the front load sensor 31A is smaller than the load from the rear load sensor 31C (step S42; Yes), the muscle force estimation unit 41 determines that the muscle strength of the living body is sufficient (step S43). When the load from the front load sensor 31A is equal to or greater than the load from the rear load sensor 31C (step S42; No), the muscle force estimation unit 41 determines that the living body muscle strength is weak (step S44).

  In this example, the case where any one of (a) to (d) described above is extracted as a feature has been described. However, the present invention is not limited to this, and for example, any one of (a) to (d), or (a) to (a) All of (d) may be extracted as features.

  As described above, in the second embodiment, the biological state estimation unit 40 is based on the load state information obtained from the sensor information detected by the plurality of load sensors 31 attached to the respective leg portions 3c of the furniture 3. The muscle strength estimating unit 41 estimates the muscle strength of the living body seated on the furniture 3, and the muscle strength estimating unit 41 estimates the muscle strength of the living body based on the characteristics indicating the operation related to the sitting on the furniture 3.

  Further, in the second embodiment, the characteristics indicating the operation related to the seating at this time are the order in which the load due to the living body detected by the plurality of load sensors 31 increases, and the magnitude of the maximum load detected by the plurality of load sensors 31. The sitting operation time indicating the time from when the living body starts sitting on the furniture 3 until it is seated, or the relative magnitude of the front and rear loads at the time of the load peak. These characteristics and the characteristics The muscle strength of the living body is estimated based on a comparison result with a preset threshold value or the like. Thereby, in this Embodiment 2, the muscular strength of a biological body can be estimated based on the operation | movement at the time of a biological body sitting on furniture, such as a chair.

Embodiment 3 FIG.
Next, a biological state estimation system according to Embodiment 3 of the present invention will be described. In the third embodiment, as in the second embodiment, the muscular strength estimation process for estimating the muscular strength of the living body contacting the furniture 3 is performed as the biological state estimation process by the biological state estimating unit 40 in the first embodiment.

  The living body state estimation system 100 according to the third embodiment uses one load sensor 31 to estimate muscle strength based on the load state when the living body is seated from the chair 3a as the furniture 3. This is different from the second embodiment. In the following description, parts common to those in the first and second embodiments described above are denoted by the same reference numerals, and detailed description thereof is omitted.

[Configuration of biological state estimation system]
FIG. 14 is a block diagram illustrating an example of a configuration of the biological state estimation system 100 according to the third embodiment. As shown in FIG. 1, the biological state estimation system 100 includes a biological information collection device 1, a biological state estimation device 2, and furniture 3. The biological information collection device 1 and the biological state estimation device 2 are connected to a network 4 such as the Internet by wire or wireless. The configuration of the estimation control device 20 in the biological state estimation device 2 is the same as that of the estimation control device 20 shown in FIG.

(Biological information collection device)
The biological information collection device 1 includes a collection control device 10, an input / output interface (I / F) 11, a display device 12, and an input device 13, similarly to the biological information collection device 1 shown in FIG. The collection control device 10 receives sensor information, which is information indicating a detection result, from the load sensor 31E attached to the furniture 3, and supplies the received sensor information to the network 4 via the input / output I / F 11. The other functions of the collection control apparatus 10 are the same as those of the collection control apparatus 10 shown in FIG.

(furniture)
The furniture 3 is, for example, a chair 3a, to which a load sensor 31E is attached. The load sensor 31E is attached to the back of the chair 3a. Specifically, the load sensor 31E is attached to either one of the left and right legs, for example.

(Muscle strength estimation processing)
Next, the muscle strength estimation process by the muscle strength estimation unit 41 according to the third embodiment will be described. When one load sensor 31E is attached to the back of the chair 3a, the peak value of the load detected by the load sensor 31E differs greatly between when the muscular strength of the living body is sufficient and when the muscular strength is weak. Specifically, when the muscular strength of the living body is sufficient, the peak value of the load is larger than that when the muscular strength is weak.

  Therefore, in the third embodiment, the muscle strength estimation unit 41 extracts the peak value of the load detected by the load sensor 31E as a feature, and estimates the muscle strength of the living body. In this case, a threshold value is set in advance for the peak value of the load, and the muscular strength estimation unit 41 compares the peak value of the load detected by the load sensor 31E with the set threshold value, and based on the comparison result, Estimate muscle strength.

  FIG. 15 is a flowchart illustrating an example of a flow of muscle strength estimation processing according to the third embodiment. The muscular strength estimation unit 41 extracts the peak value of the load with respect to the load sensor 31E as a feature (step S81). Next, the muscular strength estimation unit 41 determines whether or not the muscular strength is sufficient by comparing the peak value of the load detected by the load sensor 31E with the set threshold value (step S82).

  If the load peak value is equal to or greater than the set threshold value (step S82; Yes), the muscle force estimation unit 41 determines that the muscle strength of the living body is sufficient (step S83). If the peak load value is less than the set threshold (step S82; No), the muscle strength estimation unit 41 determines that the muscle strength of the living body is weak (step S84).

  In this example, the case where one load sensor 31E is attached to the back of the chair 3a has been described. However, the present invention is not limited thereto, and for example, a load sensor may be further attached to the front of the chair 3a. As described above, when one load sensor 31E is attached to the back of the chair 3a, muscle strength estimation at the time of sitting is performed by absolute evaluation. On the other hand, when the load sensor 31 is attached in front of the chair 3a, muscle strength estimation at the time of sitting can be relatively determined according to the state of the load before and after. Therefore, the state of the load on the chair 3a can be accurately determined, and the estimation accuracy of the muscle strength of the living body at the time of sitting can be improved.

  Further, by providing the load sensors 31 before and after the chair 3a, not only the estimation of muscle strength but also the center of gravity when the living body is seated on the chair 3a can be determined. Thereby, it can be determined whether the living body sat at the front or the rear at the moment of sitting.

  Furthermore, when there is a left-right difference in the muscle strength of the living body due to stroke hemiplegia or knee or hip surgery, the load sensors 31 may be attached to the left and right behind the chair 3a. Thereby, the state of the load on the left and right can be compared, and the degree of rehabilitation and the like can be estimated based on the comparison result.

Embodiment 4 FIG.
Next, a biological state estimation system according to Embodiment 4 of the present invention will be described. In the fourth embodiment, as in the second and third embodiments, the muscle strength estimation process for estimating the muscle strength of the living body that contacts the furniture 3 is performed as the biological state estimation process by the biological state estimation unit 40 in the first embodiment. . In the following description, the same reference numerals are given to portions common to the above-described first to third embodiments, and detailed description thereof is omitted.

  The biological state estimation system 100 according to the fourth embodiment is the same as the configuration shown in FIGS. 1 and 5 and will not be described. In the biological state estimation device 2 according to the fourth embodiment, as in the second and third embodiments, the estimation control device 20 performs the muscle strength estimation process as the biological state estimation process. The second embodiment is different from the second and third embodiments in that the muscle strength is estimated based on the state of the load when standing up from the chair 3a.

(Muscle strength estimation processing)
The muscle strength estimation process by the muscle strength estimation unit 41 according to the fourth embodiment will be described. Below, the case where a biological body stands up from the chair 3a as the furniture 3 shown in FIG. 3 is described as an example, and processing for estimating muscle strength of the standing biological body, particularly the thigh will be described.

  When the living body stands up from the chair 3a, as in the case of sitting on the chair 3a as shown in the second embodiment, the sitting surface of the chair 3a is used when the muscle strength of the living body is sufficient or weak. The load state for 3b is different. Therefore, in the muscle strength estimation processing according to the fourth embodiment, when the living body performs an operation of standing up from the chair 3a, the muscle strength of the living body is analyzed by analyzing the sensor information detected by each load sensor 31 in time series. Is estimated.

  Here, before describing the processing method of the muscle force estimation process, the state of the load on the seat surface 3b when the living body stands up from the chair 3a will be described.

(3) When muscle strength is sufficient FIG. 16: is the schematic for demonstrating the state of the load with respect to the seat surface 3b when the biological body with sufficient muscle strength of the thigh stands up from the chair 3a. In FIG. 16, the horizontal axis indicates time, and the vertical axis indicates load.

  When a living body with sufficient muscular strength stands up from the state of sitting on the chair 3a (FIG. 16 (a)), generally, the living body first tilts the trunk forward so that it bows, and the center of gravity line is Move forward (FIG. 16B). Next, the living body lifts the buttocks using the inertia when the barycentric line is moved (FIG. 16C) and stands up (FIG. 16D).

That is, in this case, as shown in FIG. 16 (e), living body began standing at some point t _5, the buttocks away from the seat surface 3b of the chair 3a, first, corresponding to the rear of the seat surface 3b The load on the load sensors 31C and 31D provided at the position gradually decreases. At the same time, the load on the load sensors 31A and 31B provided at the position corresponding to the front of the seat surface 3b gradually increases. Then, at time _{t 6,} the magnitude of the load is the minimum load for the load sensor 31C and 31D. Then, at time t _7, which is delayed in loading on the rear of the seat surface 3b, the load is reduced relative to the load sensor 31A and 31B provided in a position corresponding to the front of the seat surface 3b, the minimum load at time t ₈ .

When the living body is completely erected from a chair 3a, a constant value at load time t _8, which is detected by the load sensor 31A-31D, i.e. a state in which no load is applied by the biological, it remains stable. In this case, the time T ₃ required from the time point t ₅ at which the living body starts to stand up to the time point t ₇ at which the living body completely stands up is a relatively short time.

(4) When Muscle Strength is Weak FIG. 17 is a schematic diagram for explaining a state of a load on the seat surface 3b when a living body with weak muscle strength of the thigh stands up from the chair 3a. In FIG. 17, the horizontal axis indicates time, and the vertical axis indicates load.

  When a living body with weak muscular strength, such as an elderly person, stands up from a state where it is seated on the chair 3a (FIG. 17 (a)), it is generally difficult to stand up gradually. 17 (b)) has weak muscle strength and cannot withstand the load due to weight. Therefore, such a living body can finally stand up by repeating the standing-up operation (FIG. 17C).

That is, in this case, as shown in FIG. 17 (d), from a state in which the load during seating is not stable, because it attempts to biological suddenly standing at some point t _5, provided in a position relative to the front and rear of the seat surface 3b reduced load abruptly for all load sensors 31A~31D that is, once at time _t 6 _{(t 8),} the minimum load. Meanwhile, living body, can not be erected at a time, the same standing operation several times, after repeated increase and decrease of a sudden load is completely erected at t _9. Thus, until time t ₉ to fully erected from time t ₅ to begin the standing operation, repeatedly increasing and decreasing load on the load sensor 31A-31D, the load becomes constant value finally at time t _9, Maintain a stable state. In this case, at time T ₃ to time t ₉ to fully erected from time t ₅ to begin the standing operation, the maximum point of the load is measured a plurality of times.

  As described above, when a living body having different muscle strength performs a standing motion from the chair 3a, the load on the load sensors 31A to 31D provided on the chair 3a decreases depending on whether or not the muscle strength of the thigh is sufficient. The state of the load, such as the number of local maximum points detected from the start order to the complete standing, is different. Therefore, in this case, the muscle strength of the living body is estimated by extracting, as features, the order in which the loads on the load sensors 31A to 31D decrease and the number of local maximum points detected from the start of standing to the complete standing. can do.

Further, in this example, the time required for the living body to stand up from the chair 3a until it first stands up varies depending on the state of muscular strength. Therefore, the time until the time the organism was first erected from the time of starting to erect, that is, the time T ₅ when the time T ₃ and a biological muscle strength is weak when muscle of a living body is sufficient, when estimating the strength It can also be set as the feature.

  Thereby, the load state information generation part 27 produces | generates load state information based on the sensor information supplied for every setting time. Then, the muscular strength estimation unit 41 detects, from the load state information generated by the load state information generation unit 27, the order in which the load on each of the load sensors 31A to 31D decreases, and the maximum detected between the start of standing and the complete standing. The number of points or the time from the start of standing until the complete standing is extracted as a feature relating to muscle strength estimation. When the features relating to muscle strength estimation are extracted in this way, the muscle strength estimation unit 41 determines whether or not the muscle strength of the living body is sufficient based on the extracted features.

(E) When the order of loads on the load sensors 31A to 31D is a feature For example, consider a case where the order of increasing loads on the load sensors 31A to 31D is extracted as a feature. When the muscular strength of the living body is sufficient, in each of the load sensors 31A to 31D, the load detected by the rear load sensors 31C and 31D decreases, and then the load detected by the front load sensors 31A and 31B decreases. To do. Then, after the minimum load is detected by the rear load sensors 31C and 31D, the minimum load is detected by the front load sensors 31A and 31B.

That is, in this case, the time t ₆ the minimum load behind the load sensor 31C and 31D are detected, and the time point t ₈ the maximum load is detected in front of the load sensor 31A and 31B do not match.

  On the other hand, when the muscular strength of the living body is weak, the load detected by the rear load sensors 31C and 31D and the load detected by the front load sensors 31A and 31B simultaneously decrease in each of the load sensors 31A to 31D. . Then, the rear load sensors 31C and 31D and the front load sensors 31A and 31B reach the minimum load almost simultaneously.

That is, in this case, the time t ₆ the minimum load behind the load sensor 31C and 31D are detected, and the time t ₈ the minimum load in front of the load sensor 31A and 31B is detected to match.

  Here, “the timing at which the minimum load is detected coincides” means that the minimum load is detected by each of the load sensors 31A to 31D within a preset time. The set time at this time is a time when it can be considered that the minimum load has been detected at the same time, and specifically indicates, for example, about 0.2 seconds.

FIG. 18 is a flowchart showing a first example of the flow of muscle strength estimation processing according to the fourth embodiment. In this case, the muscular strength estimation unit 41 extracts the order of loads on the load sensors 31A to 31D as a feature (step S51). The muscle force estimation unit 41 includes a difference time T ₄ indicating a time difference between a time point t ₆ when the minimum load is detected by the load sensors 31C and 31D and a time point t ₈ when the minimum load is detected by the load sensors 31A and 31B. is calculated, depending on whether the calculated difference time T ₄ is included in the preset time, strength determines whether sufficient (step S52). When the difference time _{T 4} is not included within the set time (Step S52; Yes), muscle force estimation unit 41, the biological muscle strength is determined to be sufficient (step S53). Also, if the difference time _{T 4} is contained within the set time (Step S52; No), muscle force estimation unit 41 determines that the biometric muscle is weak (step S54).

(F) When characterized by the number of local maximum points during the standing-up operation Also, for example, during the time from when the living body starts to stand up until it completely stands up (hereinafter referred to as “standing-up operation time” as appropriate) Let us consider the case where the number of local maximum points detected is characterized. As described above, when the muscular strength of the living body is different, the standing operation from the chair 3a is different. Therefore, the number of maximum points of the load detected by each of the load sensors 31A to 31D during the standing operation time is different. Therefore, in this case, a threshold is set in advance for the maximum point of the load, and the muscle strength of the living body is estimated based on a comparison result between the maximum point of the load and the set threshold.

FIG. 19 is a flowchart showing a second example of the flow of muscle strength estimation processing according to the fourth embodiment. In this case, the muscular strength estimation unit 41 extracts the maximum point of the load detected during the standing motion time as a feature (step S61). Next, the muscular strength estimation unit 41 compares the maximum point detected by the load sensors 31 </ b> A to 31 </ b> D during the standing motion time T <b> ₃ with the set threshold value to determine whether or not the muscular strength is sufficient. Is determined (step S62).

  And when the number of local maximum points is less than a setting threshold value (step S62; Yes), the muscular strength estimation part 41 judges that the muscular strength of a biological body is enough (step S63). Moreover, when the number of local maximum points is more than a setting threshold value (step S62; No), the muscular strength estimation part 41 judges that the muscular strength of a biological body is weak (step S64). Note that the threshold value at this time is a value that does not exceed the number of maximum points generated when a living body with sufficient muscular strength stands up in a normal operation, and is specifically set to “2”, for example. .

(G) When characterized by the time of standing motion Further, for example, when extracting the time from when the living body starts standing until it first stands (hereinafter referred to as “initial standing motion time” as appropriate) think about. As described above, when the muscular strength of the living body is different, the initial standing operation time from the start to the first standing is different.

Therefore, in this case, after the decrease in the load is detected by the living body standing by the load sensors 31A to 31D, the living body first stands, and the magnitude of the load by the living body on each of the load sensors 31A to 31D is determined. A threshold is set for the initial standing up operation time until reaching “0” (time T _{3 in} FIG. 16E and time T ₅ in FIG. 17D). Then, based on the comparison result between the initial standing motion time and the set threshold, the muscle strength of the living body is estimated.

  FIG. 20 is a flowchart showing a third example of the flow of muscle strength estimation processing according to the fourth embodiment. In this case, the muscular strength estimation unit 41 extracts the initial standing motion time as a feature (step S71). Next, the muscular strength estimation unit 41 determines whether or not the muscular strength is sufficient by comparing the initial standing operation time with the set threshold value (step S72). If the initial standing operation time is equal to or longer than the set threshold (step S72; Yes), the muscle strength estimation unit 41 determines that the muscle strength of the living body is sufficient (step S73). Further, when the initial standing operation time is less than the set threshold (Step S72; No), the muscle strength estimation unit 41 determines that the muscle strength of the living body is weak (Step S74). Note that the threshold value at this time is a time that does not exceed the time required when a living body with sufficient muscular strength stands up in a normal operation, and is specifically set to about 0.4 seconds, for example.

In addition, although (g) demonstrated the case where the initial standing motion time was made into the characteristic regarding muscle strength estimation, it is not restricted to this, For example, standing motion time T until a living body fully stands after starting standing is demonstrated. ₃ may be a feature. In this case, standing operating time T ₃ in the weak strength vivo is thought to be longer than the standing operation time T ₃ in the body muscle strength is sufficient. Therefore, for example, a threshold is provided for the standing motion time T ₃ , and the muscle force estimation unit 41 determines that the muscle strength of the living body is sufficient when the standing motion time T ₃ is less than the set threshold, and the standing motion time T _3. Can be determined that the muscular strength of the living body is weak.

  In this example, the case where any one of (e) to (g) described above is extracted as a feature has been described. However, the present invention is not limited to this, and for example, any one of (e) to (g) or (e ) To (g) may be extracted as features.

  As described above, in the fourth embodiment, the biological state estimation unit 40 is based on the load state information obtained from the sensor information detected by the plurality of load sensors 31 attached to each leg 3c of the furniture 3. The muscle strength estimating unit 41 estimates the muscle strength of a living body seated on the furniture 3, and the muscle strength estimating unit 41 estimates the muscle strength of the living body based on the characteristics indicating the operation related to the standing with respect to the furniture 3.

  In addition, the characteristics indicating the operation related to standing at this time are the order in which the load due to the living body detected by the plurality of load sensors 31 decreases, and the standing time indicating the time from when the living body starts to stand up from the furniture 3 until it stands up. The number of local maximum points during the operation time or the standing operation time, and the muscle strength of the living body is estimated based on a comparison result between these features and a threshold value or the like set in advance for the features. Thereby, in this Embodiment 4, the muscular strength of a biological body can be estimated based on the operation | movement at the time of a biological body standing up from furniture, such as a chair.

Embodiment 5 FIG.
Next, a biological state estimation system according to Embodiment 5 of the present invention will be described. In the fifth embodiment, in the same manner as in the second to fourth embodiments, muscle strength estimation processing is performed on a living body seated on furniture 3 that can be seated such as chair 3a. And the seat surface height adjustment process which adjusts the height of the seat surface 3b in the chair 3a according to the estimated muscular strength is performed. In the following description, the same reference numerals are given to portions common to the above-described first to fourth embodiments, and detailed description thereof is omitted.

  The biological state estimation device 2 according to the fifth embodiment is the same as that of the second embodiment. In addition, in the chair 3a as the furniture 3 according to the fifth embodiment, the height of the seating surface 3b can be adjusted by control from the outside. In this example, the height of the seat surface 3b of the chair 3a is adjusted based on the control of the biological information collecting apparatus 1.

[Configuration of the collection controller]
FIG. 21 is a functional block diagram showing an example of the configuration of the collection control apparatus 10 according to the fifth embodiment. FIG. 21 illustrates only a portion related to the height adjustment of the seat surface 3b of the chair 3a, and illustration and description of other portions are omitted. As illustrated in FIG. 21, the collection control apparatus 10 includes an input processing unit 15, an output processing unit 16, a seating surface height determining unit 17, a seating surface drive control unit 18, and a storage unit 19.

  The input processing unit 15 receives the estimation information from the biological state estimation unit 40 and outputs the received estimation information to the seat surface height determination unit 17. The output processing unit 16 receives a seating surface control signal, which will be described later, from the seating surface drive control unit 18, and outputs the received seating surface control signal to the chair 3a.

  The seating surface height determination unit 17 refers to the seating surface height table stored in the storage unit 19 based on the estimation information received from the input processing unit 15, and the seating surface 3b corresponding to the living body seated on the chair 3a. Determine the height. The seating surface drive control unit 18 generates a seating surface control signal for setting the height of the seating surface 3 b to the height determined by the seating surface height determination unit 17, and outputs it to the output processing unit 16.

  The storage unit 19 stores in advance a seating surface height table that is used when determining the height of the seating surface 3b. The seat height table stored in the storage unit 19 is read based on a request from the seat height determining unit 17. In the seat height table, the estimated muscle strength and the height of the seat 3b are associated with each other. The height of the seat surface 3b associated with the estimated muscular strength is a height that is optimal for the living body seated on the chair 3a.

  Here, the optimum height for the living body is, for example, a height at which the living body can stand up from the chair 3a using the maximum force. Specifically, for example, for a living body with weak muscle strength, it is higher than the height of the seating surface 3b at the time of sitting, and for a living body with sufficient muscle strength, from the height of the seating surface 3b at the time of sitting. Is also low.

[Operation of biological state estimation system]
Next, the operation of the biological state estimation system 100 according to the fifth embodiment will be described with reference to FIG. In the living body state estimation system 100 according to the fifth embodiment, the process for estimating the muscle strength of the living body seated on the chair 3a is the same as in the second and third embodiments, and thus the description thereof is omitted here.

  When the estimation information indicating the muscle strength state of the living body seated on the chair 3a estimated by the estimation control device 20 of the biological state estimation device 2 is input to the collection control device 10, the input processing unit 15 receives the estimation information. Then, the received estimated information is supplied to the seat surface height determining unit 17.

  When receiving the estimation information from the input processing unit 15, the seating surface height determining unit 17 reads the seating surface height table from the storage unit 19. The seat surface height determination unit 17 performs a seat surface height adjustment process for determining the height of the seat surface 3b of the chair 3a corresponding to the muscle strength of the living body indicated by the estimation information with reference to the read seat surface height table. . Then, the seat surface height determination unit 17 supplies information indicating the height of the seat surface 3b determined by the seat surface height adjustment process to the seat surface drive control unit 18.

  Based on the information indicating the height of the seating surface 3b received from the seating surface height determining unit 17, the seating surface drive control unit 18 generates a seating surface control signal for controlling the height of the seating surface 3b, and outputs it. This is supplied to the processing unit 16. The output processing unit 16 outputs the seating surface control signal supplied from the seating surface drive control unit 18 to the chair 3a. Thereby, the height of the seat surface 3b of the chair 3a is set to the height indicated by the seat surface control signal, and the height of the seat surface 3b at this time is the optimum height for the living body seated on the chair 3a.

(Seat height adjustment process)
Next, the seat surface height adjustment process according to the fifth embodiment will be described. Usually, the height of the seat surface 3b of the chair 3a is set to a predetermined reference height such as 42 cm, for example, according to a standard or the like. When a living body is seated on such a chair 3a, if the living body tries to stand up, if the living body has sufficient muscle strength, it can easily stand up. On the other hand, if a living body with weak muscular strength tries to stand up, the muscular strength of the living body will be insufficient to stand up from the height of the seating surface 3b set to the reference height.

  As described above, the normal seating surface 3b has different muscular strengths required for standing up depending on the state of muscular strength of the living body. Therefore, in the fifth embodiment, the height of the seat surface 3b of the chair 3a is adjusted so that it can stand up from the chair 3a regardless of the state of the muscular strength of the living body and can perform appropriate training by standing up. The seat height adjustment process is performed.

  In this process, the collection control device 10 of the biological information collection device 1 increases the height of the seat surface 3b when the estimation information from the biological state estimation device 2 indicates that the muscular strength of the biological body is sufficient, for example. Set lower than the seating height. On the other hand, for example, when the estimation information indicates that the muscular strength of the living body is weak, the collection control device 10 sets the height of the seat surface 3b higher than the height at the time of sitting.

  As described above, in the living body state estimation system 100 according to the fifth embodiment, the furniture 3 is provided with the seat surface 3b on which the living body is seated, and the living body information collection device 1 is configured to increase the muscle strength of the living body and And a seating surface height determining unit 17 that determines the height of the seating surface 3b with reference to the seating surface height table based on the estimation information. And a seating surface drive control unit 18 that controls the height of the seating surface so that the seating surface 3b becomes the determined height of the seating surface.

  And the seat surface height determination part 17 makes the seat surface 3b of the furniture 3 lower than the height when the living body is seated when the estimated information indicates that the living body's muscle strength is sufficient, and the estimated information However, when it shows that the muscular strength of the living body is weak, the seating surface 3b of the furniture 3 is made higher than the height when the living body is seated. Thereby, it is possible to stand up from the chair 3a regardless of the state of the muscular strength of the living body and to perform appropriate training by standing up. In this case, the living body does not need to wear special equipment or the like and is not conscious of training.

Embodiment 6 FIG.
Next, a biological state estimation system according to Embodiment 6 of the present invention will be described. In the sixth embodiment, load state information is generated in the same manner as in the first to fifth embodiments, and a change in muscle strength from before in the living body is estimated based on the generated load state information. In the following description, the same reference numerals are given to portions common to the above-described first to fifth embodiments, and detailed description thereof is omitted.

  About the structure of the biometric information collection apparatus 1 and the furniture 3 which concern on this Embodiment 6, since it is the same as that of Embodiment 1-4, description is abbreviate | omitted. On the other hand, the biological state estimation device 2 is different from the first to fifth embodiments in that the estimation control device 20 performs a muscle force change estimation process as the biological state estimation process.

[Configuration of estimation control device]
FIG. 22 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the sixth embodiment. As illustrated in FIG. 22, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a muscle force change estimation unit 42.

  The muscle force change estimation unit 42 compares the current load state information generated by the load state information generation unit 27 with the past load state information stored in the storage device 22, and estimates a change in the muscle strength state of the living body. Perform muscle strength change estimation processing. The muscular strength change estimation unit 42 outputs information indicating the muscular strength change of the living body obtained by the estimation process as estimation information and stores the information in the storage device 22.

(Muscle strength estimation processing)
Next, the muscle force change estimation process by the muscle force change estimation unit 42 according to the sixth embodiment will be described. As described above, the load state information can be used to determine the state of muscle strength in the living body that contacts the furniture 3. Therefore, in the sixth embodiment, the load state information obtained when the sensor information is acquired is sequentially stored, the past load state information is compared with the current load state information, and the furniture 3 is contacted. A muscle force change estimation process for estimating a change in muscle strength in the living body is performed.

  The muscular strength change estimation unit 42 compares the current load state information obtained from the sensor information detected by the load sensor 31 with the past load state information stored in the storage device 22, and based on the comparison result, the furniture 3 The change of the muscular strength in the living body that contacts For example, the muscular strength change estimation unit 42 can determine that the living body is performing a similar operation on the furniture 3 such as an operation of sitting on a chair 3a as the furniture 3 from each of current and past load state information. Are extracted as features relating to the load. Then, the muscular strength change estimation unit 42 estimates the muscular strength change in the living body based on the load state indicated by the extracted feature.

  As described above, in the biological state estimation system 100 according to the sixth embodiment, the biological state estimation unit 40 is the muscle strength change estimation unit 42 that estimates a change in muscle strength in the living body based on the load state information. The estimation unit 42 estimates a change in muscle strength in the living body based on past load state information stored in the storage device 22 and current load state information generated by the load state information generation unit 27 based on the sensor information. . This makes it possible to measure changes in muscle strength without wearing special equipment on the body, such as muscle strength decline in the living body, degree of healing of injury, or comparison with muscle strength at an average age. We can estimate without being conscious of being.

Embodiment 7 FIG.
Next, a biological state estimation system according to Embodiment 7 of the present invention will be described. In the seventh embodiment, the load state information is generated in the same manner as in the first to sixth embodiments, and based on the generated load state information, the balance of the living body seated on the furniture 3 that can be seated such as the chair 3a is estimated. To do. In the following description, the same reference numerals are given to portions common to the above-described first to sixth embodiments, and detailed description thereof is omitted.

  About the structure of the biometric information collection apparatus 1 and the furniture 3 which concern on this Embodiment 7, since it is the same as that of Embodiment 1-4 and 6, description is abbreviate | omitted. On the other hand, the biological state estimation device 2 is different from the first to sixth embodiments in that the estimation control device 20 performs a balance estimation process as the biological state estimation process. Here, a case where a chair 3a on which a living body can be seated is used as the furniture 3 will be described as an example.

[Configuration of estimation control device]
FIG. 23 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the seventh embodiment. As illustrated in FIG. 23, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a balance estimation unit 43.

  The balance estimation unit 43 performs a balance estimation process for estimating the balance of the living body seated on the chair 3 a based on the load state information generated by the load state information generation unit 27. And the balance estimation part 43 is made to memorize | store in the memory | storage device 22, while outputting the information which shows the balance state of the biological body obtained by an estimation process as estimation information.

(Balance estimation process)
Next, the balance estimation process by the balance estimation unit 43 according to the seventh embodiment will be described. The balance estimation process according to the seventh embodiment is a process for estimating the balance of the living body seated on the chair 3a. The balance of the living body seated on the chair 3a is estimated based on the time from when the load detected by each of the load sensors 31A to 31D becomes maximum until the load becomes stable, and the fluctuation of the load when the load is stabilized. be able to.

  In this case, the balance estimation unit 43 is stable from the time when the load detected by each of the load sensors 31A to 31D included in the load state information generated by the load state information generation unit 27 becomes maximum, that is, the amplitude of the load is increased. The time until the state is within the preset range is extracted as a feature related to the load.

  In addition, the balance estimation unit 43 extracts the magnitude of the amplitude of the load when the load detected by each of the load sensors 31A to 31D is in a stable state as a feature related to the load. And the balance estimation part 43 estimates the balance of a biological body based on the extracted feature.

  Specifically, in this example, a threshold is set for the time from when the maximum load is detected by each of the load sensors 31A to 31D until the load on the load sensors 31A to 31D becomes stable. . Further, in this example, a threshold value is set for the magnitude of the amplitude of the load when the load by the living body on each of the load sensors 31A to 31D is stabilized. And the balance estimation part 43 estimates the balance of a biological body based on the result of having compared this time and the threshold value with respect to time, and also comparing the magnitude | size of the amplitude of a load with the threshold value with respect to the magnitude of an amplitude.

  The balance estimation unit 43 has a time from when the maximum load is detected by each of the load sensors 31A to 31D to when the load by the living body on each of the load sensors 31A to 31D becomes stable, is equal to or less than the threshold value, and the load is When the amplitude of the load in the stable state is equal to or less than the threshold value, it is estimated that the balance of the living body is sufficient. In addition, the balance estimation unit 43 estimates that the balance of the living body is not sufficient in other cases.

  The threshold for the time at this time is a value corresponding to a standard time required when a living body with sufficient balance is seated in a normal operation, and specifically, for example, set to about 1 second. Is done. Further, the threshold for the magnitude of the amplitude of the load is a value at which it can be determined that the balance of the living body is sufficient. Specifically, for example, the threshold is set to ± 2 times the standard deviation in the amplitude for 1 second.

  As described above, in the biological state estimation system 100 according to Embodiment 7, the biological state estimation unit 40 is the balance estimation unit 43 that estimates the balance of the biological body based on the load state information. Based on the time from when the load detected by the plurality of load sensors 31 included in the load state information is maximized until the load is stabilized and the amplitude of the load when the load is stabilized, the balance of the living body is determined. presume. Thereby, it is possible to estimate whether or not the balance of the living body is sufficient without attaching a special instrument or the like to the living body and without being conscious of measuring the balance.

Embodiment 8 FIG.
Next, a biological state estimation system according to Embodiment 8 of the present invention will be described. In the eighth embodiment, the load state information is generated in the same manner as in the first to seventh embodiments, and based on the generated load state information, the dozing of the living body sitting on the furniture 3 that can be seated such as the chair 3a is estimated. To do. In the following description, the same reference numerals are given to portions common to the above-described first to seventh embodiments, and detailed description thereof is omitted.

  About the structure of the biometric information collection apparatus 1 and the furniture 3 which concern on this Embodiment 8, since it is the same as that of Embodiment 1-4, 6 and 7, description is abbreviate | omitted. On the other hand, the biological state estimation device 2 differs from the first to seventh embodiments in that the estimation control device 20 performs a dozing estimation process as the biological state estimation process. Here, a case where a chair 3a on which a living body can be seated is used as the furniture 3 will be described as an example.

[Configuration of estimation control device]
FIG. 24 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the eighth embodiment. As illustrated in FIG. 24, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a dozing estimation unit 44.

  Based on the load state information generated by the load state information generation unit 27, the dozing estimation unit 44 performs a dozing estimation process for estimating the dozing of the living body sitting on the chair 3a. Then, the dozing estimation unit 44 outputs information indicating the dozing state of the living body obtained by the estimation process as the estimation information and causes the storage device 22 to store the information.

(Dozing estimation process)
Next, a dozing estimation process performed by the dozing estimation unit 44 according to the eighth embodiment will be described. In general, it is considered that the living body sitting on the chair 3a in the state of falling asleep is less swayed than the state of waking up, and the state is sustained to some extent. Therefore, in the eighth embodiment, dozing estimation for estimating dozing of the living body based on the magnitude of the amplitude of the load detected by each of the load sensors 31A to 31D in the stable state of the living body sitting on the chair 3a. Process.

  In this case, the dozing estimation unit 44 extracts, based on the load state information generated by the load state information generation unit 27, the magnitude of the load amplitude in the portion indicating the stable state of the living body as a feature related to the load. Then, the dozing estimation unit 44 estimates the dozing of the living body based on the extracted features.

  Specifically, in this example, a threshold is set for the magnitude of the amplitude of the load when the load on the load sensors 31A to 31D is stabilized by the living body. Then, the dozing estimation unit 44 estimates the dozing of the living body based on a result of comparing the magnitude of the amplitude of the load with a threshold value for the magnitude of the amplitude.

  The dozing estimation unit 44 is configured such that the magnitude of the amplitude of the load when the load by the living body on each of the load sensors 31A to 31D is in a stable state is less than or equal to the threshold value, and the state lasts for a preset time. If it is, it is estimated that the living body is dozing. The dozing estimation unit 44 estimates that the living body is not dozing when the magnitude of the amplitude of the load exceeds the threshold.

  The threshold for the magnitude of the amplitude of the load at this time is a value at which it can be determined that the living body seated on the chair 3a is not asleep while being awake. It is set to ± 2 times the standard deviation in the amplitude in seconds.

  As described above, in the biological state estimation system 100 according to the eighth embodiment, the biological state estimation unit 40 is the dozing estimation unit 44 that estimates the dozing of the living body based on the load state information, and the dozing estimation unit 44 includes Based on the magnitude of the amplitude of the load when the loads detected by the plurality of load sensors included in the load state information are stabilized, the dozing of the living body is estimated. Thereby, it is estimated whether the living body seated on the chair 3a is dozing without attaching a special instrument or the like to the living body and without being conscious of measuring the dozing state. be able to.

Embodiment 9 FIG.
Next, a biological state estimation system according to Embodiment 9 of the present invention will be described. In the ninth embodiment, load state information is generated in the same manner as in the first to eighth embodiments, and the posture of the living body in contact with the furniture 3 is estimated based on the generated load state information. In the following description, the same reference numerals are given to portions common to the above-described first to eighth embodiments, and detailed description thereof is omitted.

  Since the configuration of biological information collecting apparatus 1 according to the ninth embodiment is the same as that of the first to fourth and sixth to eighth embodiments, the description thereof is omitted. On the other hand, the biological state estimation device 2 is different from the first to eighth embodiments in that the estimation control device 20 performs a body posture estimation process as the biological state estimation process. Here, a case where a bed is used as the furniture 3 will be described as an example.

[Composition of furniture]
FIG. 25 is a schematic diagram illustrating an example of the configuration of the bed 3e as the furniture 3 according to the ninth embodiment. As shown in FIG. 25, the bed 3e includes a main body that comes into contact with a living body and a plurality of legs 3g that support the main body.

The bed 3e is provided with a bedding surface 3f on which a living body can lie.
Each sensor unit 30 including a load sensor 31 is attached to each leg 3g of the bed 3e. Specifically, for example, the sensor unit 30 including the load sensor 31A is attached to the right front leg with the living body lying on the bed 3e, and the sensor unit 30 including the load sensor 31B includes the left front leg. Attached to. The sensor unit 30 including the load sensor 31C is attached to the right rear leg, and the sensor unit 30 including the load sensor 31D is attached to the left rear leg.

  The sensor unit 30 is attached, for example, between the bedding surface 3f and the leg 3g of the bed 3e, or at a position where the leg 3g contacts the floor surface.

[Configuration of estimation control device]
FIG. 26 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the ninth embodiment. As illustrated in FIG. 26, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a body position estimation unit 45.

  The body position estimation unit 45 performs body position estimation processing for estimating the body position of the living body lying on the bed 3e based on the load state information generated by the load state information generation unit 27. The body position estimation unit 45 then outputs information indicating the body position of the living body obtained by the estimation process as estimated information, and stores the information in the storage device 22.

(Posture estimation process)
Next, the posture estimation process by the posture estimation unit 45 according to the ninth embodiment will be described. The body posture estimation process according to the ninth embodiment is a process for estimating the body posture of the living body lying on the bed 3e. In general, when a living body is sleeping, it maintains a certain posture, but when sleep is shallow, it is known that the body turns unconsciously. Therefore, in the posture estimation process according to the ninth embodiment, the posture when the living body is sleeping on the bed 3e is estimated based on the load state information obtained by using the sensor information detected by each load sensor 31. To do.

  FIG. 27 is a schematic diagram illustrating an example of load state information used in the body posture estimation process according to the ninth embodiment. FIG. 27A is a graph showing the state of a living body analyzed using a three-dimensional motion analysis apparatus capable of analyzing the motion of the living body in three dimensions as a reference example. Moreover, FIG.27 (b) shows the graph which shows the state of the load with respect to each load sensor 31A-31D. The graphs shown in FIGS. 27A and 27B are obtained by acquiring data from the same living body at the same time. In addition, FIG.27 (b) shows the magnitude | size of the load of each load sensor 31A-31D with respect to time. Here, if the temporal change in the magnitude of the load in each load sensor 31 can be confirmed, the magnitude of the load itself is not particularly important, so that the temporal change in the magnitude of the load can be clearly confirmed. It is assumed that the graph of each load sensor 31 is displayed while being shifted in the vertical axis direction.

  In FIG. 27A, when the waveform is present on the upper side, that is, in the positive direction, the living body lies with the right side down, and when the waveform is present on the lower side, that is, in the negative direction, Shown lying on the left side down.

  When comparing FIG. 27 (a) and FIG. 27 (b), the load applied to the load sensor 31A attached to the right front leg 3g is almost the same as when the living body is in a state with the right side down. Has increased. In addition, although the magnitude | size of a load differs also about the load with respect to the load sensor 31C attached to the leg part 3g of the right back, it has increased.

  In addition, the load on the load sensor 31B attached to the left front leg 3g is increasing at approximately the same time as when the living body is lying on the left side. In addition, although the magnitude | size of a load differs also about the load with respect to the load sensor 31D attached to the left back leg part 3g, it has increased.

  Thus, when the living body turns over, the load applied to the load sensor 31 changes accordingly. Therefore, in this case, the body position of the living body can be estimated by extracting the change in the load with respect to each of the load sensors 31A to 31D as a feature.

  Thereby, the body posture estimation part 45 extracts the change of the load with respect to each load sensor 31A-31D from the load state information produced | generated by the load state information generation part 27 as a characteristic. Then, the body position estimation unit 45 estimates the body position of the living body lying on the bed 3e based on the extracted features.

  At this time, the body position estimation unit 45 is in a state where the living body lies with the right side down when the load on at least the load sensor 31A among the right front load sensor 31A and the right rear load sensor 31C increases. Judge that there is. The body position estimation unit 45 is in a state where the living body lies with the left side face down when the load on at least the load sensor 31B among the left front load sensor 31B and the left rear load sensor 31D increases. Judge.

  As described above, in the biological state estimation system 100 according to the ninth embodiment, the biological state estimation unit 40 is the body position estimation unit 45 that estimates the body position based on the load state information. The body position of the living body is estimated based on changes in the loads applied to the plurality of load sensors 31 by the living body. Thereby, the posture of the living body in contact with the furniture 3 can be estimated without wearing a special instrument or the like with respect to the living body and without being aware that the posture is being measured.

Embodiment 10 FIG.
Next, a biological state estimation system according to Embodiment 10 of the present invention will be described. In the tenth embodiment, load state information is generated in the same manner as in the first to ninth embodiments, and the posture of the living body in contact with the furniture 3 is estimated based on the generated load state information. In the following description, the same reference numerals are given to portions common to the above-described first to ninth embodiments, and detailed description thereof is omitted.

  About the structure of the biological condition estimation system 100 which concerns on this Embodiment 10, it is the same as that of Embodiment 9, but here, as the furniture 3, what a living body can sit down, especially the point which uses a western style toilet bowl is used. This is different from the ninth embodiment.

  In general, when defecation is performed with a Western-style toilet, it is preferable to take a forward leaning posture while a living body is seated on a toilet seat. Therefore, in the tenth embodiment, the body position when the living body is seated on the toilet seat of the Western-style toilet is estimated.

[Composition of furniture]
FIG. 28 is a schematic diagram illustrating an example of a configuration of a Western-style toilet 3h as the furniture 3 according to the tenth embodiment. The western toilet 3h is provided with a toilet seat 3i for a living body to sit on. Load sensors 31 </ b> A to 31 </ b> D are attached to the back surface of the toilet seat 3 i (FIG. 28A) or the bottom of the western style toilet 3 h. Specifically, for example, the load sensor 31A is attached to the right front of the western toilet 3h while the living body is seated on the western toilet 3h, and the load sensor 31B is attached to the left front of the western toilet 3h. The load sensor 31C is attached to the right rear of the western toilet 3h, and the load sensor 31D is attached to the left rear of the western toilet 3h.

(Posture estimation process)
Next, the posture estimation process performed by the posture estimation unit 45 according to the tenth embodiment will be described. The body position estimation process according to the tenth embodiment is a process for estimating the body position when the living body sits on the Western-style toilet 3h and uses the stool. When the living body sits on the Western-style toilet 3h and uses the stool, the state of the load applied to the Western-style toilet 3h or the toilet seat 3i is different. Therefore, in the body posture estimation process according to the tenth embodiment, when the living body sits on the Western-style toilet 3h and uses the stool, the body position of the living body is estimated based on the sensor information detected by each load sensor 31.

  For example, in the case where a living body sits on a Western-style toilet 3h and uses the upper body in an upright state (hereinafter referred to as “upright sitting position” as appropriate), the living-body is seated in a balanced manner on the Western-style toilet 3h. become. Therefore, the load sensors 31A to 31D detect substantially the same load. On the other hand, for example, when a living body sits on a Western-style toilet 3h and uses a bowed state (hereinafter appropriately referred to as “forward-bending sitting position”), the front of the western-style toilet 3h is more forward than the rear. Many loads are applied.

  Therefore, the load detected by the load sensors 31A and 31B attached to the front of the western-style toilet 3h increases more than the load in the upright sitting position. Moreover, the load detected by the load sensors 31C and 31D attached to the rear is smaller than the load in the upright sitting position. That is, in this case, the loads detected by the load sensors 31A and 31B are larger than the loads detected by the load sensors 31C and 31D.

  As described above, when a living body having a different body posture uses the Western toilet 3h, the magnitude of the load applied to the front load sensors 31A and 31B and the rear load sensors 31C and 31D depending on the body posture. Is different. Therefore, in such a case, the body position of the living body is estimated by extracting the distribution of the load detected by each of the load sensors 31A to 31D in a state where the living body is seated on the Western-style toilet 3h as a feature. can do.

  Thereby, the body posture estimation unit 45 extracts the distribution of the load detected by each of the load sensors 31A to 31D from the load state information generated by the load state information generation unit 27 as a feature related to the body posture estimation. Then, the body position estimation unit 45 determines the body position of the living body to be used based on the extracted features.

  Based on the extracted features, when the distribution of the load detected by each of the load sensors 31A to 31D is substantially uniform, that is, when the magnitude of the load detected by each of the load sensors 31A to 31D is substantially the same, 45 determines that the body posture is an upright sitting position. In addition, when the loads detected by the load sensors 31A and 31B are larger than the loads detected by the load sensors 31C and 31D, the body position estimation unit 45 determines that the body position is the forward bending sitting position. On the other hand, when the relationship between the loads detected by the load sensors 31A to 31D is other than the relationship described above, the body position estimation unit 45 determines that the body position of the living body is another sitting position.

  In this case, for example, it is preferable to estimate the posture in consideration of the body weight of the living body. Specifically, for example, when the living body is seated on the Western-style toilet 3h, the weight obtained by subtracting the total load applied to each of the load sensors 31A to 31D by the living body sitting on the Western-style toilet 3h from the body weight of the living body. Is calculated. Then, the state of the living body leg is estimated based on the calculated weight, and the body position of the living body is estimated based on the estimated state of the living body leg and the distribution or fluctuation of the load detected by each of the load sensors 31A to 31D. Thereby, the estimation accuracy of the body posture can be improved.

  Further, in this example, since it is only necessary to determine whether or not the posture of the living body seated on the western-style toilet 3h is the forward-bending position, the body position estimation unit 45 can detect the load detected by the load sensors 31A and 31B, for example. What is necessary is just to judge whether it is larger than the load detected by the load sensors 31C and 31D.

  Furthermore, when each load sensor 31A-31D is attached to the bottom part of the western style toilet 3h, in this Embodiment 10, in addition to the body position estimation, the load detected by the load sensors 31A-31D before and after the stool The amount of excretion due to urination and defecation can also be estimated by calculating the difference amount.

  As described above, in the living body state estimation system 100 according to the tenth embodiment, the furniture 3 is the western toilet 3h having the toilet seat 3i on which the living body is seated, and each of the plurality of load sensors 31 is attached to the toilet seat 3i. The body position estimation unit 45 estimates the body position of the living body seated on the Western-style toilet 3h based on the load state information. Thereby, the posture of the living body seated on the toilet seat 3i can be estimated without wearing a special instrument or the like with respect to the living body and without being conscious of measuring the posture.

Embodiment 11 FIG.
Next, a biological state estimation system according to Embodiment 11 of the present invention will be described. In the eleventh embodiment, load state information is generated in the same manner as in the first to tenth embodiments, and based on the generated load state information, the posture of the living body seated on the furniture 3 that can be seated such as the chair 3a is low back pain. It is estimated whether or not there is a risk of causing this. In the following description, the same reference numerals are given to portions common to the above-described first to tenth embodiments, and detailed description thereof is omitted.

  About the structure of the biometric information collection apparatus 1 and the furniture 3 which concern on this Embodiment 11, since it is the same as that of Embodiment 1-4 and 6-10, description is abbreviate | omitted. On the other hand, the biological state estimation device 2 is different from Embodiments 1 to 10 in that the estimation control device 20 performs a back pain estimation process as the biological state estimation process. Here, a case where a chair 3a on which a living body can be seated is used as the furniture 3 will be described as an example.

[Configuration of estimation control device]
FIG. 29 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the eleventh embodiment. As illustrated in FIG. 29, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a back pain estimation unit 46.

  Based on the load state information generated by the load state information generation unit 27, the low back pain estimation unit 46 performs low back pain estimation processing for estimating whether or not the body position of the living body sitting on the chair 3a may cause back pain. . Then, the back pain estimation unit 46 outputs information indicating the back pain state of the living body obtained by the estimation process as the estimation information and stores it in the storage device 22.

  In the eleventh embodiment, the storage device 22 stores the back pain posture information in advance. The back pain posture information is information indicating the state of the load detected by each of the load sensors 31A to 31D when the living body at the time of back pain is seated on the chair 3a, and is used when performing back pain estimation processing.

(Back pain estimation process)
Next, back pain estimation processing by the back pain estimation unit 46 according to Embodiment 11 will be described. In general, it is known that maintaining a seated state on the chair 3a causes back pain. Moreover, even if it is the state seated on the chair 3a, the burden concerning the waist | hip | lumbar of a biological body changes with the attitude | positions greatly.

  Then, when sitting on the chair 3a in the state where the back pain has developed, the muscle strength of the waist is weakened, so it is difficult to sit in the correct posture due to the pain. Therefore, the living body changes its posture so as to avoid pain. Thus, in the eleventh embodiment, low back pain estimation processing is performed to estimate whether or not the body position of the living body may cause low back pain based on changes in the load detected by the load sensors 31A to 31D.

  Specifically, in this example, first, low back pain posture information indicating a state of a load on the chair 3a in a living body developing low back pain is acquired in advance. Then, based on the result of comparing the load state information based on the load detected by each of the load sensors 31 </ b> A to 31 </ b> D and the previously acquired low back pain posture information, it is estimated whether or not the living body may cause low back pain.

  The back pain estimation unit 46 reads back pain posture information stored in the storage device 22 and compares the load state information generated by the load state information generation unit 27 with the back pain posture information. When the load state information matches the back pain posture information, the back pain estimation unit 46 estimates that the living body may cause back pain.

  As described above, in the biological state estimation system 100 according to the eleventh embodiment, the biological state estimation unit 40 estimates whether or not the biological body may cause low back pain based on the load state information. The storage device 22 stores low back pain posture information indicating the state of load in the living body that develops low back pain, and the low back pain estimation unit 46 causes low back pain in the living body based on the load state information and low back pain posture information. Estimate whether there is a risk.

  Thereby, it is possible to determine whether or not the living body sitting on the chair 3a may cause back pain without wearing a special instrument or the like on the living body and not being aware of the posture measurement. Can be estimated. And the onset of the low back pain with respect to a biological body can be suppressed.

Embodiment 12 FIG.
Next, a biological state estimation system according to Embodiment 12 of the present invention will be described. In the twelfth embodiment, the load state information is generated in the same manner as in the first to eleventh embodiments, and the tremor of the living body in contact with the furniture 3 is estimated based on the generated load state information. In the following description, the same reference numerals are given to portions common to the above-described first to eleventh embodiments, and detailed description thereof is omitted.

  The configurations of biological information collecting apparatus 1 and furniture 3 according to the twelfth embodiment are the same as those of the first to fourth, sixth to eighth, and eleventh embodiments, and thus the description thereof is omitted. On the other hand, the biological state estimation device 2 is different from Embodiments 1 to 4, 6 to 8, and 11 in that the estimation control device 20 performs a tremor estimation process as the biological state estimation process. Here, a case where a chair 3a on which a living body can be seated is used as the furniture 3 will be described as an example.

[Configuration of estimation control device]
FIG. 30 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the twelfth embodiment. As illustrated in FIG. 30, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a tremor estimation unit 47.

  Based on the load state information generated by the load state information generation unit 27, the tremor estimation unit 47 performs tremor estimation processing for estimating tremor of a living body sitting on the chair 3a, that is, tremor. And the tremor estimation part 47 outputs the information which shows the state of the tremor of the biological body obtained by an estimation process as estimated information, and memorize | stores it in the memory | storage device 22. FIG.

(Tremor estimation process)
Next, tremor estimation processing by the biological state estimation system 100 according to Embodiment 12 will be described. In general, tremor is thought to be due to a decrease in the coordination of nerves and muscles due to aging, or abnormal movement adjustment. In addition, even in a neurological disease such as mild dementia (MCI), Parkinson's disease or parkinsonism, tremor or smoothness of motion at rest can be confirmed.

  FIG. 31 is a schematic diagram for explaining tremor. In FIG. 31A, the horizontal axis represents time, and the vertical axis represents the magnitude of the load. In FIG. 31 (b), the horizontal axis represents frequency, and the vertical axis represents power due to load.

  As shown in FIG. 31A, in a living body in which tremor is observed, continuous amplitude fluctuations having a constant period can be confirmed even when the state of the living body is stable. The amplitude fluctuation due to tremor at this time has a specific frequency as shown in FIG. 31B, and the amplitude fluctuation of the load due to tremor in this example has a frequency of 4 Hz, for example.

  Therefore, in the twelfth embodiment, the tremor of the living body is estimated from continuous and periodic amplitude fluctuations having a specific frequency based on the change in the load detected by each of the load sensors 31A to 31D, and Parkinson's disease, Biological conditions such as physiological tremor due to various neurological diseases or aging are judged.

  FIG. 32 is a flowchart illustrating an example of a tremor estimation process according to the twelfth embodiment. First, the tremor estimation unit 47 acquires the load state information for each set time generated by the load state information generation unit 27 (step S91). The tremor estimation unit 47 extracts a frequency component based on the acquired load state information for each set time (step S92).

  The tremor estimation unit 47 determines whether or not a specific frequency is included in the extracted frequency component (step S93). When the extracted frequency component includes a specific frequency (step S93; Yes), the tremor estimation unit 47 determines that the load state information indicates tremor (step S94). On the other hand, when the specific frequency is not included in the extracted frequency component (step S93; No), the tremor estimation unit 47 determines that the load state information is not tremor (step S95).

(Effective time estimation process)
By the way, for such diseases that cause abnormalities in the coordination of nerves and muscles, pharmacotherapy is common, but since the appropriate amount of drug differs depending on the symptoms, adjustment of the amount of drug to the living body can be adjusted. Have difficulty. Here, after the living body takes the drug, the effect of the drug (hereinafter referred to as “medicine” as appropriate) disappears, and if the time until tremor occurs again is known, the medicinal effect on tremor can be determined. .

  Therefore, in the twelfth embodiment, the time of taking the drug is acquired in advance, and the medicinal effect is determined based on the time of taking the drug and the estimation result of tremor.

  In this example, the biological state estimation system 100 first obtains in advance a time for taking a drug for suppressing tremor (hereinafter referred to as “drug taking time” as appropriate) and stores it in the storage device 22. .

  The tremor estimation unit 47 estimates a tremor having a specific frequency based on the load state information generated by the load state information generation unit 27. Also, the tremor estimation unit 47 reads out the drug taking time stored in the storage device 22. Then, the tremor estimation unit 47 estimates the drug effect time (hereinafter referred to as “medicinal effect time” as appropriate) based on the read time indicated by the drug taking time and the estimated time when the tremor appeared. The tremor estimation unit 47 outputs this medicinal duration as estimation information.

  As described above, in the biological state estimation system 100 according to the twelfth embodiment, the biological state estimation unit 40 is the tremor estimation unit 47 that estimates biological tremor based on the load state information. If there is a continuous and periodic amplitude fluctuation having a specific frequency, it is determined that the load state information indicates tremor. Thereby, tremor can be estimated without the living body being conscious.

  Further, the storage device 22 stores a drug taking time indicating a time when the living body has taken a drug for suppressing tremor, and the tremor estimation unit 47 is estimated based on the drug taking time and the load state information. Based on the time when tremor appears, the duration of the drug taken by the living body is estimated. Thereby, the medication time and the amount of the medication to be taken can be appropriately set according to the symptoms. In this case, the effect time of the drug can be estimated without being conscious of the living body.

Embodiment 13 FIG.
Next, a biological state estimation system according to Embodiment 13 of the present invention will be described. In the thirteenth embodiment, load state information is generated in the same manner as in the first to twelfth embodiments, and based on the load state information, the living body defecate sitting on the chair 3a as the furniture 3 in contact with the furniture 3 Is estimated. In the following description, the same reference numerals are given to portions common to the above-described first to twelfth embodiments, and detailed description thereof is omitted.

  About the structure of the biometric information collection apparatus 1 and the furniture 3 which concern on this Embodiment 13, since it is the same as that of Embodiment 1-4, 6-8, 11 and 12, description is abbreviate | omitted. On the other hand, the biological state estimation device 2 is different from the first to twelfth embodiments in that the estimation control device 20 performs the defecation estimation process as the biological state estimation process.

[Configuration of estimation control device]
FIG. 33 is a functional block diagram showing an example of the configuration of the estimation control apparatus 20 according to the thirteenth embodiment. As illustrated in FIG. 33, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a defecation estimation unit 48 as a biological state estimation unit 40.

  The defecation estimation unit 48 performs a defecation estimation process that estimates the defecation of a living body that contacts the furniture 3 based on the load state information generated by the load state information generation unit 27. And the defecation estimation part 48 outputs the information which shows the state of the defecation of the biological body obtained by the defecation estimation process as estimation information, and memorize | stores it in the memory | storage device 22. FIG.

(Defecation estimation process)
Next, defecation estimation processing by the defecation estimation unit 48 will be described. The defecation estimation process is a process of estimating the defecation of the living body seated on the chair 3a. For example, when a living body with dementia does not know the location of the toilet, cannot understand a series of excretion movements, cannot understand the necessity of excretion, or cannot communicate a request for excretion to a caregiver, etc. In many cases, there is a restless behavior, such as taking a unique action to vibrate the thighs. Therefore, in the thirteenth embodiment, defecation estimation processing for estimating a state before defecation by a living body is performed by detecting a specific action such as vibrating the body.

  In this example, the defecation estimation unit 48 loads the vibration based on the load state information generated by the load state information generation unit 27 as a feature indicating the operation related to defecation as a characteristic of continuous vibration due to a specific operation during defecation. It is determined whether or not it can be extracted from the state information. And when the defecation estimation part 48 can extract the vibration at the time of defecation, it determines that the living body is in the state before defecation. The defecation estimation unit 48 outputs information indicating the state of the defecation as estimation information.

  As described above, in the biological state estimation system 100 according to the thirteenth embodiment, the biological state estimation unit 40 is the defecation estimation unit 48 that estimates biological body defecation based on the load state information. Based on the load state information, continuous vibrations due to unique operations during defecation are detected, and defecation is estimated based on the detected continuous vibrations. As a result, even when the living body cannot transmit a request for defecation to a caregiver or the like, defecation can be estimated without the living body being aware of it.

Embodiment 14 FIG.
Next, a biological state estimation system according to Embodiment 14 of the present invention will be described. In the fourteenth embodiment, the load state information is generated in the same manner as in the first to thirteenth embodiments, and the chair 3a as the furniture 3 on which the living body is seated is based on the generated load state information and the weight of the living body. The height of the surface 3b is adjusted. In the following description, the same reference numerals are given to portions common to the above-described first to thirteenth embodiments, and detailed description thereof is omitted.

[Configuration of biological state estimation system]
FIG. 34 is a block diagram illustrating an example of the configuration of the biological state estimation system 100 according to the fourteenth embodiment. As shown in FIG. 22, the biological state estimation system 100 includes a biological information collection device 1, a biological state estimation device 2, and furniture 3, and the biological information collection device 1 and the biological state estimation device 2 are connected by a network 4. Yes.

(Biological information collection device)
The biological information collection device 1 includes a collection control device 10, an input / output interface (I / F) 11, a display device 12, and an input device 13, similarly to the biological information collection device 1 shown in FIG. Note that a description of portions common to the biological information collecting apparatus 1 illustrated in FIG. 1 is omitted. In this example, the configuration of the collection control apparatus 10 is the same as that of the collection control apparatus 10 according to the fifth embodiment shown in FIG.

  The collection control device 10 includes sensor information, which is information indicating detection results from the load sensors 31 </ b> A to 31 </ b> D attached to the furniture 3, and an ID for identifying a living individual from the personal information acquisition device 51 provided in the furniture 3 ( Personal information such as IDentification) is received, and the received sensor information and personal information are supplied to the network 4 via the input / output I / F 11.

  Further, when the collection control device 10 receives the estimation information from the biological state estimation device 2, the collection control device 10 controls the height of the seat surface 3b based on information indicating the height of the seat surface 3b of the chair 3a included in the estimation information. To generate a seating surface control signal and supply it to the chair 3a.

(Biological state estimation device)
The biological state estimation device 2 includes an estimation control device 20, an input / output interface (I / F) 21, and a storage device 22, similarly to the biological state estimation device 2 shown in FIG. In addition, description is abbreviate | omitted about the part which is common in the biological condition estimation apparatus 2 shown in FIG.

  The estimation control device 20 receives the sensor information and personal information received from the biological information collection device 1 via the network 4 and the input / output I / F 21 via the input / output I / F 21 and stores them in the storage device 22 described later. . The storage device 22 stores a weight table in which personal information and weight information indicating the weight of a living body corresponding to the personal information are associated with each other.

  Based on the sensor information and the weight table, the estimation control device 20 performs seat surface height estimation processing for estimating the appropriate height of the seat surface 3b with respect to the living body seated on the chair 3a. Then, the estimation control device 20 transmits, to the biological information collection device 1 via the input / output I / F 21 and the network 4, estimated information indicating an appropriate seating surface height with respect to the living body obtained by the seating surface height estimation process. To do.

(furniture)
The furniture 3 is, for example, a chair 3a whose height of the seat surface 3b can be adjusted by control from the outside, and a plurality of load sensors 31A to 31D are attached in the same manner as the furniture 3 shown in FIG. The furniture 3 is provided with a personal information acquisition device 51.

  The personal information acquisition device 51 communicates with, for example, an RFID (Radio Frequency IDentification) tag attached to a living body, acquires personal information of the living body such as an ID, and supplies the personal information to the biological information collecting apparatus 1.

[Configuration of estimation control device]
FIG. 35 is a functional block diagram showing an example of the configuration of the estimation control device 20 in FIG. As illustrated in FIG. 35, the estimation control device 20 includes an input processing unit 25, an output processing unit 26, a load state information generation unit 27, and a seat surface height estimation unit 49 as the biological state estimation unit 40.

  The input processing unit 25 receives the sensor information and personal information received from the biological information collection device 1 and outputs the received sensor information and personal information to the load state information generation unit 27.

  The load state information generation unit 27 stores the sensor information and personal information received from the input processing unit 25 in the storage device 22. Further, the load state information generation unit 27 generates load state information based on the sensor information, and stores the load state information in the storage device 22.

  The seat surface height estimation unit 49 refers to the weight table based on the personal information and acquires the weight information of the living body seated on the chair 3a. And the seat surface height estimation part 49 estimates the height of the appropriate seat surface 3b with respect to the biological body seated on the chair 3a based on the load state information and weight information which were produced | generated by the load state information generation part 27. The seat surface height estimation process is performed. And the seat surface height estimation part 49 is made to memorize | store in the memory | storage device 22 while outputting the estimation information which shows the result of a seat surface height estimation process.

[Operation of biological state estimation system]
Next, the operation of biological state estimation system 100 according to the fourteenth embodiment will be described with reference to FIGS.

  First, each load sensor 31 provided in the chair 3a as the furniture 3 detects a load for each preset time and outputs the detection result as sensor information. Moreover, the personal information acquisition apparatus 51 provided in the chair 3a acquires personal information from the living body seated on the chair 3a.

  Sensor information and personal information output from each load sensor 31 is supplied to the biological information collecting apparatus 1. The biological information collection device 1 receives sensor information and personal information and outputs them to the outside via the collection control device 10 and the input / output I / F 11. The sensor information and personal information output from the biological information collecting device 1 are supplied to the biological state estimating device 2 via the network 4.

  The biological state estimation device 2 receives sensor information and personal information supplied from the biological information collection device 1 via the input / output I / F 21 and supplies them to the estimation control device 20. The estimation control device 20 stores the supplied sensor information in the storage device 22 and acquires the weight information of the living body sitting on the chair 3a by referring to the weight table based on the personal information. Then, the load state information generation unit 27 generates load state information based on the sensor information. Moreover, the estimation control apparatus 20 performs the seat surface height estimation process based on the load state information and the personal information in the seat surface height estimation unit 49. Then, the estimation control device 20 generates and outputs estimation information indicating the estimation result, and stores it in the storage device 22.

  The estimation information output from the estimation control device 20 is output from the biological state estimation device 2 via the input / output I / F 21. The estimation information output from the biological state estimation device 2 is supplied to the biological information collection device 1 via the network 4.

  The biological information collection device 1 receives the estimation information supplied from the biological state estimation device 2 via the input / output I / F 11 and supplies it to the collection control device 10. The collection control device 10 generates a seating surface control signal based on information indicating the height of the seating surface 3b included in the estimation information, and outputs it to the chair 3a. Thereby, the height of the seat surface 3b of the chair 3a is set to the height indicated by the seat surface control signal, and the height of the seat surface 3b at this time is the optimum height for the living body seated on the chair 3a.

(Seat height adjustment process)
Next, the seat surface height estimation process according to Embodiment 14 will be described. As described in the fifth embodiment, the height of the seat surface 3b of the chair 3a is normally set to a predetermined reference height such as 42 cm according to the standard. When a living body is seated on such a chair 3a, in order for the living body to sit properly, the sole of the living body is in proper contact with the floor surface while the living body is seated on the chair 3a. There is a need. For example, when the foot is completely lifted, the body posture is not stable. For example, in a state where the sole of the foot is in full contact with the floor surface with the knee bent at an acute angle, the user sits on the chair 3a with a burden on the body.

  Thus, in the state where the living body is seated on the chair 3a, the load on the seating surface 3b of the chair 3a varies depending on the relationship between the feet of the living body and the floor surface. For example, when the living body is seated on the chair 3a with the living body's feet floating, the load applied to the seating surface 3b is equivalent to the body weight of the living body. On the other hand, for example, when the living body is seated on the chair 3a with the living body's feet in contact with the floor surface, the load applied to the sitting surface 3b is smaller than the body weight of the living body.

  Therefore, in the fourteenth embodiment, the sitting state of the living body is estimated based on the weight of the living body and the load applied to the seating surface 3b, and the height of the appropriate seating surface 3b in the living body is estimated and adjusted. The seat height adjustment process is performed.

  In this example, the seat surface height estimation unit 49 acquires the load applied to the seat surface 3 b based on the load state information generated by the load state information generation unit 27. Moreover, the seat surface height estimation part 49 acquires the weight of the living body seated on the chair 3a with reference to a weight table based on personal information. And the seat surface height estimation part 49 calculates the difference amount of the body weight and the acquired load of the seat surface 3b, and estimates the height of the appropriate seat surface 3b with respect to a living body according to a calculation result. The seat surface height estimation unit 49 outputs information indicating the estimated height of the seat surface 3b as estimation information and stores the information in the storage device 22.

  For example, when the difference between the calculated weight of the living body and the acquired load on the seating surface 3b is “0”, the seating surface height estimating unit 49 determines that the foot of the living body is floating, and the chair 3a. Information indicating that the height of the seating surface 3b is lower than the current height is output as estimation information. Further, when the difference amount is smaller than the weight of the living body, for example, when the difference is smaller than half of the body weight, the seat surface height estimating unit 49 determines that the sole of the living body is completely in contact with the floor surface. It judges and outputs the information which shows making the height of the seat surface 3b of the chair 3a higher than the present height as estimation information.

  Next, the collection control device 10 of the biological information collection device 1 adjusts the height of the seat surface 3b of the chair 3a based on the estimation information. Thereby, the height of the seat surface 3b of the chair 3a is adjusted to an appropriate height for the living body.

  As described above, in the living body state estimation system 100 according to the fourteenth embodiment, the furniture 3 has the seat surface 3b on which the living body is seated and the personal information acquisition device 51 that acquires the personal information of the living body seated on the seat surface 3b. And the biological information collecting apparatus 1 is configured so that the seat surface height determining unit 17 that determines the height of the seat surface 3b based on the estimation information and the seat surface height at which the seat surface 3b is determined. And a living body state estimation unit 40 that estimates the height of the seat surface 3b of the furniture 3 based on the load state information and the personal information. The storage device 22 stores a weight table in which the personal information of the living body and the weight of the living body corresponding to the personal information are associated with each other, and the seat surface height estimating section 49 stores the weight information in the load state information. Based on the load applied to the seating surface 3b. By referring to the weight table based on the information, the weight of the living body seated on the seating surface 3b is acquired, the difference amount between the weight of the acquired living body and the load applied to the seating surface 3b is calculated, and the calculated difference amount is Based on the result estimated by the seat surface height estimation unit 49, the biological information collection device 1 sets the height of the seat surface 3b. Thereby, the height of the seat surface 3b of the chair 3a as the furniture 3 on which the living body is seated can be appropriately adjusted without being conscious of the living body.

  In this example, information for identifying an individual of a living body is acquired as personal information, and the weight of the living body is acquired using a weight table. However, the present invention is not limited thereto. You may get your weight directly.

Embodiment 15 FIG.
Next, a biological state estimation system according to Embodiment 15 of the present invention will be described. The fifteenth embodiment is different from the first to fourteenth embodiments in that a piezoelectric sensor or an acceleration sensor is used as means for detecting biological information. In the following description, the same reference numerals are given to portions common to the above-described first to fourteenth embodiments, and detailed description thereof is omitted.

(When using a piezoelectric sensor)
First, the case where a piezoelectric sensor is used as a biometric information detection means will be described. A piezoelectric sensor detects the pressure with respect to the furniture 3 by a biological body as a physical quantity. At this time, the piezoelectric sensor detects biological information having the same tendency as when the load sensor 31 is used. Therefore, the biological state estimation system 100 using a piezoelectric sensor can estimate various biological states such as muscle strength by the same method as that when the load sensor 31 is used.

That is, when the piezoelectric sensor is used, the living body state estimation system 100 estimates the muscle strength when the living body is seated on the chair 3a by the same method as that described in the second embodiment. Specifically, the biological state estimation system 100 estimates the muscular strength of the biological body with any one of the following (h) to (k) as a feature.
(H) Order of pressure for each piezoelectric sensor attached to each leg 3c of chair 3a as furniture 3 (i) Maximum pressure magnitude for each piezoelectric sensor (j) Time of seating operation (k) Pressure peak Relative magnitude of front and rear pressure in time

  In addition, the biological state estimation system 100 is not limited to this, and as described in Embodiment 3, the living body state estimation system 100 has one pressure sensor attached to the back of the chair 3a and is based on the pressure peak value detected by the pressure sensor. The muscle strength of the living body may be estimated.

Furthermore, the living body state estimation system 100 estimates the muscle strength when the living body stands up from the chair 3a by the same method as that described in the fourth embodiment. Specifically, the living body state estimation system 100 estimates the muscle strength of the living body with any one of the following (l) to (n) as features.
(L) Order of pressure for each piezoelectric sensor (m) Number of local maximum points during standing operation (n) Standing operation time

(When using an acceleration sensor)
Next, a case where an acceleration sensor is used as the biological information detection means will be described. When the acceleration sensor is attached to the chair 3a as the furniture 3, the acceleration sensor detects acceleration in the three-axis directions of the vertical direction, the front-rear direction, and the left-right direction on the seat surface of the chair 3a as a physical quantity. In addition, what is necessary is just to attach one acceleration sensor with respect to the chair 3a when using an acceleration sensor.

  FIG. 36 is a schematic diagram illustrating a first example for describing sensor information when an acceleration sensor is used. In the example shown in FIG. 36, as shown in the second embodiment, the state of the load on the load sensors 31 </ b> A to 31 </ b> D and the acceleration in the vertical direction with respect to the acceleration sensor when a living body with sufficient muscle strength sits on the chair 3 a. It is a comparison. In FIG. 36, the horizontal axis indicates time. In addition, the vertical axis indicates acceleration, an increase in acceleration indicates an upward displacement, and a decrease in acceleration indicates a downward displacement.

  As described in the second embodiment, when a living body with sufficient muscular strength sits on the chair 3a, the load behind the seat surface 3b increases after the load ahead of the seat surface 3b increases. When the living body is completely seated, a constant value is obtained after the load before and after the seating surface 3b is reduced. At this time, as shown in FIG. 36, since the seating surface 3b is lowered at the time of sitting, the acceleration detected by the acceleration sensor is generated in the downward direction. Then, when the forward load increases, the acceleration occurs in the upward direction, and when the backward load increases, the acceleration occurs in the downward direction.

  FIG. 37 is a schematic diagram illustrating a second example for describing sensor information when an acceleration sensor is used. As shown in the fourth embodiment, FIG. 37 (a) shows the state of the load applied to the load sensors 31A to 31D when a living body with sufficient muscle strength stands from the chair 3a and the acceleration in the vertical direction relative to the acceleration sensor. It is a comparison. In FIG. 37 (a), the horizontal axis represents time. In addition, the vertical axis indicates acceleration, an increase in acceleration indicates an upward displacement, and a decrease in acceleration indicates a downward displacement.

  FIG. 37B shows the state of acceleration in the front-rear direction detected by the acceleration sensor in the state of FIG. In FIG. 37 (b), the horizontal axis indicates time. In addition, the vertical axis represents acceleration, an increase in acceleration indicates a forward displacement, and a decrease in acceleration indicates a backward displacement.

  As described in the fourth embodiment, when a living body with sufficient muscular strength stands up from the chair 3a, the load behind the seat surface 3b decreases and simultaneously the front load increases before the front of the seat surface 3b. The load of is reduced. When the living body stands up completely, the load before and after the seating surface 3b becomes a constant value. At this time, as shown in FIG. 37 (a), the vertical acceleration detected by the acceleration sensor increases when the rear load decreases and the front load increases, and the living body leaves the chair 3a. It becomes zero (1G) at the time. In addition, as shown in FIG. 37B, the acceleration in the front-rear direction increases when the rear load decreases and at the same time the front load increases, and decreases due to the reaction. That is, at the time of standing, the center of gravity of the living body is once displaced forward, and then completely lifted to eliminate the load on the chair 3a.

  Thus, the state of the load with respect to the load sensors 31A to 31D and the state of the acceleration with respect to the acceleration sensor correspond to each other. Therefore, an acceleration sensor can be applied instead of the load sensors 31A to 31D. Further, in the case of using the acceleration sensor, the same information as when the load on the seat surface 3b of the chair 3a is detected using the load sensors 31A to 31D by detecting the acceleration in the vertical direction among the three axis directions. Can be acquired. Therefore, the living body state estimation system 100 can estimate the muscle strength of the living body as in the second and fourth embodiments.

  When the acceleration sensor is used, the living body state estimation system 100 can estimate not only the muscle force but also the center of gravity of the living body. The center of gravity of the living body at the time of sitting can be estimated by acceleration in the left-right direction and the front-rear direction.

  FIG. 38 is a schematic diagram for explaining estimation of the center of gravity of the living body in the first example of FIG. Fig.38 (a) shows the mode of the load with respect to load sensor 31A-31D at the time of the biological body with sufficient muscular strength sitting on the chair 3a. In FIG. 38A, the horizontal axis indicates time, and the vertical axis indicates the magnitude of the load detected by each of the load sensors 31A to 31D. FIG. 38B shows a state of acceleration in the left-right direction detected by the acceleration sensor. In FIG. 38B, the horizontal axis indicates time. In addition, the vertical axis represents acceleration, an increase in acceleration indicates a displacement in the left direction, and a decrease in acceleration indicates a displacement in the right direction. FIG. 38C shows the state of acceleration in the front-rear direction detected by the acceleration sensor. In FIG.38 (c), a horizontal axis shows time. In addition, the vertical axis represents acceleration, an increase in acceleration indicates a forward displacement, and a decrease in acceleration indicates a backward displacement.

  When estimating the center of gravity of the living body by the load sensors 31A to 31D, as shown in FIG. 38A, the magnitude of the load at the time of sitting is “right rear <left rear” and “right front <left front”. It is. Therefore, when the living body is seated on the chair 3a, it is estimated that the center of gravity is displaced leftward from the center. Further, the magnitude of the load in a stable state after sitting is “left front> left rear”. Therefore, it is estimated that the center of gravity of the living body at the time of stability is located forward.

  On the other hand, when the center of gravity of the living body is estimated by the acceleration sensor, as shown in FIG. 38 (b), the lateral acceleration at the time of sitting once increases and then increases, and a peak value exists in the positive direction. Therefore, when the living body is seated on the chair 3a, it is estimated that the center of gravity is displaced leftward. Further, as shown in FIG. 38 (c), the longitudinal acceleration increases after once decreasing, and has a peak value in the positive direction. Therefore, it is estimated that the center of gravity of the living body at the time of stability is located forward.

  As described above, the living body state estimation system 100 can estimate the center of gravity of the living body at the time of sitting by using the acceleration sensor as in the case of using the load sensors 31A to 31D.

When the acceleration sensor is used instead of the load sensors 31A to 31D in this way, the living body state estimation system 100 uses the same method as that described in the second embodiment to increase the muscle strength when the living body is seated on the chair 3a. Is estimated. Specifically, the living body state estimation system 100 estimates the muscle strength of the living body with any one of the following (o) to (r) as features.
(O) Order of increase / decrease in vertical acceleration by the acceleration sensor (p) Maximum acceleration magnitude in the vertical direction by the acceleration sensor (q) Time of seating operation (r) Relative front-rear acceleration at acceleration peak size

In addition, the living body state estimation system 100 estimates the muscle strength when the living body stands up from the chair 3a by the same method as described in the fourth embodiment. Specifically, the living body state estimation system 100 estimates the muscle strength of the living body with any one of the following (s) to (u) as features.
(S) Order of increase / decrease in acceleration in the vertical direction by the acceleration sensor (t) Number of local maximum points during standing motion (u) Time for standing motion

  Here, the muscle strength estimation method at the time of sitting or standing of the living body has been described as an example. However, the present invention is not limited to this, and the living body state estimation system 100 described in Embodiments 5 to 14 also includes a piezoelectric sensor or an acceleration sensor. Can be used.

  For example, a piezoelectric sensor or an acceleration sensor can also be used when estimating the tremor of the living body described in the twelfth embodiment. In this case, the biological state estimation system 100 estimates continuous and periodic amplitude fluctuations having a specific frequency specific to tremor from the change in acceleration detected by the acceleration sensor, and estimates biological tremor. To do.

  As mentioned above, although Embodiment 1-15 was demonstrated, this invention is not limited to Embodiment 1-15 mentioned above, Various deformation | transformation and application are within the range which does not deviate from the summary of this invention. Is possible. For example, the configuration according to the first to fifteenth embodiments is not limited to the case where each process is performed independently, and the configurations according to the respective embodiments may be combined so that these processes can be performed simultaneously. Good.

  DESCRIPTION OF SYMBOLS 1 Living body information collection apparatus, 2 Living body state estimation apparatus, 3 Furniture, 3a Chair, 3b Seat surface, 3c Leg part, 3e Bed, 3f Bedding surface, 3g Leg part, 3h Western style toilet, 3i Toilet seat, 4 Network, 10 Collection control Device, 11 input / output interface, 12 display device, 13 input device, 15 input processing unit, 16 output processing unit, 17 seating surface height determining unit, 18 seating surface drive control unit, 19 storage unit, 20 estimation control device, 21 Input / output interface, 22 storage device, 25 input processing unit, 26 output processing unit, 27 load state information generation unit, 30 sensor unit, 31, 31A, 31B, 31C, 31D, 31E load sensor, 32 plate member, 33a, 33b color, 40 biological state estimation unit, 41 muscle strength estimation unit, 42 muscle strength change estimation unit, 43 balance estimation unit, 44 dozing Estimating unit, 45 Positions estimator, 46 back pain estimator, 47 tremor estimation unit, 48 defecation estimator, 49 Seat height estimating section, 51 the personal information acquisition unit, 100 biological condition estimation system.

The present invention relates to a biological state estimation system you good beauty student body state estimation method for estimating the state of the living body.

Therefore, the present invention has been made in view of the above-described problems in the prior art, and estimates the state of a living body without attaching a special instrument or the like to the living body and without being conscious of the living body. an object of the present invention is to provide a biological state estimation system you good beauty student body state estimation method that can be.

The living body state estimation system according to the present invention detects a physical quantity generated by contact of a living body, and furnishes to which a state detection unit that outputs a detection result as sensor information is attached, and the sensor information output from the state detection unit. A living body information collecting device that collects and a living body state estimating device that estimates the state of the living body in contact with the furniture based on the sensor information collected by the living body information collecting device; A storage device that stores the sensor information; and an estimation control device that estimates the state of the living body based on the sensor information. The estimation control device indicates the state of the physical quantity with respect to time based on the sensor information. A state information generation unit that generates state information to be displayed and stores the generated state information in the storage device; and the state information Extracting a feature relating to the physical quantity, estimating a state of the living body contacting the furniture based on the feature, and a biological state estimating unit storing the estimated information indicating the estimated state of the living body in the storage device; Yes, and the biological status estimating unit, based on the state information, the a muscle force estimation unit that estimates a muscle force of the living body to be seated on furniture, the muscle force estimation unit, an operation related to seating of the organism to the furniture The muscular strength of the living body is estimated based on the characteristics shown or the characteristics showing the operation related to the standing of the living body .

Claims (27)

Furniture with attached state detection means for detecting physical quantities generated by contact with a living body and outputting detection results as sensor information;
A biological information collection device that collects the sensor information output from the state detection means;
Based on the sensor information collected by the biological information collection device, the biological state estimation device that estimates the state of the biological body that contacts the furniture,
The biological state estimating device
A storage device for storing the sensor information;
An estimation control device that estimates the state of the living body based on the sensor information;
The estimation control device includes:
Based on the sensor information, a state information generation unit that generates state information indicating the state of the physical quantity with respect to time, and stores the generated state information in the storage device;
A feature relating to the physical quantity is extracted from the state information, the state of the living body contacting the furniture is estimated based on the feature, and estimated information indicating the estimated state of the living body is stored in the storage device A biological state estimation system. The biological state estimation unit is a muscle strength estimation unit that estimates muscle strength of the living body seated on the furniture based on the state information,
The muscle strength estimation unit
The living body state estimation system according to claim 1, wherein the muscle strength of the living body is estimated based on a feature indicating an operation related to sitting of the living body on the furniture or a feature indicating an operation related to standing of the living body. The state detecting means is plural,
The feature indicating the operation related to the seating is:
The physical quantity detected by a plurality of the state detection means is an order of change,
The muscle strength estimation unit
Among the plurality of state detection means, when the maximum value of the physical quantity is detected by the state detection means arranged in front of the furniture, and the maximum value of the physical quantity is detected by the state detection means arranged behind the furniture. The biological state estimation system according to claim 2, wherein the muscular strength of the biological body is estimated based on a comparison result between a difference time from the detected time point and a set time. The feature indicating the operation related to the seating is:
The size of the maximum value of the physical quantity detected by the state detection means,
The muscle strength estimation unit
The biological state estimation system according to claim 2 or 3, wherein the muscular strength of the biological body is estimated based on a comparison result between the maximum value detected by the state detection means and a set threshold value. The feature indicating the operation related to the seating is:
It is a seating operation time indicating a time from when the living body starts to sit on the furniture to sit down,
The muscle strength estimation unit
The living body state estimation system according to any one of claims 2 to 4, wherein the muscle strength of the living body is estimated based on a comparison result between the sitting operation time and a set time. The state detecting means is plural,
The feature indicating the operation related to the seating is:
A relative magnitude of the front and rear physical quantities when a peak value of the physical quantity is detected by a plurality of the state detection means;
The muscle strength estimation unit
Among the plurality of state detection means, a comparison result between the physical quantity detected by the state detection means arranged in front of the furniture and the physical quantity detected by the state detection means arranged behind the furniture. The biological state estimation system according to any one of claims 2 to 5, wherein the muscular strength of the biological body is estimated based on. The state detecting means is plural,
The feature indicating the operation related to the standing is as follows:
The physical quantity detected by a plurality of the state detection means is an order of change,
The muscle strength estimation unit
Among the plurality of state detection means, when the minimum value of the physical quantity is detected by the state detection means arranged behind the furniture, and when the minimum value of the physical quantity is detected by the state detection means arranged in front of the furniture, The biological state estimation system according to claim 2, wherein the muscular strength of the biological body is estimated based on a comparison result between a difference time from the detected time point and a set time. The feature indicating the operation related to the standing is as follows:
It is the number of local maximum points during the standing operation time indicating the time from when the living body starts standing up from the furniture to standing up,
The muscle strength estimation unit
The biological state estimation system according to claim 2 or 7, wherein the muscular strength of the living body is estimated based on a comparison result between the number of maximum points during the standing operation time and a set threshold value. The feature indicating the operation related to the standing is as follows:
It is a standing operation time indicating a time from when the living body starts to stand up from the furniture to stand up,
The muscle strength estimation unit
The biological state estimation system according to claim 2, 7 or 8, wherein muscle strength of the living body is estimated based on a comparison result between the standing operation time and a set time. The furniture is provided with a seating surface on which the living body is seated,
The biological information collection device includes:
A storage unit storing a seating surface height table in which the muscle strength of the living body and the height of the seating surface are associated with each other;
Based on the estimation information, a seat surface height determining unit that determines the height of the seat surface with reference to the seat surface height table;
The living body state estimation system according to any one of claims 2 to 6, further comprising a seating surface drive control unit that controls the height of the seating surface so that the seating surface becomes the determined height of the seating surface. . The seat height determining unit
When the estimation information indicates that the muscular strength of the living body is sufficient, the seat surface of the furniture is lower than the height when the living body is seated,
The biological state estimation system according to claim 10, wherein when the estimation information indicates that the muscular strength of the living body is weak, the seating surface of the furniture is made higher than a height when the living body is seated. The biological state estimation unit is a muscle force change estimation unit that estimates a change in muscle strength in the living body based on the state information,
The muscle strength change estimation unit
The change in muscle strength in the living body is estimated based on past state information stored in the storage device and current state information generated by the state information generation unit based on the sensor information. Biological state estimation system. The biological state estimation unit is a balance estimation unit that estimates the balance of the biological body based on the state information,
The balance estimation unit
Based on the time from when the physical quantity detected by the state detection means included in the state information becomes maximum until the physical quantity becomes stable, and the magnitude of the physical quantity when the physical quantity becomes stable, the balance of the living body is determined. The biological state estimation system according to claim 1 to estimate. The biological state estimation unit is a dozing estimation unit that estimates the dozing of the living body based on the state information,
The dozing estimation unit
The biological state estimation system according to claim 1, wherein the biological state estimating system estimates the dozing of the biological body based on a size of the physical quantity when the physical quantity detected by the state detection unit included in the state information is stabilized. The living body state estimating unit is a body position estimating unit that estimates the body position of the living body based on the state information,
The body position estimation unit
The living body state estimation system according to claim 1, wherein the body position of the living body is estimated based on a change in the physical quantity given to the state detection unit. The state detecting means is plural,
The furniture is a Western-style toilet having a toilet seat on which the living body is seated,
Each of the plurality of state detection means is attached to the toilet seat,
The living body state estimating system according to claim 15, wherein the body position estimating unit estimates the body position of the living body seated on the Western-style toilet based on the state information. The biological state estimation unit is a low back pain estimation unit that estimates whether the biological body may cause low back pain based on the state information,
The storage device stores low back pain posture information indicating a state of the physical quantity in a living body having low back pain,
The low back pain estimation unit
The biological state estimation system according to claim 1, wherein the biological state estimation system estimates whether or not the biological body is likely to cause back pain based on the state information and the back pain posture information. The biological state estimation unit is a tremor estimation unit that estimates tremor of the biological body based on the state information,
The biological state estimation system according to claim 1, wherein when the state information includes a continuous and periodic amplitude variation having a specific frequency, the state information is determined to indicate tremor. The storage device stores a drug taking time indicating a time when the living body took a drug for suppressing the tremor,
The tremor estimation unit
The biological state estimation system according to claim 18, wherein an effect time of the drug taken by the living body is estimated based on the drug taking time and a time at which a tremor estimated based on the state information appears. The biological state estimation unit is a defecation estimation unit that estimates the defecation of the biological body based on the state information,
The defecation estimation unit
Based on the status information, detect continuous vibration due to unique movements during defecation,
The biological state estimation system according to claim 1, wherein the defecation is estimated based on the detected continuous vibration. The furniture is
A seating surface on which the living body is seated;
A personal information acquisition device for acquiring personal information of the living body seated on the seating surface;
The biological information collection device includes:
Based on the estimation information, a seat surface height determining unit that determines the height of the seat surface;
A seating surface drive control unit that controls the height of the seating surface so that the seating surface becomes the determined height of the seating surface,
The biological state estimating unit
Based on the state information and the personal information, a seat surface height estimation unit that estimates the height of the seat surface of the furniture,
The storage device
Storing a weight table in which the personal information of the living body and the weight of the living body corresponding to the personal information are associated with each other;
The seat surface height estimation unit is
Based on the state information, obtain the physical quantity given to the seating surface,
Referencing the weight table based on the personal information, obtaining the weight of the living body seated on the seat surface,
Calculating a difference amount between the acquired body weight of the living body and the physical quantity given to the seat surface;
Based on the calculated difference amount, the height of the seating surface is estimated,
The biological information collection device includes:
The living body state estimation system according to claim 1, wherein the height of the seating surface is set based on a result estimated by the seating surface height estimation unit. The state detection means is a load sensor that detects a load,
The biological state estimation system according to any one of claims 1 to 21, wherein the physical quantity is a load applied to the furniture. The state detection means is an acceleration sensor that detects acceleration in the vertical direction, the left-right direction, and the front-rear direction,
The biological state estimation system according to any one of claims 1 to 21, wherein the physical quantity is an acceleration applied to the furniture. The state detection means is a piezoelectric sensor that detects pressure,
The biological state estimation system according to any one of claims 1 to 21, wherein the physical quantity is a pressure applied to the furniture. A sensor unit that is attached to furniture in contact with a living body,
State detection means for detecting a physical quantity generated by contact of the living body and outputting the detection result as sensor information;
Provided in the state detection means, comprising a connection member connected to at least one of the main body and legs of the furniture,
The sensor unit attached between the said main body and the said leg part of the said furniture, or the bottom face side of the said leg part via the said connection member. Furniture that comes in contact with living organisms,
A main body with which the living body comes into contact;
A plurality of legs that support the body;
A furniture comprising the sensor unit according to claim 25. Furniture with attached state detection means for detecting physical quantities generated by contact with a living body and outputting detection results as sensor information;
A biological information collection device that collects the sensor information output from the state detection means;
Based on the sensor information collected by the biological information collection device, the biological state estimation device that estimates the state of the biological body that contacts the furniture,
The biological state estimating device
A storage device for storing the sensor information;
A biological state estimation method for a biological state estimation system comprising: an estimation control device that estimates the state of the biological body based on the sensor information,
Generating state information indicating the state of the physical quantity with respect to time based on the sensor information;
Extracting features relating to the physical quantity from the state information;
Estimating the state of the living body in contact with the furniture based on the feature.