MEASUREMENT APPARATUS, MEASUREMENT SYSTEM, MEASUREMENT METHOD, AND PROGRAM

Abstract

Provided is a measurement device comprising: a detection unit which detects a walking event from time series data of sensor data pertaining to the movements of feet in order to measure lower limbs on the basis of the sensor data acquired by a single sensor; and a measurement unit which measures the lower limbs by using the sensor data for a prescribed period starting from a walking event timing on the basis of a geometric model to which a constraint condition pertaining to the movements of the lower limbs is given.

Description

[Technical field]

[0001] The present disclosure relates to a measurement device or the like that performs measurements related to the lower limbs. [Background technology]

[0002] Due to the growing interest in health care for physical condition management, attention is being paid to services that measure gait, including walking characteristics, and provide users with information according to that gait. If information on the lower limbs could be obtained based on walking data, more advanced gait analysis would be possible.

[0003] Non-Patent Document 1 discloses a public dataset on the dynamics of walking motion of healthy individuals. In Non-Patent Document 1, the dynamics of walking motion is verified based on the trajectories of markers attached to the legs, pelvis, etc.

[0004] Patent Document 1 discloses a gait analysis system that analyzes the walking state of a walker based on measurement data measured by sensors attached to the legs, waist, etc. The system of Patent Document 1 obtains the joint angles of the hip joint, knee joint, or ankle joint of the walker using measurement data from sensors attached to positions sandwiching the hip joint, knee joint, or ankle joint. The system of Patent Document 1 obtains the stride length of the walker from measurement data from a measurement sensor attached to the back of the foot. The system of Patent Document 1 evaluates the walking state of the walker by comparing the correlation coefficient between the feature points of the joint angle and the stride length with the correlation coefficient between the feature points of the joint angle of the hip joint, knee joint, or ankle joint and the stride length of a previously obtained healthy person when walking.

[0005] Patent Document 2 discloses an exercise information display system that displays exercise information. The system in Patent Document 2 generates a video that reproduces the movement state of the legs of an exercising person based on the values ​​of acceleration and angular velocity measured by a sensor attached to one of the legs. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Patent No. 5586050 [Patent Document 2] JP 2016-112108 A [Non-patent literature]

[0007] [Non-Patent Document 1] Fukuchi et al., “A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals”, (2018), PeerJ, DOI 10.7717 / peerj.4640, pp. 1-17. Summary of the Invention [Problem to be solved by the invention]

[0008] In the methods of Non-Patent Document 1 and Patent Document 1, the walking state of a walker is analyzed based on measurement data measured by sensors attached to multiple locations on the legs. That is, in the methods of Non-Patent Document 1 and Patent Document 1, when analyzing the walking of a walker, it is necessary to attach sensors to multiple locations on the legs.

[0009] In the method of Patent Document 2, the length of the lower leg is calculated based on the movement during calibration. In the method of Patent Document 2, the position of the knee and the position of the base of the thigh while running are estimated based on the length of the lower leg calculated during calibration. In other words, in the method of Patent Document 2, unless the length of the lower leg is measured in advance, the movement of the knee and the base of the thigh cannot be verified.

[0010] An object of the present disclosure is to provide a measurement device etc. that can perform measurements related to the lower limbs based on sensor data acquired by a single sensor. [Means for solving the problem]

[0011] A measurement device according to one aspect of the present disclosure includes a detection unit that detects walking events from time-series data of sensor data relating to foot movement, and a measurement unit that performs measurements relating to the lower limbs based on a geometric model to which constraints on the movement of the lower limbs are imposed, using sensor data for a predetermined period starting from the timing of the walking event.

[0012] In a measurement method according to one aspect of the present disclosure, a computer detects walking events from time-series data of sensor data relating to foot movement, and performs measurements of the lower limbs based on a geometric model to which constraints on the movement of the lower limbs are imposed, using the sensor data for a predetermined period starting from the timing of the walking event.

[0013] A program according to one embodiment of the present disclosure causes a computer to perform a process of detecting walking events from time-series data of sensor data relating to foot movement, and a process of taking measurements relating to the lower limbs based on a geometric model to which constraints on the movement of the lower limbs are imposed, using the sensor data for a predetermined period starting from the timing of the walking event. Effect of the Invention

[0014] According to the present disclosure, it is possible to provide a measurement device etc. that can perform measurements related to the lower limbs based on sensor data acquired by a single sensor. [Brief description of the drawings]

[0015] [Figure 1] 1 is a block diagram showing an example of the configuration of a measurement system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of arrangement of data acquisition devices in a measurement system according to a first embodiment. [Diagram 3] 2 is a conceptual diagram for explaining a coordinate system set in a data acquisition device of the measurement system according to the first embodiment. FIG. [Figure 4] 2 is a conceptual diagram for explaining an example of a human body surface used in explaining the measurement system according to the first embodiment. FIG. [Diagram 5] FIG. 2 is a conceptual diagram for explaining an example of a walking cycle used in explaining the measurement system according to the first embodiment. [Figure 6] 2 is a block diagram showing an example of the configuration of a data acquisition device of the measurement system according to the first embodiment. FIG. [Figure 7] 1 is a block diagram showing an example of the configuration of a measurement device of a measurement system according to a first embodiment. [Figure 8] FIG. 11 is a conceptual diagram for explaining another example of a walking cycle used in explaining the measurement system according to the first embodiment. [Figure 9] 4 is a conceptual diagram for explaining a method for measuring the distance between a data acquisition device and a heel in a sagittal plane by a measurement device of the measurement system according to the first embodiment. FIG. [Figure 10] FIG. 2 is a conceptual diagram for explaining an example of measurement of a trajectory of a knee during a predetermined period from foot crossing by a measurement device of the measurement system according to the first embodiment. [Figure 11] 10 is a conceptual diagram for explaining a third constraint condition imposed on the measurement of the lower limbs by the measurement device of the measurement system according to the first embodiment. FIG. [Figure 12] FIG. 13 is a conceptual diagram for explaining a sixth constraint condition imposed on the measurement of the lower limbs by the measurement device of the measurement system according to the first embodiment. [Figure 13] 1 is a conceptual diagram for explaining an example of measurement of a trajectory of a knee during a period from tibia vertical to heel contact by a measurement device of a measurement system according to a first embodiment. FIG. [Figure 14] FIG. 2 is a conceptual diagram for explaining an example of measurement of the length of a lower limb by a measurement device of the measurement system according to the first embodiment. [Figure 15] 5 is a flowchart for explaining an example of an operation of the measurement apparatus of the measurement system according to the first embodiment. [Figure 16] 5 is a flowchart for explaining an example of a first measurement process by the measurement apparatus of the measurement system according to the first embodiment. [Figure 17]10 is a flowchart for explaining an example of a second measurement process by the measurement apparatus of the measurement system according to the first embodiment. [Figure 18] FIG. 11 is a block diagram showing an example of the configuration of a learning system according to a second embodiment. [Figure 19] FIG. 11 is a conceptual diagram for explaining an example of learning by a learning device of a learning system according to a second embodiment. [Figure 20] FIG. 13 is a block diagram showing an example of the configuration of a measurement system according to a third embodiment. [Figure 21] FIG. 13 is a block diagram showing an example of the configuration of a measurement device of a measurement system according to a third embodiment. [Figure 22] FIG. 13 is a conceptual diagram for explaining an example of estimation of a body state by a measurement device of a measurement system according to a third embodiment. [Figure 23] FIG. 13 is a conceptual diagram for explaining another example of estimation of a body state by a measurement device of the measurement system according to the third embodiment. [Figure 24] FIG. 13 is a conceptual diagram for explaining Application Example 1 according to the third embodiment. [Diagram 25] FIG. 13 is a conceptual diagram for explaining Application Example 2 according to the third embodiment. [Figure 26] FIG. 13 is a conceptual diagram for explaining an example of the configuration of a measurement apparatus according to a fourth embodiment. [Figure 27] FIG. 2 is a conceptual diagram illustrating an example of a hardware configuration for implementing control and processing according to each embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Hereinafter, the embodiments for carrying out the present invention will be described with reference to the drawings. However, the embodiments described below are limited in a manner that is technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following. In addition, in all the drawings used to explain the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. In addition, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0017] (First embodiment) First, a measurement system according to a first embodiment will be described with reference to the drawings. The measurement system of this embodiment measures sensor data related to physical quantities related to foot movement by a sensor installed in footwear worn by a user. For example, the physical quantities related to foot movement include acceleration in three axial directions (also called spatial acceleration) measured by an acceleration sensor, and angular velocity around three axes (also called spatial angular velocity) measured by an angular velocity sensor. The measurement system of this embodiment performs measurements related to the lower limbs based on time series data (also called walking waveform) of the measured sensor data.

[0018] (composition) Fig. 1 is a block diagram showing an example of the configuration of a measurement system 10 of this embodiment. The measurement system 10 includes a data acquisition device 11 and a measurement device 15. The data acquisition device 11 and the measurement device 15 may be connected by wire or wirelessly. The data acquisition device 11 and the measurement device 15 may be configured as a single device. Although only one data acquisition device 11 is shown in Fig. 1, one data acquisition device 11 each on the left and right feet (two in total) may be provided.

[0019] The data acquisition device 11 is installed on at least one of the left and right feet. For example, the data acquisition device 11 is installed on footwear such as shoes. In this embodiment, an example in which the data acquisition device 11 is installed at the back side of the arch of the left and right feet will be described. The data acquisition device 11 includes an acceleration sensor and an angular velocity sensor. The data acquisition device 11 measures physical quantities related to the movement of the foot of a user wearing the footwear, such as acceleration in three axial directions (also called spatial acceleration) and angular velocity around three axes (also called spatial angular velocity). The physical quantities related to the movement of the foot measured by the data acquisition device 11 include not only acceleration and angular velocity, but also velocity and angle calculated by integrating the acceleration and angular velocity. In addition, the physical quantities related to the movement of the foot measured by the data acquisition device 11 also include a position (trajectory) calculated by integrating the acceleration twice. The data acquisition device 11 converts the measured physical quantities into digital data (also called sensor data). The data acquisition device 11 transmits the converted sensor data to the measurement device 15.

[0020] FIG. 2 is a conceptual diagram showing an example of installing the data acquisition device 11 in the shoe 100. In the example of FIG. 2, the data acquisition device 11 is installed at a position that hits the back side of the arch of the foot. For example, the data acquisition device 11 is installed in an insole inserted into the shoe 100. For example, the data acquisition device 11 is installed on the bottom surface of the shoe 100. For example, the data acquisition device 11 is embedded in the body of the shoe 100. The data acquisition device 11 may be detachable from the shoe 100, or may not be detachable from the shoe 100. The data acquisition device 11 may be installed at a position other than the back side of the arch of the foot, as long as it can acquire sensor data related to the movement of the foot. The data acquisition device 11 may also be installed in socks worn by the user or in an accessory such as an anklet worn by the user. The data acquisition device 11 may also be directly attached to the foot or embedded in the foot. FIG. 2 shows an example in which the data acquisition device 11 is installed in the shoes 100 of both feet. The data acquisition device 11 may be installed on at least one of the feet. If the data acquisition devices 11 are installed in the shoes 100 of both feet, evaluation can be performed based on the sensor data measured by the data acquisition devices 11 installed on the left and right feet.

[0021] 3 is a conceptual diagram for explaining a local coordinate system (x-axis, y-axis, z-axis) set in the data acquisition device 11 and a world coordinate system (X-axis, Y-axis, Z-axis) set with respect to the ground. In the world coordinate system (X-axis, Y-axis, Z-axis), when the user is standing upright, the horizontal direction of the user is set as the X-axis direction (rightward is positive), the direction in front of the user (moving direction) is set as the Y-axis direction (forward is positive), and the direction of gravity is set as the Z-axis direction (vertical upward is positive). In this embodiment, a local coordinate system consisting of the x-direction, y-direction, and z-direction with the data acquisition device 11 as a reference is set.

[0022] FIG. 4 is a conceptual diagram for explaining a plane (also called a human body plane) set for a human body. In this embodiment, a sagittal plane that divides the body into left and right, a coronal plane that divides the body into front and back, and a horizontal plane that divides the body horizontally are defined. In the upright state as shown in FIG. 4, the world coordinate system and the local coordinate system coincide. In this embodiment, a rotation in the sagittal plane about the x-axis as the rotation axis is defined as a roll, a rotation in the coronal plane about the y-axis as the rotation axis is defined as a pitch, and a rotation in the horizontal plane about the z-axis as the rotation axis is defined as a yaw. In addition, a rotation angle in the sagittal plane about the x-axis as the rotation axis is defined as a roll angle, a rotation angle in the coronal plane about the y-axis as the rotation axis is defined as a pitch angle, and a rotation angle in the horizontal plane about the z-axis as the rotation axis is defined as a yaw angle. In this embodiment, when the body is viewed from the right side, a clockwise rotation in the sagittal plane is defined as positive, and a counterclockwise rotation in the sagittal plane is defined as negative.

[0023] The data acquisition device 11 is realized by, for example, an inertial measurement device including an acceleration sensor and an angular velocity sensor. An example of the inertial measurement device is an IMU (Inertial Measurement Unit). The IMU includes a three-axis acceleration sensor and a three-axis angular velocity sensor. Also, examples of the inertial measurement device include a VG (Vertical Gyro) and an AHRS (Attitude Heading Resistance Sensor). Reference System An example of an inertial device is the Global Positioning System / Inertial Navigation System (GPS / INS).

[0024] For example, the data acquisition device 11 is connected to a measurement device 15 constructed in the cloud via a mobile terminal (not shown) carried by a user. The mobile terminal (not shown) is a portable communication device. For example, the mobile terminal is a mobile communication device having a communication function such as a smartphone, a smart watch, or a mobile phone. The mobile terminal receives sensor data related to the movement of the user's feet from the data acquisition device 11. The mobile terminal transmits the received sensor data to a server or the like in which the measurement device 15 is implemented. Note that the function of the measurement device 15 may be realized by an application installed in the mobile terminal. In that case, the mobile terminal processes the received sensor data by application software (also called an app) installed in the mobile terminal itself.

[0025] The measurement device 15 acquires sensor data from the data acquisition device 11. The measurement device 15 converts the coordinate system of the acquired sensor data from a local coordinate system to a world coordinate system. The coordinate system of the sensor data may be converted to the world coordinate system by the data acquisition device 11. The measurement device 15 generates time series data (also called a walking waveform) of the sensor data after conversion to the world coordinate system. The measurement device 15 detects walking events from the walking waveform. The measurement device 15 performs measurements related to the lower limbs based on the detected walking events using a geometric model to which constraints on the movement of the lower limbs are imposed. The geometric model is a model for geometrically capturing and verifying the positions and angles of parts of the lower limbs at multiple timings included in the measurement target period.

[0026] For example, the measurement device 15 measures the length of the portion between the knee joint and the ankle joint (also called the lower leg) and the length of the portion between the hip joint and the knee joint (also called the upper leg). For example, the measurement device 15 measures the positions of the knee joint and the hip joint. For example, the measurement device 15 measures the time change (trajectory) of the positions of the knee joint and the hip joint. For example, the measurement device 15 measures the knee joint angle.

[0027] A method for measuring the measurement values ​​related to the lower limbs by the measurement device 15 will be described later in detail. The measurement device 15 outputs information related to the lower limbs. For example, the measurement device 15 outputs the information related to the lower limbs to a display device (not shown) or an external system.

[0028] Here, a walking event detected from a walking waveform will be described with reference to the drawings. FIG. 5 is a conceptual diagram for explaining a step cycle based on the right foot. A step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 5 indicates a walking cycle normalized to 100% for one right foot step cycle starting from the time when the heel of the right foot lands on the ground and ending at the time when the heel of the right foot lands on the ground. One walking cycle of one foot is roughly divided into a stance phase in which at least a part of the sole of the foot is in contact with the ground, and a swing phase in which the sole of the foot is off the ground. The stance phase is further divided into an early stance phase T1, a mid stance phase T2, a final stance phase T3, and an early swing phase T4. The swing phase is further divided into an early swing phase T5, a mid swing phase T6, and a final swing phase T7. Note that FIG. 5 is an example, and does not limit the periods constituting a step cycle or the names of these periods.

[0029] As shown in Figure 5, multiple events (also called walking events) occur during walking. Figure 5 (a) shows the event of the heel of the right foot touching the ground (heel strike) (HS: Heel Strike). Figure 5 (b) shows the event of the toe of the left foot lifting off the ground (opposite toe off) while the sole of the right foot is on the ground (OTO: Opposite Toe Off). Figure 5 (c) shows the event of the heel of the right foot lifting off the ground (heel rise) while the sole of the right foot is on the ground (HR: Heel Rise). Figure 5 (d) shows the event of the heel of the left foot touching the ground (opposite heel strike) (OHS: Opposite Heel Strike). Figure 5 (e) shows the event of the toe of the right foot lifting off the ground (toe off) while the sole of the left foot is on the ground (TO: Toe Off). FIG. 5(f) shows an event where the left and right feet cross (foot crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). FIG. 5(g) shows an event where the tibia of the right foot is almost perpendicular to the ground with the sole of the left foot touching the ground (TV: Tibia Vertical). FIG. 5(h) shows an event where the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). FIG. 5(h) corresponds to the end point of the walking cycle that starts from FIG. 5(a) and corresponds to the starting point of the next walking cycle. Note that FIG. 5 is merely an example and does not limit the events that occur in walking or the names of those events.

[0030] [Data acquisition device] Next, details of the data acquisition device 11 will be described with reference to the drawings. Fig. 6 is a block diagram showing an example of a detailed configuration of the data acquisition device 11. The data acquisition device 11 has an acceleration sensor 111, an angular velocity sensor 112, a control unit 113, and a transmission unit 115. The data acquisition device 11 also includes a power source (not shown).

[0031] Acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). Acceleration sensor 111 outputs the measured acceleration to control unit 113. For example, a piezoelectric type, a piezoresistance type, a capacitance type, or other type of sensor can be used for acceleration sensor 111. Note that the measurement type of the sensor used for acceleration sensor 111 is not limited as long as it can measure acceleration.

[0032] Angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) around three axes. Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a vibration type, a capacitance type, or other type of sensor can be used for angular velocity sensor 112. Note that the measurement method of the sensor used for angular velocity sensor 112 is not limited as long as it can measure angular velocity.

[0033] The control unit 113 acquires actual values ​​of acceleration in three axial directions from the acceleration sensor 111. The control unit 113 acquires actual values ​​of angular velocity around an axis from the angular velocity sensor 112. The control unit 113 converts the acquired actual values ​​of acceleration and angular velocity into digital data (also called sensor data). The control unit 113 outputs the converted digital data to the transmission unit 115. The sensor data includes at least acceleration data (including acceleration vectors in three axial directions) and angular velocity data (including angular velocity vectors around three axes) converted into digital data. The sensor data includes acquisition times of actual values ​​that are the basis of the acceleration data and angular velocity data. The control unit 113 may be configured to output sensor data that is obtained by adding corrections such as mounting errors, temperature correction, and linearity correction to the acquired acceleration data and angular velocity data. The control unit 113 may convert the coordinate system of the sensor data from a local coordinate system to a world coordinate system. The control unit 113 may generate angle data around three axes (also called plantar angle) using the acquired acceleration data and angular velocity data.

[0034] For example, the control unit 113 is a microcomputer or a microcontroller that performs overall control of the data acquisition device 11 and data processing. For example, the control unit 113 has a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, etc. The control unit 113 controls the acceleration sensor 111 and the angular velocity sensor 112 to measure angular velocity and acceleration. For example, the control unit 113 performs AD conversion (Analog-to-Digital Conversion) of physical quantities (analog data) such as the measured angular velocity and acceleration, and stores the converted digital data in a flash memory. Note that the physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data in each of the acceleration sensor 111 and the angular velocity sensor 112. The digital data stored in the flash memory is output to the transmission unit 115 at a predetermined timing.

[0035] The transmitting unit 115 acquires sensor data from the control unit 113. The transmitting unit 115 transmits the acquired sensor data to the measuring device 15. For example, the transmitting unit 115 transmits the sensor data to the measuring device 15 via a wired connection such as a cable. For example, the transmitting unit 115 transmits the sensor data to the measuring device 15 via wireless communication. For example, the transmitting unit 115 is configured to transmit the sensor data to the measuring device 15 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the transmitting unit 115 may be conforming to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0036] [Measuring equipment] Next, details of the measurement device 15 will be described with reference to the drawings. Fig. 7 is a block diagram showing an example of a detailed configuration of the measurement device 15. The measurement device 15 has an acquisition unit 151, a generation unit 153, a detection unit 155, and a measurement unit 157.

[0037] The acquisition unit 151 receives sensor data from the data acquisition device 11. The acquisition unit 151 outputs the received sensor data to the generation unit 153. For example, the acquisition unit 151 receives the sensor data from the data acquisition device 11 via a wired connection such as a cable. For example, the acquisition unit 151 receives the sensor data from the data acquisition device 11 via wireless communication. For example, the acquisition unit 151 is configured to receive the sensor data from the data acquisition device 11 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the acquisition unit 151 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0038] The generation unit 153 acquires sensor data from the acquisition unit 151. The generation unit 153 converts the coordinate system of the acquired sensor data from a local coordinate system to a world coordinate system. The generation unit 153 generates time-series data (also called a walking waveform) of the sensor data after the conversion to the world coordinate system. The generation unit 153 outputs the generated walking waveform to the detection unit 155.

[0039] For example, the generating unit 153 generates a walking waveform such as a spatial acceleration or a spatial angular velocity. The generating unit 153 also integrates the spatial acceleration or the spatial angular velocity to generate a walking waveform such as a spatial velocity or a spatial angle (sole angle). The generating unit 153 also performs a second-order integration of the spatial acceleration to generate a walking waveform of a spatial trajectory. The generating unit 153 generates a walking waveform at a predetermined timing or time interval set in accordance with a general walking cycle or a walking cycle specific to a user. The timing at which the generating unit 153 generates the walking waveform can be set arbitrarily. For example, the generating unit 153 is configured to continue generating a walking waveform during a period during which the user continues walking. The generating unit 153 may also be configured to generate a walking waveform at a specific time.

[0040] The detection unit 155 acquires a walking waveform from the generation unit 153. The detection unit 155 detects walking events from the walking waveform. For example, the detection unit 155 detects walking events such as heel strike, toe off, foot crossing, and tibia vertical. The detection unit 155 outputs to the measurement unit 157 the timing of the detected walking event and the value of the sensor data for a predetermined period starting from the timing of the walking event.

[0041] Here, an example of detection of a walking event by the detection unit 155 will be described. In this embodiment, an example will be described in which the timing of the center of the stance phase (the start of stance end phase T3) is set as the starting point of a step cycle, and heel contact, toe off, foot crossing, and tibia vertical are detected as walking events. FIG. 8 is a conceptual diagram for explaining an example of a step cycle based on the right foot, which is set by the detection unit 155. The step cycle set by the detection unit 155 starts from the timing of the start of stance end phase T3. The timing of the start of stance end phase T3 corresponds to the timing of heel lift. In the following, the description will be made according to the order of detection of walking events, not the order of time series in the walking waveform of the step cycle.

[0042] First, the detection unit 155 extracts a gait waveform of a step cycle starting from the timing of the start of the stance end phase T3 from the gait waveform of the plantar angle. Here, the state where the toe is located above the heel (dorsiflexion) is defined as negative, and the state where the toe is located below the heel (plantar flexion) is defined as positive. The timing at which the gait waveform of the plantar angle becomes minimum corresponds to the timing at which the stance phase starts. The timing at which the gait waveform of the plantar angle becomes maximum corresponds to the timing at which the swing phase starts. The timings at which the stance phase starts, the swing phase starts, and the midpoint correspond to the timing at the center of the stance phase. The detection unit 155 sets the timing at the center of the stance phase as the starting point of the step cycle. The detection unit 155 also sets the time of the timing at the center of the next stance phase as the end point of the step cycle.

[0043] The detection unit 155 detects the timing of the minimum (first dorsiflexion peak) and the timing of the maximum (first plantar flexion peak) after the first dorsiflexion peak from the gait waveform of the plantar angle for one step cycle. Furthermore, the detection unit 155 detects the timing of the minimum (second dorsiflexion peak) after the first plantar flexion peak and the timing of the maximum (second plantar flexion peak) after the second dorsiflexion peak from the gait waveform of the plantar angle for the next step cycle. The detection unit 155 sets the timing of the midpoint between the timing of the first dorsiflexion peak and the timing of the first plantar flexion peak as the start point of the step cycle. Furthermore, the detection unit 155 sets the timing of the midpoint between the timing of the second dorsiflexion peak and the timing of the second plantar flexion peak as the end point of the step cycle.

[0044] The detection unit 155 extracts a gait waveform for one step gait cycle from the gait waveform generated by the generation unit 153. For example, the detection unit 155 extracts gait waveform data for one step gait cycle, starting from the midpoint between the timing of the first dorsiflexion peak and the timing of the first plantar flexion peak, and ending from the midpoint between the timing of the second dorsiflexion peak and the timing of the second plantar flexion peak. Similarly, the detection unit 155 extracts a gait waveform for one step gait cycle from the time series data of the sensor data based on the physical quantities (spatial acceleration, spatial angular velocity, spatial trajectory) related to the foot movement measured by the data acquisition device 11.

[0045] The detection unit 155 detects the timing of toe-off from the walking waveform of the forward acceleration (also called Y-direction acceleration). The detection unit 155 detects a maximum peak within a range of 20 to 40% of the walking cycle in the walking waveform of the Y-direction acceleration starting from the timing of heel lift. The maximum peak includes two maximum peaks and a minimum peak sandwiched between the maximum peaks. The timing of toe-off corresponds to the timing at which the minimum peak sandwiched between the two maximum peaks is detected.

[0046] The detection unit 155 detects the timing of heel strike from the walking waveform of the Y-direction acceleration or the vertical direction acceleration (also called Z-direction acceleration). The detection unit 155 detects the timing of heel strike using a characteristic peak that appears near the timing of heel strike. The detection unit 155 detects a minimum peak in the Y-direction acceleration starting from the timing of heel lift when the walking cycle exceeds 60%. This minimum peak corresponds to the timing of sudden deceleration of the foot at the swing leg end phase T7. The detection unit 155 also detects a maximum peak in the Y-direction acceleration starting from the timing of heel lift when the walking cycle is about 70%. This maximum peak corresponds to the timing of heel rocker. When the data acquisition device 11 is installed at the position of the arch of the foot, the data acquisition device 11 is located on the toe side of the rotation axis of the heel joint, so that an acceleration component in the forward direction (+Y direction) is generated during the heel rocker (rotation) movement. Therefore, the period of heel rocker operation includes a period in which the acceleration in the gravity direction (Z direction) is converted to the traveling direction (Y direction) by the rotation of the heel around the circumference after the heel strikes the ground. The timing of heel strike is included in the period from the timing when the minimum peak is detected to the timing when the maximum peak is detected. The detection unit 155 detects the midpoint timing between the timing when the minimum peak is detected and the timing when the maximum peak is detected as the timing of heel strike. The timing when the minimum peak is detected in the Y-direction acceleration and the timing when the maximum peak is detected in the Z-direction acceleration are almost the same. Therefore, instead of the timing when the minimum peak is detected in the Y-direction acceleration, the timing when the maximum peak is detected in the Z-direction acceleration may be used as the timing of sudden deceleration of the foot at the swing leg end phase T7.

[0047] The detection unit 155 detects the timing of the maximum peak between toe-off and heel-contact in the walking waveform of the Z-direction acceleration as the timing of the tibia vertical. The tibia vertical is a state in which the tibia is almost perpendicular to the ground. In the tibia vertical, the heel joint is in a neutral state, and the sole of the foot is perpendicular to the tibia. That is, in the tibia vertical, the roll angle associated with the rotation of the heel joint is 0 degrees. At the timing when the roll angle is 0 degrees, the peak of the walking waveform of the Z-direction acceleration is maximum. That is, the tibia vertical corresponds to the timing of the maximum value between toe-off and heel-contact in the walking waveform of the Z-direction acceleration.

[0048] The detection unit 155 detects the timing of the foot crossing, which is the timing when the gentle peak on the side closer to the tibia vertical between the tibia vertical and the toe off becomes maximum in the walking waveform of the Y-direction acceleration. In this embodiment, in a state where the left foot that is on the ground is in front of the right foot, the timing of the foot crossing is defined as the midpoint between the timing when the toe of the right foot passes the position of the heel of the left foot and the timing when the toe of the right foot passes the position of the toe of the left foot.

[0049] The measurement unit 157 acquires from the detection unit 155 the timing of a walking event and the value of sensor data for a predetermined period starting from the timing of a walking event. The measurement unit 157 performs measurement related to the lower limbs by applying the acquired sensor data value to a geometric model to which a constraint condition related to the movement of the lower limbs is imposed. For example, the measurement unit 157 measures the length of the part between the knee joint and the ankle joint (also called the lower leg) and the length of the part between the hip joint and the knee joint (also called the upper leg). For example, the measurement unit 157 measures the position of the knee joint and the hip joint. For example, the measurement unit 157 measures the time change (trajectory) of the position of the knee joint and the hip joint. For example, the measurement unit 157 measures the knee joint angle.

[0050] [Measurement processing] Next, an example of the measurement process by the measurement unit 157 will be described. The measurement process by the measurement unit 157 includes a first measurement process and a second measurement process. The first measurement process is a process for measuring the trajectory of the knee joint (knee) during a predetermined period starting from the crossing of the legs. The second measurement process is a process for measuring the trajectory of the hip joint (pelvis) and the angle of the knee joint during the period from the tibia vertical to the heel touching the ground.

[0051] <First measurement process> Next, the first measurement process will be described with reference to the drawings. In the first measurement process, the measurement unit 157 measures the trajectory of the knee at mid-swing T6 of the swing phase using the values ​​of sensor data for a predetermined period starting from the foot crossing.

[0052] The measurement unit 157 calculates the distance L between the data acquisition device 11 and the ankle joint using the value of the plantar angle at the timing of heel strike. FIG. 9 is a conceptual diagram for explaining a method of measuring the distance L between the data acquisition device 11 and the ankle joint in the sagittal plane. It is assumed that the angle between the lower leg and the sole of the foot is a right angle at the timing of heel strike. For example, the measurement unit 157 calculates the position (y fhs , z fhs ) and plantar angle θ hs into the following equation 1 or 2 to calculate the distance L between the data acquisition device 11 and the ankle joint. TIFF0007616358000001.tif6150 TIFF0007616358000002.tif6150For example, the measurement unit 157 may use an average value of the distance L calculated using the above formulas 1 and 2. For example, the measurement unit 157 measures the distance L between the data acquisition device 11 and the ankle joint for each stride cycle. For example, the measurement unit 157 may measure the distance L between the data acquisition device 11 and the ankle joint immediately after startup or at the timing of calibration. For example, the distance L between the data acquisition device 11 and the ankle joint may be measured in advance and stored in a storage unit (not shown) accessible by the measurement unit 157.

[0053] Next, the measurement unit 157 measures the trajectory of the knee in the middle swing phase T6 and the end swing phase T7 using the values ​​of the sensor data from the timing of foot crossing to heel contact. In the middle swing phase T6 and the end swing phase T7, the measurement unit 157 measures the trajectory of the knee under the following first to third constraint conditions. The first constraint condition is based on the knowledge of biomechanics disclosed in Non-Patent Document 1 (Non-Patent Document 1: Fukuchi et al., “A public dataset of overground and treadmill walking kinematics and kinetics in healthy individuals”, 2018, PeerJ, DOI 10.7717 / peerj.4640, pp. 1-17.).

[0054] The first constraint condition is that "the angle between the lower leg (tibia) and the sole of the foot is a right angle during the period from foot crossing to heel contact." In other words, during the period from foot crossing to heel contact (mid-swing phase T6 to end-swing phase T7), the ankle joint is almost neutral (≈90 degrees). Figure 3J (Ankle Dorsi / plantarflexion) on page 8 of Non-Patent Document 1 discloses data showing that the ankle joint is almost neutral (within ±5 degrees) during the period from mid-swing phase T6 to end-swing phase T7. In this embodiment, the angle between the lower leg (tibia) and the sole of the foot is considered to be a right angle during the period from foot crossing to heel contact.

[0055] The second constraint condition is that "extension / flexion of the knee joint is a rotational movement centered on the knee joint." In this embodiment, the movement of the knee joint is verified in a polar coordinate system centered on the knee joint. When performing a three-dimensional analysis, the trajectory of the knee joint can be verified in a spherical coordinate system.

[0056] The third constraint condition is that the knee moves at a constant speed during a predetermined time (measurement time period) starting from the leg crossing / tibia vertical. FIG. 10 is a graph showing an example of the time change (trajectory) of the knee position measured according to the walking of a person wearing e-skin (registered trademark) manufactured by Xenoma (registered trademark). For example, the knee trajectory is measured by applying the movement of each part of the body to a skeleton / muscle model. FIG. 10 includes the trajectory (solid line) of the Z direction position of the knee and the trajectory (dashed line) of the Y direction position. In this embodiment, the knee is considered to move at a constant speed during the period immediately after the leg crossing (within the range of the dashed line frame in FIG. 10) and the period immediately after the tibia vertical (within the range of the dashed line frame in FIG. 10).

[0057] The measurement unit 157 sets a first relative coordinate system (also called a knee origin coordinate system at the time of leg crossing) whose origin is the position of the knee joint at the timing of leg crossing. The measurement unit 157 measures the trajectory of the knee joint in the first relative coordinate system under the first to third constraint conditions. The measurement unit 157 converts the measured trajectory of the knee joint in the first relative coordinate system into the world coordinate system. In this embodiment, the origin of the world coordinate system in the first relative coordinate system is expressed as (y'0, z'0).

[0058] FIG. 11 is a conceptual diagram for explaining measurement of the trajectory of the knee immediately after the foot crossing (mid-swing phase T6). 10 , timing of leg crossing t 10 Timing t within a given period 11 and timing t 12 In the following, the state of the legs at the timing t 10 ~t 12 Change the symbol to t 1i (i=0, 1, 2). The measurement value of the data acquisition device 11 converted into the first relative coordinate system is written as (Y i , Z i11 shows an example in which sensor data values ​​at three timing points are used, but sensor data values ​​at four or more timing points may be used. The measurement unit 157 calculates the trajectory of the knee in the period from the timing of foot crossing to the timing of heel contact (mid swing phase T6, end swing phase T7) in the following procedure.

[0059] The measurement unit 157 converts the positions of the knee joint and the heel into a polar coordinate system based on the second constraint condition. 1i The position of the heel in the polar coordinate system (y a1i , z a1i ) have the relationship shown in the following Equation 3 and Equation 4. TIFF0007616358000003.tif6150 TIFF0007616358000004.tif6150In the above formulas 3 and 4, (y f1i , z f1i ) is the timing t 1i θ is the position of the data acquisition device 11 in the polar coordinate system at 1i is the timing t 1i This is the plantar angle at

[0060] Timing 1i The position (y f1i , z f1i ) have the relationship shown in Equation 5 and Equation 6 below. TIFF0007616358000005.tif6150 TIFF0007616358000006.tif6150In the above formulas 5 and 6, (Y i , Z i ) is timing t 1i 1 is the position of the data acquisition device 11 in the first relative coordinate system.

[0061] Based on the third constraint condition, by substituting Equation 3 into Equation 5 and Equation 4 into Equation 6, the following relationships in Equations 7 and 8 are obtained. TIFF0007616358000007.tif6150 TIFF0007616358000008.tif6150In the above equations 7 and 8, v ky is the timing t 10 ~t 12 is the knee velocity in the Y direction at kz is the timing t 10 ~t 12 is the knee velocity in the Z direction at

[0062] Timing 10 and timing t 12 By using formulas 5 to 8 in the above, the relationships in formulas 9 and 10 below are obtained. TIFF0007616358000009.tif6150 Similarly, TIFF0007616358000010.tif6150, timing t 11 and timing t 12 By using formulas 5 to 8 in the above, the relationships in formulas 11 and 12 below are obtained. TIFF0007616358000011.tif6150 TIFF0007616358000012.tif6150 The measurement unit 157 measures the timing t 1i The value of the plantar angle θ 1i Substituting the above equations 9 to 12, the thigh length R1 and the knee velocity in the Y direction v ky , and the knee velocity in the Z direction is v kz Calculate.

[0063] The measurement unit 157 calculates the timing t 1i The knee position in the world coordinate system (Y k1i , Z k1i ) to calculate TIFF0007616358000013.tif6150 The measurement unit 157 applies the timing t 1i Plantar angle θ at 1i By substituting the above, the position of the knee in the world coordinate system (Y k1i , Zk1i The trajectory of the knee can be obtained by connecting the knee positions in a time series for a given period starting from the crossing of the legs.

[0064] <Second measurement process> Next, the second measurement process will be described with reference to the drawings. In the second measurement process, the measurement unit 157 measures the pelvis trajectory and the knee joint angle at the end of the swing phase T7 using the values ​​of the sensor data during the period from the tibia vertical to the heel contact. In this embodiment, the position of the hip joint in the sagittal plane is regarded as the position of the pelvis. During the period near the tibia vertical, the rotation of the sole is regarded as being caused by the movement of the thigh and the lower leg. Here, the rotation of the sole is verified by the angle of the sole with respect to the ground in the sagittal plane (plantar angle). At the end of the swing phase T7, the trajectory of the knee is measured under the following fourth to sixth constraint conditions. The fourth and fifth constraint conditions are based on the knowledge of biomechanics disclosed in Non-Patent Document 1.

[0065] The fourth constraint condition is that "the hip joint angle is constant during the period from tibia vertical to heel contact." Figure 3D (Hip Flexion / Extension) on page 8 of Non-Patent Document 1 discloses data showing that the hip joint angle is almost constant from the latter half of mid-swing phase T6 to end-swing phase T7. In this embodiment, the hip joint angle is considered to be fixed during the period from tibia vertical to heel contact.

[0066] The fifth constraint condition is that "the thigh and the lower leg are in a straight line just before the heel touches the ground." Figure 3G (Knee Fix / Extension) on page 8 of Non-Patent Document 1 discloses data showing that just before the heel touches the ground, the thigh and the lower leg are in a nearly straight line and the knee joint is in a nearly extended state. In this embodiment, it is assumed that the thigh and the lower leg are in a straight line at the timing of the heel touching the ground.

[0067] The sixth constraint condition is that "the position of the pelvis in the sagittal plane at the timing of heel contact is the center position between both knees." Figure 12 is a graph showing an example of the time change (trajectory) of the positions of the left and right knees and the pelvis in the sagittal plane in the moving direction (Y direction) measured by motion capture. The distance between the left and right knees in the sagittal plane is maximum at the timing of heel contact. At the timing of heel contact, the position of the pelvis in the moving direction (Y direction) in the sagittal plane corresponds to the center position of the positions of the left and right heels in the moving direction (Y direction) in the sagittal plane.

[0068] The measurement unit 157 calculates the trajectory of the hip joint in a second relative coordinate system (knee joint origin coordinate system when the tibia is vertical) having the position of the knee joint at the time when the tibia is vertical as its origin under the fourth to sixth constraint conditions.

[0069] FIG. 13 is a conceptual diagram for explaining the measurement of the pelvis trajectory at the end of the swing phase T7. 20 , tibial vertical timing t 20 Timing t included in a specified period starting from 21 , timing of heel contact t 22 In the following, the state of the legs at the timing t 20 ~t 22 Change the symbol to t 2i (i=0, 1, 2). Although an example is shown in Fig. 13 in which sensor data values ​​at three timing points are used, sensor data values ​​at four or more timing points may be used. The measuring unit 157 calculates the trajectory of the pelvis at the end of the swing leg phase T7 by the following procedure.

[0070] The measurement unit 157 measures the knee joint angle θ based on the fourth and fifth constraint conditions. tsi Based on the fourth and fifth constraint conditions, the angle of the thigh with respect to the normal line (Z axis) of the ground is constant at the end of the swing leg phase T7. The measurement unit 157 calculates the angle of the thigh with respect to the normal line (Z axis) of the ground using the following formula 15 at the timing t 2i Knee joint angle θ tsi Calculate. TIFF0007616358000015.tif6150Timing of tibial vertical 20 Plantar angle θ at 20 is 0, and the timing of heel contact t 22 Plantar angle θ at 22 is θ hs It is.

[0071] The measurement unit 157 calculates the thigh length R2 based on the sixth constraint condition. Fig. 14 is a conceptual diagram for explaining a method for calculating the thigh length R2. The measurement unit 157 calculates the stride length D using the sensor data. The stride length D corresponds to the movement distance in the Y direction of the data acquisition device 11 at the timing of successive heel strikes, successive toe lifts, etc. The measurement unit 157 calculates the thigh length R2 using the following formula 16. TIFF0007616358000016.tif9150 For example, the measurement unit 157 uses a stride length calculated in the following procedure.

[0072] For example, the measurement unit 157 measures the movement distance in the Y direction of the data acquisition device 11 at the timing of successive heel strikes, successive toe offs, etc. as the stride length. The movement distance in the Y direction of the data acquisition device 11 can be calculated based on a trajectory calculated by second-order integration of the Y direction acceleration. For example, the difference in the Y direction position between successive heel strikes or successive toe offs corresponds to the stride length. Note that the measurement unit 157 may calculate the difference in the Y direction position during any successive walking event, not limited to heel strikes or toe offs, as the stride length.

[0073] For example, the measurement unit 157 may measure the stride length based on the timing of toe-off, heel-on, and foot-crossing. The measurement unit 157 extracts the section between toe-off and heel-on from the walking waveform of the Y-direction trajectory as the walking waveform of the Y-direction trajectory of one step. The measurement unit 157 calculates the absolute value of the difference between the spatial position at the foot crossing and the spatial position at the toe-off using the walking waveform of the Y-direction trajectory of one step. The absolute value of the difference between the spatial position at the foot crossing and the spatial position at the toe-off corresponds to the left foot step length (also called the first step length) in a state where the left foot is forward and the right foot is backward. The measurement unit 157 also calculates the absolute value of the difference between the spatial position at the timing of foot crossing and the spatial position at the heel-on using the walking waveform of the Y-direction trajectory of one step. The absolute value of the difference between the spatial position at the timing of foot crossing and the spatial position at heel contact corresponds to the right foot step length (also called the second step length) when the right foot is forward and the left foot is backward. The sum of the right foot step length and the left foot step length corresponds to the stride length. With this method, the step length of each foot can be measured individually.

[0074] The measurement unit 157 calculates the timing t 2i The position of the pelvis in the second relative coordinate system (y p2i , z p21 ) to calculate TIFF0007616358000017.tif6150 TIFF0007616358000018.tif6150 The measurement unit 157 measures the knee position (y k21 , z k21 ) into each of Equations 17 and 18 to obtain the position of the pelvis in the second relative coordinate system (y p2i , z p21 ) to calculate the knee position (y k21 , z k21 ) is measured by the method of the first measurement process.

[0075] For example, the measurement unit 157 uses the following expressions 19 and 20 to convert the coordinate system of the position of the pelvis in the second relative coordinate system into the second relative coordinate system (y p2i , z p21 ) to the world coordinate system (Y p2i , Zp21 ) TIFF0007616358000019.tif6150 TIFF0007616358000020.tif6150In the above formulas 19 and 20, (y k20 , z k20 ) is the timing of the tibial vertical 20 This is the position of the knee in the world coordinate system.

[0076] When measuring the three-dimensional position of the pelvis, a spherical coordinate system can be used instead of a polar coordinate system, including the length of the pelvis in the left-right direction (X direction). For example, when performing three-dimensional measurement, a constraint of constant speed in the x direction can be imposed, and the position of the pelvis can be measured using a determinant for converting from the polar coordinate system to the spherical coordinate system. Three-dimensional measurement allows the movement of the pelvis to be verified in three dimensions.

[0077] The measurement unit 157 outputs information related to the measured movement of the lower limbs. For example, the measurement unit 157 outputs information related to the trajectory of the knee joint at mid-swing phase T6 and end-swing phase T7, and the trajectories of the knee joint and pelvis (hip joint) at end-swing phase T7. For example, the measurement unit 157 outputs information related to the movement of the lower limbs to a display device (not shown). The information related to the movement of the lower limbs outputted to the display device is displayed on the screen of the display device. For example, the measurement unit 157 outputs information related to the movement of the lower limbs to an external system. The information related to the movement of the lower limbs outputted to the external system is used for any purpose.

[0078] As described above, the measurement unit 157 calculates the trajectory of the knee joint based on a geometric model in a first relative coordinate system (origin coordinate system at the time of leg crossing) having the position of the knee joint at the timing of leg crossing as its origin. Then, the measurement unit 157 calculates the trajectory of the hip joint (pelvis) and the knee joint angle based on a geometric model in a second relative coordinate system (origin coordinate system at the time of tibia vertical) having the position of the knee joint at the time of tibia vertical as its origin.

[0079] For example, if the length R1 of the lower leg is known, the calculation can be simplified. For example, the length R1 of the lower leg is calculated for the first few steps of walking, and the calculated value of the length R1 of the lower leg is used thereafter, thereby simplifying the calculation. For example, in the initial setting and calibration for using the measuring device 15, the length R1 of the lower leg, the length R2 of the upper leg, and the distance L between the data acquisition device 11 and the ankle are calculated, and these values ​​are recorded in a memory unit (not shown). In the measurement of the lower limbs, the calculation can be simplified by using the length R1 of the lower leg, the length R2 of the upper leg, and the distance L between the data acquisition device 11 and the ankle recorded in the memory unit.

[0080] (operation) Next, the operation of the measuring device 15 of the measurement system 10 of this embodiment will be described with reference to the drawings. In the following, the operation will be described with the measuring device 15 as the subject of the operation. FIG. 15 is a flowchart for explaining an example of the operation of the measuring device 15.

[0081] 15, first, the measurement device 15 acquires sensor data related to the physical quantity of the foot movement of a pedestrian walking while wearing the footwear on which the data acquisition device 11 is installed, from the data acquisition device 11 (step S11). The measurement device 15 acquires sensor data in a local coordinate system set in the data acquisition device 11. For example, the measurement device 15 acquires data on spatial acceleration and spatial angular velocity as sensor data related to the foot movement.

[0082] Next, the measurement device 15 converts the coordinate system of the sensor data from the local coordinate system of the data acquisition device 11 to the world coordinate system (step S12).

[0083] Next, the measurement device 15 generates time series data (gait waveform) of the sensor data after conversion to the world coordinate system (step S13). For example, the measurement device 15 generates a gait waveform of acceleration in the X-direction, Y-direction, and Z-direction. For example, the measurement device 15 generates a gait waveform of angular velocity around the X-axis, Y-axis, and Z-axis. For example, the measurement device 15 generates a gait waveform of spatial angle (plantar angle) using at least one of sensor data of spatial acceleration and spatial angular velocity. For example, the measurement device 15 generates time series data of spatial velocity and spatial trajectory.

[0084] Next, the measurement device 15 detects the timing of heel strike from the walking waveform (step S14). For example, the measurement device 15 detects the timing of heel strike from the walking waveform of the Y-direction acceleration and the Z-direction acceleration.

[0085] Next, the measurement device 15 calculates the distance between the data acquisition device 11 and the ankle joint using the plantar angle at the time of heel contact (step S15).

[0086] Next, the measuring device 15 executes a first measurement process (step S16). In the first measurement process, the measuring device 15 measures the trajectory of the knee joint in a predetermined period starting from the foot crossing under the first to third constraint conditions. For example, the predetermined period is the period immediately after the foot crossing. For example, the predetermined period is the period from the foot crossing to the tibia vertical. For example, the predetermined period is the period from the foot crossing to the heel touching the ground.

[0087] Next, the measurement device 15 executes a second measurement process (step S17). In the second measurement process, the measurement device 15 measures the trajectories of the knee joint and pelvis (hip joint) and the angle of the knee joint during the period from the tibia vertical to the heel contact (end swing phase T7) under the fourth to sixth constraint conditions.

[0088] Next, the measurement device 15 outputs information related to the measured lower limbs (step S18). For example, the information related to the lower limbs output from the measurement device 15 is output to a display device or an external system (not shown).

[0089] [First measurement process] Next, the first measurement process (step S16 in FIG. 15) by the measuring device 15 will be described in detail with reference to the drawings. Fig. 16 is a flowchart for explaining an example of the first measurement process by the measuring device 15. In the explanation following the flowchart in Fig. 16, the measuring device 15 will be described as the subject of operations.

[0090] In FIG. 16, first, the measuring device 15 extracts sensor data for a predetermined period starting from the crossing of the legs (step S111).

[0091] Next, the measurement device 15 transforms the coordinate system of the extracted sensor data into a first relative coordinate system whose origin is the position of the knee (knee joint) at the timing of the leg crossing (step S112).

[0092] Next, under the first to third constraint conditions, the measurement device 15 calculates the length of the lower leg and the moving speed of the knee by using the sensor data converted into the first relative coordinate system (step S113).

[0093] Next, the measurement device 15 calculates the trajectory of the knee in the world coordinate system based on the calculated length of the lower leg and the moving speed of the knee (step S114).

[0094] [Second measurement process] Next, details of the second measurement process (step S17 in FIG. 15) by the measuring device 15 will be described with reference to the drawings. Fig. 17 is a flowchart for explaining an example of the second measurement process by the measuring device 15. In the explanation following the flowchart in Fig. 17, the measuring device 15 will be described as the subject of operations.

[0095] In FIG. 17, first, the measurement device 15 extracts sensor data during the period from the tibia vertical to the heel contact (step S121).

[0096] Next, the measurement device 15 transforms the coordinate system of the extracted sensor data into a second relative coordinate system whose origin is the position of the knee (knee joint) at the time when the tibia is vertical (step S122).

[0097] Next, the measuring device 15 calculates the step length (step S123). For example, the measuring device 15 measures the movement distance of the data acquiring device 11 in the Y direction at the timing of successive heel strikes, successive toe offs, etc. as the stride length. For example, the measuring device 15 measures the stride length based on the timing of toe offs, heel strikes, and foot crossings. Note that the stride length may be a value measured in advance.

[0098] Next, the measurement device 15 calculates the length of the upper leg under the fourth to sixth constraint conditions, using the sensor data converted into the second relative coordinate system (step S124).

[0099] Next, the measuring device 15 calculates upper leg Based on the length of the joint and the trajectory of the knee, the knee joint angle and the trajectory of the hip joint (pelvis) are calculated (step S125).

[0100] Next, the measurement device 15 transforms the coordinate system of the calculated measurement values ​​related to the lower limbs from the second relative coordinate system to the world coordinate system (step S126).

[0101] As described above, the measurement system of this embodiment includes a data acquisition device and a measurement device. The data acquisition device is placed on the user's footwear. The data acquisition device measures spatial acceleration and spatial angular velocity in response to the user's walking. The data acquisition device generates sensor data based on the measured spatial acceleration and spatial angular velocity. The data acquisition device outputs the generated sensor data to the measurement device. The measurement device has an acquisition unit, a generation unit, a detection unit, and a measurement unit. The acquisition unit acquires sensor data related to foot movement. The generation unit generates time series data of the sensor data related to foot movement. The detection unit detects walking events from the time series data of the sensor data related to foot movement. The measurement unit performs measurement of the lower limbs based on a geometric model to which a constraint condition related to the movement of the lower limbs is imposed, using sensor data for a predetermined period starting from the timing of the walking event.

[0102] The measurement device of this embodiment performs measurements on the lower limbs (upper leg / lower leg) using time-series sensor data acquired by a single data acquisition device (sensor) in response to natural walking movements. The measurement device of this embodiment performs measurements on the lower limbs based on knowledge of biomechanics. For example, the measurement device of this embodiment measures the trajectories of the knee and pelvis (hip joint) and the angle of the knee joint. According to this embodiment, measurements on the lower limbs can be performed based on sensor data acquired by a single sensor.

[0103] In one aspect of this embodiment, the detection unit detects foot crossing and heel contact as walking events. The measurement unit converts a coordinate system of sensor data for a predetermined period starting from foot crossing into a first relative coordinate system with the position of the knee joint at the timing of foot crossing as the origin. The measurement unit calculates the length of the lower leg and the moving speed of the knee based on a geometric model to which the first to third constraint conditions are imposed in the first relative coordinate system. The first constraint condition is a condition that the angle between the lower leg and the sole of the foot is a right angle during the period from foot crossing to heel contact. The second constraint condition is a condition that the extension / flexion of the knee joint is a rotational movement centered on the knee joint. The third constraint condition is a condition that the knee moves at a constant speed during the predetermined period from foot crossing. The measurement unit calculates the trajectory of the knee during the period from foot crossing to heel contact based on the geometric model to which the first to third constraint conditions are imposed, using the length of the lower leg and the moving speed of the knee joint. According to this aspect, the trajectory of the knee can be measured during the period from foot crossing to heel contact.

[0104] In one aspect of this embodiment, the detection unit detects the tibia vertical as a walking event. The measurement unit converts the coordinate system of the sensor data from the tibia vertical to the heel strike into a second relative coordinate system with the position of the knee joint at the time of the tibia vertical as the origin. The measurement unit calculates the length of the upper leg based on a geometric model to which the fourth to sixth constraints are imposed in the second relative coordinate system. The fourth constraint condition is a condition that the angle of the hip joint is constant during the period from the tibia vertical to the heel strike. The fifth constraint condition is a condition that the upper leg and the lower leg are in a straight line immediately before the heel strike. The sixth constraint condition is a condition that the position of the pelvis in the sagittal plane at the timing of the heel strike is the middle position of both knees. The measurement unit uses the trajectory of the knee joint and the length of the upper leg to calculate the trajectory of the hip joint and the angle of the knee joint during the period from the tibia vertical to the heel strike based on a geometric model to which the fourth to sixth constraints are imposed. According to this embodiment, the trajectory of the pelvis (hip joint) and the angle of the knee joint during the period from tibia vertical to heel contact can be calculated.

[0105] Second Embodiment Next, a measurement system according to a second embodiment will be described with reference to the drawings. The measurement system of this embodiment generates an estimation model for estimating a user's physical condition by learning using information about the lower limbs measured by the method of the first embodiment.

[0106] (composition) 18 is a block diagram showing an example of the configuration of the learning system 20 of this embodiment. The learning system 20 includes a measurement device 25 and a learning device 27. The measurement device 25 and the learning device 27 may be connected by wire or wirelessly. The measurement device 25 and the learning device 27 may be configured as a single device.

[0107] The measurement device 25 has the same configuration as the measurement device 15 of the first embodiment. The measurement device 25 acquires sensor data from a data acquisition device (not shown). The measurement device 25 converts the coordinate system of the acquired sensor data from a local coordinate system to a world coordinate system. The measurement device 25 generates time series data (also called walking waveform) of the sensor data after conversion to the world coordinate system. The measurement device 25 detects walking events from the walking waveform. The measurement device 25 performs measurements on the lower limbs using a geometric model to which a constraint condition specific to walking is imposed based on the detected walking events. For example, the measurement device 25 measures the length of the part between the knee joint and the ankle joint (also called the lower leg) and the length of the part between the hip joint and the knee joint (also called the upper leg). For example, the measurement device 25 measures the positions of the knee joint and the hip joint. For example, the measurement device 25 measures the time change (trajectory) of the position of the knee joint and the hip joint. For example, the measurement device 25 measures the knee joint angle.

[0108] The measurement device 25 outputs information about the lower limbs to the learning device 27. For example, the measurement device 25 may accumulate the information about the lower limbs in a database (not shown). The information about the lower limbs includes foot information, knee information, and pelvis information. The foot information is information about the movement of the foot. For example, the foot information includes information such as the spatial acceleration, spatial angular velocity, spatial velocity, spatial angle (plantar angle), and spatial trajectory of the foot. The knee information is information about the movement of the knee. For example, the knee information includes information such as the position, trajectory, and angle of the knee joint. The pelvis information is information about the movement of the pelvis. For example, the pelvis information includes information such as the position and trajectory of the pelvis (hip joint).

[0109] The learning device 27 acquires information about the lower limbs from the measurement device 25. The learning device 27 may be configured to receive information about the lower limbs stored in a database (not shown). When using information about the lower limbs stored in the database, the learning device 27 acquires information about the lower limbs from the database.

[0110] The learning device 27 learns the received information on the lower limbs. For example, the learning device 27 learns information on the lower limbs extracted from the walking waveforms of multiple users as teacher data. The learning device 27 generates an estimation model learned for multiple users. The learning device 27 stores the generated estimation model in a storage device (not shown). The estimation model learned by the learning device 27 may be stored in an external storage device of the learning device 27.

[0111] For example, the learning device 27 performs learning using a linear regression algorithm. For example, the learning device 27 performs learning using a support vector machine (SVM) algorithm. For example, the learning device 27 performs learning using a Gaussian process regression (GPR) algorithm. For example, the learning device 27 performs learning using an algorithm such as random forest (RF). For example, the learning device 27 may perform unsupervised learning to classify input information according to input of information related to the lower limbs. There is no particular limitation on the learning algorithm performed by the learning device 27.

[0112] FIG. 19 is a conceptual diagram showing an example of learning the learning device 27 by using a data set of information on the lower limbs as an explanatory variable and an index of the physical condition as a response variable as teacher data. In the example of FIG. 19, at least one of a plurality of pieces of information on the lower limbs is used as an explanatory variable, and at least one of a plurality of physical conditions is used as a response variable. For example, the learning device 27 learns data on a plurality of subjects, and generates an estimation model that outputs an index value of the physical condition in response to input of information on the lower limbs extracted from the sensor data. An example of the index of the physical condition shown in FIG. 19 will be described below. Note that the index of the physical condition shown in FIG. 19 is an example, and does not limit the index of the physical condition learned by the learning device 27.

[0113] Balance is an index of symmetry between the legs while walking. For example, balance is a value that quantifies the difference between the left and right information about the lower legs while walking. The higher the symmetry between the legs while walking, the higher the balance.

[0114] The flexibility of the lower limbs is an index of the range of motion of the pelvis while walking. For example, the flexibility of the lower limbs is calculated based on the movement and rotation of the pelvis while walking. The greater the range of motion of the pelvis while walking, the higher the flexibility of the lower limbs.

[0115] Muscle tightness is an index of muscle tension. For example, it evaluates the tightness of the hip joint's internal rotation, iliopsoas, quadriceps, triceps surae, and gluteal muscles. Higher muscle tension tends to increase muscle tightness.

[0116] Gait stability is an index of gait variability. For example, gait stability can be evaluated based on the variability of pelvis acceleration. Large gait variability leads to large variability of pelvis acceleration and small gait stability.

[0117] The harmonic ratio is an index showing the symmetry of the waveform (also called the acceleration waveform) of the time series data of acceleration measured by an acceleration sensor attached near the pelvis. In walking, two cycles (one walking cycle) of acceleration changes are repeated, with one step of each foot on the left and right. The harmonic ratio in the vertical direction (Z direction) and the moving direction (Y direction) can be calculated by performing a Fourier transform with the time of one walking cycle as the basic cycle, as the ratio of the power sum of the even numbers (Even Harmonics) corresponding to the elements in the walking cycle to the power sum of the odd numbers (Odd Harmonics) that are elements that deviate from it. The harmonic ratio in the left and right direction (X direction) can be calculated as the reciprocal of the harmonic ratio in the vertical direction (Z direction) and the moving direction (Y direction) by regarding the odd harmonics as an element in the walking cycle, since two steps make one cycle. The more harmonic the walking, the greater the harmonic ratio, since it includes acceleration changes that occur in normal walking during one walking cycle. On the other hand, the harmonic ratio while walking tends to decrease in patients with Parkinson's disease, knee osteoarthritis, and the elderly. In addition, a decrease in the harmonic ratio while walking tends to increase the risk of falling. Therefore, the harmonic ratio can be used as an indicator of the progression of disease and the risk of falling.

[0118] As described above, the learning system of this embodiment includes a measurement device and a learning device. The measurement device detects walking events from time-series data of sensor data related to foot movement. The measurement device performs measurement of the lower limbs based on a geometric model to which a constraint condition related to the movement of the lower limbs is imposed, using sensor data for a predetermined period starting from the timing of the walking event. The learning unit learns information related to the lower limbs measured by the measurement device. The learning device generates an estimation model learned for a plurality of subjects. The learning device stores the generated estimation model in a storage device.

[0119] The learning system of this embodiment generates an estimation model that performs estimation according to the information about the lower limbs by learning using information about the lower limbs measured by a measurement device. According to this embodiment, it is possible to generate an estimation model that performs estimation according to the information about the lower limbs.

[0120] (Third embodiment) Next, a measurement system according to a third embodiment will be described with reference to the drawings. For example, the measurement system of this embodiment estimates a physical state of a user by using an estimation model trained by the learning device of the second embodiment.

[0121] (composition) Fig. 20 is a block diagram showing an example of the configuration of the measurement system 30 of this embodiment. The measurement system 30 includes a data acquisition device 31 and a measurement device 35. The data acquisition device 31 and the measurement device 35 may be connected by wire or wirelessly. The data acquisition device 31 and the measurement device 35 may be configured as a single device. Also, the measurement system 30 may be configured with only the measurement device 35, excluding the data acquisition device 31 from the configuration of the measurement system 30. Although only one data acquisition device 31 is shown in Fig. 20, one data acquisition device 31 each on the left and right feet (two in total) may be provided.

[0122] The data acquisition device 31 has the same configuration as the data acquisition device 11 of the first embodiment. The data acquisition device 31 is installed on at least one of the left and right feet. The data acquisition device 31 includes an acceleration sensor and an angular velocity sensor. The data acquisition device 31 converts the measured physical quantity into digital data (also called sensor data). The data acquisition device 31 transmits the converted sensor data to the measurement device 35.

[0123] The measurement device 35 receives sensor data from the data acquisition device 31. The measurement device 35 converts the coordinate system of the acquired sensor data from the local coordinate system to the world coordinate system. The measurement device 35 generates time series data (also called walking waveform) of the sensor data converted into the world coordinate system. The measurement device 35 detects walking events from the walking waveform. Similar to the measurement device 15 of the first embodiment, the measurement device 35 performs measurement on the lower limbs using a geometric model to which a constraint condition specific to walking is imposed based on the detected walking event. The measurement device 35 estimates the user's physical state based on the measured information on the lower limbs. For example, the measurement device 35 inputs information on the lower limbs to an estimation model generated by the learning device 27 of the second embodiment, and estimates the user's physical state. For example, the measurement device 35 compares information on the lower limbs measured at different timings to estimate the user's physical state. The measurement device 35 outputs the estimated physical state. For example, the measurement device 35 outputs information on the lower limbs to a display device (not shown) or an external system.

[0124] [Measuring equipment] Next, details of the measurement device 35 will be described with reference to the drawings. Fig. 21 is a block diagram showing an example of a detailed configuration of the measurement device 35. The measurement device 35 has an acquisition unit 351, a generation unit 353, a detection unit 355, a measurement unit 357, and an estimation unit 359.

[0125] The acquisition unit 351 has the same configuration as the acquisition unit 151 in the first embodiment. The acquisition unit 351 receives sensor data from the data acquisition device 31. The acquisition unit 351 outputs the received sensor data to the generation unit 353.

[0126] The generating unit 353 has the same configuration as the generating unit 153 in the first embodiment. The generating unit 353 acquires sensor data from the acquiring unit 351. The generating unit 353 converts the coordinate system of the acquired sensor data from the local coordinate system to the world coordinate system. The generating unit 353 generates time-series data (also called a walking waveform) of the sensor data after the conversion to the world coordinate system. The generating unit 353 outputs the generated walking waveform to the detecting unit 355.

[0127] The detection unit 355 has the same configuration as the detection unit 155 in the first embodiment. The detection unit 355 acquires a walking waveform from the generation unit 353. The detection unit 355 detects a walking event from the walking waveform. The detection unit 355 outputs to the measurement unit 357 the timing of the detected walking event and the value of the sensor data for a predetermined period starting from the walking event.

[0128] The measuring unit 357 has the same configuration as the measuring unit 157 of the first embodiment. The measuring unit 357 acquires the timing of a walking event and the value of sensor data for a predetermined period starting from the timing of the walking event from the detection unit 355. The measuring unit 357 applies the acquired sensor data value to a geometric model with a constraint condition, and performs measurement related to the lower limbs.

[0129] The measurement unit 357 outputs information related to the measured movement of the lower limbs to the estimation unit 359. For example, the information related to the lower limbs includes foot information, knee information, and pelvis information. The foot information is information related to the movement of the feet. For example, the foot information includes information such as the spatial acceleration, spatial angular velocity, spatial velocity, spatial angle (plantar angle), and spatial trajectory of the foot. The knee information is information related to the movement of the knee. For example, the knee information includes information such as the position, trajectory, and angle of the knee joint. The pelvis information is information related to the movement of the pelvis. For example, the pelvis information includes information such as the position and trajectory of the pelvis (hip joint).

[0130] The estimation unit 359 acquires information about the lower limbs from the measurement unit 357. The estimation unit 359 estimates the user's physical condition using the acquired information about the lower limbs. For example, the estimation unit 359 estimates the user's physical condition based on an index value output by inputting information about the lower limbs, such as foot information, knee information, and pelvis information, into an estimation model. For example, the estimation unit 359 compares multiple pieces of information about the lower limbs measured at different times to estimate the user's physical condition.

[0131] The estimation unit 359 outputs an estimation result based on information on the lower limbs. For example, when using an estimation model stored in a storage device such as an external server, the estimation model may be configured to be used via an interface (not shown) connected to the storage device. For example, the estimation unit 359 outputs the estimation result of the physical state to a display device (not shown). The estimation result of the physical state output to the display device is displayed on the screen of the display device. For example, the estimation unit 359 outputs the estimation result of the physical state to an external system. The estimation result of the physical state output to the external system is used for any purpose.

[0132] Fig. 22 is a conceptual diagram showing an example in which information about the lower limbs measured as the user walks is input to a pre-constructed estimation model 370, and an index value of the user's physical condition is output. The estimation model 370 outputs a physical condition according to the input information about the lower limbs. In the example of Fig. 22, at least one of a plurality of pieces of information about the lower limbs is input to the estimation model 370, and at least one of a plurality of physical conditions is output from the estimation model 370. As long as an estimation result about the physical condition can be output according to the input of information about the lower limbs, there is no limitation on the estimation result estimated using the estimation model 370.

[0133] FIG. 23 is a conceptual diagram showing an example in which an evaluation value according to a change in information about the lower limbs is output by inputting information about the lower limbs measured at different times as the user walks. Information about the lower limbs measured at different times is input to the estimation unit 359. The estimation unit 359 outputs an evaluation value according to a change in information about the lower limbs measured at different times. In the example of FIG. 23, information about the lower limbs before and after training is input to the estimation unit 359. In the example of FIG. 23, an evaluation value according to a change in information about the lower limbs before and after training is output. For example, if information about the lower limbs is improved after training compared to before training, the estimation unit 359 outputs an evaluation value indicating that the effect of the training was good. For example, if information about the lower limbs is deteriorated after training compared to before training, the estimation unit 359 outputs an evaluation value indicating that the effect of the training was poor. As long as an estimation result (evaluation value) regarding a change in the physical state can be output according to an input of information about the lower limbs measured at different times, there is no limitation on the estimation result estimated by the estimation unit 359.

[0134] (Examples) Here, application examples of this embodiment will be described with reference to the drawings. Fig. 24 and Fig. 25 are conceptual diagrams for explaining an example of application of this embodiment. In the following application example, sensor data is transmitted to a mobile terminal 360 carried by a user wearing shoes 300 in which a data acquisition device 31 is installed, according to the walking of the user. An application (measurement device 35) installed in the mobile terminal 360 displays information on the user's physical condition on the screen of the mobile terminal 360 based on the received sensor data.

[0135] [Application example 1] Fig. 24 is a conceptual diagram for explaining Application Example 1. In this application example, a physical condition is estimated based on information about the lower limbs using an estimation model 370 generated by the method of Fig. 22. It is assumed that an application having the function of the measurement device 35 is installed in a mobile terminal 360.

[0136] For example, the app generates recommendation information according to an index value of an estimated physical condition. For example, when an index value of a certain physical condition exceeds a threshold, the app generates recommendation information that may decrease the index value. For example, when an index value of a certain physical condition falls below a threshold, the app generates recommendation information that may increase the index value. For example, when an index value of a certain physical condition is close to a threshold, the app generates recommendation information that recommends maintaining the walking state at that time. For example, the app displays recommendation information according to an estimated physical condition on the screen of the mobile terminal 360.

[0137] 24, in response to a loss of left-right balance while walking, recommendation information such as "walk with awareness of left-right balance" is displayed on the screen of the mobile terminal 360. For example, a user who sees the information displayed on the screen of the mobile terminal 360 can recognize his or her own physical condition according to the information.

[0138] In this application example, recommendation information according to a physical state estimated based on information about the lower limbs is displayed on the screen of a mobile terminal 360 carried by the user. Therefore, according to this application example, recommendation information reflecting the physical state of the user can be provided to the user via the screen of the mobile terminal 360. For example, for a user who experiences pain in the lower back due to left-right balance in walking, it is desirable to walk so as to maintain normal left-right balance. According to this application example, by recommending awareness of left-right balance in accordance with left-right balance in walking, the user can continue walking while maintaining appropriate balance.

[0139] [Application example 2] Fig. 25 is a conceptual diagram for explaining an application example 2 of this embodiment. In this application example, a plurality of pieces of information on the lower limbs measured at different times are compared to estimate a physical condition using the method of Fig. 23. It is assumed that an application having the functions of the measuring device 35 is installed in the mobile terminal 360.

[0140] For example, the app generates notification information according to the estimated evaluation value. For example, when the evaluation value of the information on the lower limbs exceeds the target value, the app generates notification information indicating that the target has been achieved. For example, when the evaluation value of the information on the lower limbs falls below the target value, the app generates notification information indicating that the target has not been achieved. For example, the app displays notification information according to the estimated evaluation value on the screen of the mobile terminal 360.

[0141] 25, after training, in response to the evaluation value of information related to the lower limbs, such as the trajectories of the knees and pelvis, exceeding a target value, notification information saying "The training is working. Keep it up" is displayed on the screen of the mobile terminal 360. For example, a user who sees the information displayed on the screen of the mobile terminal 360 can recognize the effect of the training according to the information.

[0142] In this application example, notification information according to an evaluation value estimated based on information about the lower limbs measured at different times is displayed on the screen of the mobile terminal 360 carried by the user. Therefore, according to this application example, notification information according to changes in information about the lower limbs measured at different times can be provided to the user. For example, for a user who experiences pain in the lower back due to left-right balance in walking, it is desirable for the user to walk so as to maintain normal left-right balance. According to this application example, by notifying the user of the training effect according to changes in left-right balance in walking, the user can continue appropriate training.

[0143] As described above, the measurement system of the present embodiment includes a data acquisition device and a measurement device. The data acquisition device is disposed on the user's footwear. The data acquisition device measures spatial acceleration and spatial angular velocity according to the user's walking. The data acquisition device generates sensor data based on the measured spatial acceleration and spatial angular velocity. The data acquisition device outputs the generated sensor data to the measurement device. The measurement device includes an acquisition unit, a generation unit, a detection unit, a measurement unit, and an estimation unit. The acquisition unit acquires sensor data related to foot movement. The generation unit generates time series data of the sensor data related to foot movement. The detection unit detects walking events from the time series data of the sensor data related to foot movement. The measurement unit performs measurement related to the lower limbs based on a geometric model to which a constraint condition related to the movement of the lower limbs is imposed, using sensor data for a predetermined period starting from the timing of the walking event. The estimation unit estimates the physical state of the user based on information related to the user's lower limbs.

[0144] The measurement device of this embodiment can estimate the physical condition of a user based on information about the lower limbs measured using time-series data of sensor data.

[0145] In one aspect of this embodiment, the estimation unit inputs information about the user's lower limbs to an estimation model that outputs an index value of a physical condition in response to input of information about the lower limbs. The estimation unit estimates the user's physical condition based on the index value output from the estimation model in response to input of information about the lower limbs. The estimation unit outputs recommendation information in response to the user's physical condition. According to this aspect, it is possible to provide recommendation information in response to the user's physical condition based on information about the user's lower limbs using a pre-generated estimation model.

[0146] In one aspect of the present embodiment, the estimation unit compares a plurality of pieces of information related to the lower limbs measured at different times. The estimation unit estimates the user's physical condition based on an evaluation value related to a comparison result of the plurality of pieces of information related to the lower limbs. The estimation unit outputs notification information according to the user's physical condition. According to this aspect, notification information according to the user's physical condition can be provided based on information related to the user's lower limbs using a pre-generated estimation model.

[0147] In one aspect of the present embodiment, the estimation unit outputs information corresponding to the user's physical condition to a terminal device carried by the user. According to this aspect, the user can recognize his / her own physical information by visually checking the information displayed on the display unit of the terminal device.

[0148] (Fourth embodiment) Next, a measurement device according to a fourth embodiment will be described with reference to the drawings. The measurement device of this embodiment has a simplified configuration of the measurement devices of the first to third embodiments.

[0149] 26 is a block diagram showing an example of the configuration of the measurement device 45 of this embodiment. The measurement device 45 includes a detection unit 455 and a measurement unit 457. The detection unit 455 detects walking events from time-series data of sensor data related to foot movements. The measurement unit 457 performs measurement related to the lower limbs based on a geometric model to which constraint conditions related to the movement of the lower limbs are imposed, using sensor data for a predetermined period starting from the timing of the walking event.

[0150] The measurement device of this embodiment performs measurements on the lower limbs based on biomechanical knowledge using time-series data of sensor data acquired by a single sensor. That is, according to this embodiment, measurements on the lower limbs can be performed based on sensor data acquired by a single sensor.

[0151] (Hardware) Here, a hardware configuration for executing the control and processing according to each embodiment of the present disclosure will be described using an information processing device 90 in Fig. 27 as an example. Note that the information processing device 90 in Fig. 27 is an example of a configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0152] As shown in Fig. 27, an information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In Fig. 27, the interface is abbreviated as I / F (Interface). The processor 91, the main storage device 92, the auxiliary storage device 93, the input / output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. In addition, the processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are connected to a network such as the Internet or an intranet via the communication interface 96.

[0153] The processor 91 loads a program stored in the auxiliary storage device 93 or the like into the main storage device 92. The processor 91 executes the program loaded into the main storage device 92. In this embodiment, a software program installed in the information processing device 90 may be used. The processor 91 executes the control and processing according to this embodiment.

[0154] The main memory device 92 has an area in which a program is loaded. The processor 91 loads a program stored in the auxiliary memory device 93 or the like in the main memory device 92. The main memory device 92 is realized by a volatile memory such as a dynamic random access memory (DRAM). Furthermore, a non-volatile memory such as a magnetoresistive random access memory (MRAM) may be configured / added to the main memory device 92.

[0155] The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or a flash memory. Note that it is also possible to configure the main storage device 92 to store various data and omit the auxiliary storage device 93.

[0156] The input / output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices through a network such as the Internet or an intranet based on standards and specifications. The input / output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

[0157] Input devices such as a keyboard, a mouse, and a touch panel may be connected to the information processing device 90 as necessary. These input devices are used to input information and settings. When a touch panel is used as an input device, the display screen of the display device may also serve as an interface for the input device. Data communication between the processor 91 and the input devices may be mediated by an input / output interface 95.

[0158] The information processing device 90 may be equipped with a display device for displaying information. When the display device is equipped, the information processing device 90 is preferably equipped with a display control device (not shown) for controlling the display of the display device. The display device may be connected to the information processing device 90 via the input / output interface 95.

[0159] The information processing device 90 may also be provided with a drive device. The drive device mediates between the processor 91 and a recording medium (program recording medium) for reading data and programs from the recording medium, writing the processing results of the information processing device 90 to the recording medium, and the like. The drive device may be connected to the information processing device 90 via an input / output interface 95.

[0160] The above is an example of a hardware configuration for enabling the control and processing according to each embodiment of the present invention. The hardware configuration in FIG. 27 is an example of a hardware configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present invention. In addition, a program for causing a computer to execute the control and processing according to each embodiment is also included in the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also included in the scope of the present invention. The recording medium can be realized, for example, by an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may be realized by a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. In addition, the recording medium may be realized by a magnetic recording medium such as a flexible disk or other recording medium. When a program executed by a processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

[0161] The components of each embodiment may be combined in any manner. Furthermore, the components of each embodiment may be realized by software or by a circuit.

[0162] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0163] This application claims priority based on Japanese Patent Application No. 2021-067830, filed on April 13, 2021, the disclosure of which is incorporated herein in its entirety. [Explanation of symbols]

[0164] 10, 30 Measurement system 11, 31 Data acquisition device 15, 25, 35, 45 Measuring equipment 20 Learning System 27 Learning Device 111 Acceleration Sensor 112 Angular Rate Sensor 113 Control Unit 115 Transmitter 151, 351 Acquisition Department 153, 353 generation part 155, 355, 455 Detector 157, 357, 457 Measurement section 359 Estimation Department

Claims

1. A detection means for detecting walking events from time-series data of sensor data relating to foot movements; a measurement means for performing measurements related to the lower limbs based on a geometric model to which a constraint condition related to the movement of the lower limbs is imposed, using sensor data for a predetermined period starting from the timing of the walking event; The detection means includes: Detecting foot crossing and heel strike as the walking events; The measuring means is converting a coordinate system of the sensor data for a predetermined period starting from the leg crossing into a first relative coordinate system having an origin at a position of a knee joint at the timing of the leg crossing; calculate the length of the lower leg and the moving speed of the knee based on the geometric model to which a first constraint condition that the angle between the lower leg and the sole of the foot is a right angle during the period from the foot crossing to the heel contact, a second constraint condition that the extension / flexion of the knee joint is a rotational movement around the knee joint, and a third constraint condition that the knee moves at a constant speed during the predetermined period are imposed in the first relative coordinate system; A measurement device that calculates the trajectory of the knee during the period from the foot crossing to the heel contact, based on the geometric model to which the first constraint condition, the second constraint condition, and the third constraint condition are imposed, using the length of the lower leg and the movement speed of the knee.

2. The detection means includes: Detecting a tibia vertical as the gait event; The measuring means is A coordinate system of the sensor data from the tibial vertical to the heel contact is transformed into a second relative coordinate system having an origin at the position of the knee joint at the time of the tibial vertical; calculate a length of the upper leg based on the geometric model to which a fourth constraint condition that a hip joint angle is constant during the period from the tibia vertical to the heel contact, a fifth constraint condition that the upper leg and the lower leg are in a straight line immediately before the heel contact, and a sixth constraint condition that the position of the pelvis in the sagittal plane at the timing of the heel contact is the middle position of both knees are imposed in the second relative coordinate system; 2. The measurement device according to claim 1, further comprising: a measurement device for measuring a trajectory of the hip joint and an angle of the knee joint during a period from the tibia vertical to the heel contact, the measurement device being configured to use the trajectory of the knee joint and the length of the upper leg to calculate a trajectory of the hip joint and an angle of the knee joint during a period from the tibia vertical to the heel contact, based on the geometric model to which the fourth constraint condition, the fifth constraint condition, and the sixth constraint condition are imposed.

3. The measurement device according to claim 1 , further comprising an estimation unit that estimates a physical condition of the user based on information relating to the lower limbs of the user.

4. The estimation means includes: inputting information about the lower limbs of the user into an estimation model that outputs an index value of a physical state in response to input of information about the lower limbs; estimating a physical state of the user based on the index value output from the estimation model in response to input of information regarding the lower limbs; The measurement device according to claim 3 , further comprising: a measuring section for measuring a physical condition of the user, the measuring section being configured to output recommendation information according to a physical condition of the user.

5. The estimation means includes: Comparing a plurality of pieces of information about the lower limbs measured at different times; estimating a physical state of the user based on an evaluation value related to a comparison result of the plurality of pieces of information related to the lower limbs; The measuring device according to claim 3 , further comprising a display that displays notification information corresponding to the user's physical condition.

6. The estimation means includes:

6. The measuring device according to claim 4, wherein information corresponding to the physical condition of the user is output to a terminal device carried by the user.

7. A measuring device according to any one of claims 1 to 6, a data acquisition device that is placed on a user's footwear, measures spatial acceleration and spatial angular velocity in accordance with the user's walking, generates sensor data based on the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data to the measurement device.

8. The computer Detecting walking events from time series data of sensor data relating to foot movements; performing measurements related to the lower limbs based on a geometric model imposing constraints on the movement of the lower limbs using sensor data for a predetermined period starting from the timing of the walking event; In the detection, Detecting foot crossing and heel strike as the walking events; In the measurement, converting a coordinate system of the sensor data for a predetermined period starting from the leg crossing into a first relative coordinate system having an origin at a position of a knee joint at the timing of the leg crossing; calculate the length of the lower leg and the moving speed of the knee based on the geometric model to which a first constraint condition that the angle between the lower leg and the sole of the foot is a right angle during the period from the foot crossing to the heel contact, a second constraint condition that the extension / flexion of the knee joint is a rotational movement around the knee joint, and a third constraint condition that the knee moves at a constant speed during the predetermined period are imposed in the first relative coordinate system; A measurement method for calculating a trajectory of the knee during a period from the foot crossing to the heel contact, based on the geometric model to which the first constraint condition, the second constraint condition, and the third constraint condition are imposed, using the length of the lower leg and the movement speed of the knee.

9. A computer comprising: A process of detecting walking events from time series data of sensor data regarding foot movements; and performing a process of measuring the lower limbs based on a geometric model to which a constraint condition regarding the movement of the lower limbs is imposed, using the sensor data for a predetermined period starting from the timing of the walking event as a starting point; In the detection process, Executing a process of detecting foot crossing and heel strike as the walking events; In the measuring process, A process of converting a coordinate system of the sensor data for a predetermined period starting from the leg crossing into a first relative coordinate system having an origin at a position of a knee joint at the timing of the leg crossing; a process of calculating the length of the lower leg and the moving speed of the knee based on the geometric model to which a first constraint condition that the angle between the lower leg and the sole of the foot is a right angle during the period from the foot crossing to the heel contact is imposed, a second constraint condition that the extension / flexion of the knee joint is a rotational movement around the knee joint, and a third constraint condition that the knee moves at a constant speed during the predetermined period in the first relative coordinate system is imposed; and calculating a trajectory of the knee during the period from the foot crossing to the heel contact, based on the geometric model to which the first constraint condition, the second constraint condition, and the third constraint condition are imposed, using the length of the lower leg and the movement speed of the knee.

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