JP2022108984A - Walking measuring system - Google Patents

Abstract

To highly accurately track leg positions of an object person.SOLUTION: A walking measuring system 100 comprises a laser range sensor 10 and a data analysis section 24. The laser range sensor 10 acquires two-dimensional plane distance information over time. The data analysis section 24 acquires moving trajectories of leg parts of a subject 1 on the basis of, the two-dimensional plane distance information. The data analysis section 24 includes a leg detection part 26 for detecting observation values on the basis of the two-dimensional plane distance information, a predicted position calculation part 27c for calculating predicted positions of both leg parts on the basis of observation values in past time, and a correlation processing part 27d associating the observation values with the predicted positions of both leg parts. The correlation processing part 27d sets up a first gate GA1, also sets a second gate GA2 expanding according to the observation values of pieces of past time, with the predicted positions of both leg parts used as a reference, and associates the predicted positions of both leg parts relative to the observation values present in a range where the first gate GA1 and the second gate GA2 are overlapped.SELECTED DRAWING: Figure 10

Description

The present invention relates to a walking measurement system.

As part of health promotion and nursing care prevention projects associated with aging, walking training is being carried out at welfare facilities and the like. One of the motor tests that can evaluate functional mobility ability, which includes walking ability, dynamic balance and agility, is, for example, the Timed up and go (TUG) test. The TUG test is a test in which a subject (subject) sits on a chair, stands up, turns a marker 3 m ahead, and sits on the chair again.

In recent years, a walking measurement system described in Patent Document 1 has been developed as a system for tracking the leg positions of a subject walking in such a walking test. The walking measurement system includes a distance information acquisition unit that emits detection waves so as to scan along the horizontal direction and acquires distance information regarding the distance to an object over time based on the reflection state of the detection waves; and a data analysis unit that obtains a movement trajectory of the subject's leg based at least on: The data analysis unit detects an observed value that is a position candidate for the leg from the distance information, and associates (correlates) the observed value with the leg.

JP 2016-137226 A

In the above-described technique, the predicted positions of the legs of the subject are calculated from the observed values of the past time, and the gates are set based on the predicted positions. The trajectory of the subject's legs is acquired based on the observed values within the set gate. However, in this case, there is a possibility that the leg position of another person, such as a caregiver who assists the subject, may be erroneously tracked as the leg position of the subject.

An object of the present invention is to provide a walking measurement system capable of accurately tracking the leg positions of a subject.

A gait measurement system according to the present invention is a gait measurement system that tracks the position of the legs of a walking target person. a distance information acquiring unit that acquires, over time, distance information relating to the distance to the object that reflected the detection wave, and a movement trajectory of the subject's legs based on at least the distance information acquired by the distance information acquiring unit. a data analysis unit for detecting, based on the distance information, a leg detection unit for detecting one or a plurality of observation values that are position candidates for the leg; a predicted position calculation unit that calculates predicted positions of both legs of the subject; A first gate that expands based on the predicted position is set, and a second gate that expands based on the predicted position of both legs is set according to the observed value at the past time, and the area where the first gate and the second gate overlap. We map the predicted positions of both legs to the observations in .

In this gait measurement system, even if the leg positions of another person, such as a caregiver, are erroneously detected as observed values, the first gate prevents the observed values from being associated with the predicted positions of both legs. In addition, it can also be suppressed by the second gate. Therefore, erroneous tracking of the leg position of the other person as the leg position of the subject can be suppressed, and the leg position of the subject can be accurately tracked.

In the walking measurement system according to the present invention, the correlation processing unit determines whether both the observed value associated with the subject's right leg and the observed value associated with the subject's left leg at the immediately preceding processing time are If present, a second gate may be established centered around a position between the predicted position of the right leg and the predicted position of the left leg. In this case, the second gate can be set centering on the position between the predicted positions of both legs with high accuracy.

In the walking measurement system according to the present invention, if there is only an observation value associated with one of the legs at the immediately preceding processing time, the correlation processing unit A second gate that expands based on the predicted position may be set. In this case, the second gate can be set based on the predicted position of one of the legs, which has a high degree of certainty.

In the gait measurement system according to the present invention, the correlation processing unit determines whether both the observed value associated with the subject's right leg and the observed value associated with the subject's left leg at the immediately preceding processing time are If not, a second gate may be set up that extends around a position between the predicted position of the right leg and the predicted position of the left leg. Thus, when it can be assumed that the predicted positions of both legs are not highly accurate, the second gate can be set centering on the position between the predicted positions of both legs.

In the walking measurement system according to the present invention, the second gate may be an ellipse having a major axis to minor axis ratio of 8:5. As a result, when another person approaches the subject in the direction of the short axis of the second gate, the effect of accurately tracking the leg position of the subject is particularly effective.

A walking measurement system according to the present invention is a system used for a walking test of a subject. Then, after turning the marker, it is a test to return to the chair and sit down again, and the second gate may be elongated in the direction from the chair to the marker. In this case, the second gate can have a shape suitable for TUG testing.

In the walking measurement system according to the present invention, the first gate includes a gate that expands based on the predicted position of the subject's right leg, and a gate that expands based on the predicted position of the subject's left leg. good too. In this case, the two first gates can effectively prevent erroneously detected observation values from being associated with predicted positions of both legs.

In the walking measurement system according to the present invention, the range of the first gate may be variable and the range of the second gate may be fixed. In this case, the observation value can be flexibly limited by the variable first gate, and the observation value can be reliably limited by the fixed second gate.

According to the present invention, it is possible to provide a walking measurement system capable of accurately tracking the leg positions of a subject.

FIG. 1 is a block diagram showing the configuration of a walking measurement system according to one embodiment. FIG. 2 is a schematic diagram showing a TUG test to which the gait measurement system of FIG. 1 is applied. 3 is a plan view illustrating a laser range sensor of the walking measurement system of FIG. 1. FIG. FIG. 4A is a diagram illustrating an example of an observation pattern used for detecting observed values. FIG. 4(b) is a diagram for explaining an example of an observation pattern used for detecting observed values. FIG. 4(c) is a diagram for explaining an example of an observation pattern used for detecting observed values. FIG. 5(a) is a diagram for explaining an example of an observation pattern used for detecting an observed value. FIG. 5(b) is a diagram for explaining an example of an observation pattern used for detecting observed values. FIG. 6A is a diagram illustrating an example of an observation pattern used for detecting observed values. FIG. 6(b) is a diagram for explaining an example of an observation pattern used for detecting observed values. FIG. 7A is a diagram illustrating an example of an observation pattern used for detecting observed values. FIG. 7(b) is a diagram for explaining an example of an observation pattern used for detecting observed values. FIG. 8 is a diagram for explaining a walking model. FIG. 9 is a diagram for explaining predicted positions of the legs. FIG. 10 is a diagram explaining the first gate and the second gate. FIG. 11 is another diagram illustrating the first gate and the second gate. FIG. 12 is a flow chart showing processing in the walking measurement system of FIG. FIG. 13A is a diagram illustrating association using a particle filter. FIG. 13B is a diagram illustrating association using a particle filter. FIG. 13C is a diagram illustrating association using a particle filter. FIG. 14A is a diagram illustrating association using a particle filter. FIG. 14B is a diagram illustrating association using a particle filter. FIG. 14C is a diagram illustrating association using a particle filter. FIG. 15 is a diagram showing the tracking results of the TUG test by the gait measurement system according to the comparative example and the gait measurement system according to the example.

Preferred embodiments of the present invention will be described in detail below with reference to the drawings. In the following description, the same reference numerals are used for the same or corresponding elements, and overlapping descriptions are omitted.

FIG. 1 is a block diagram showing the configuration of a walking measurement system according to one embodiment. FIG. 2 is a schematic diagram explaining a TUG test to which the gait measurement system of FIG. 1 is applied. As shown in FIGS. 1 and 2, the walking measurement system 100 includes a chair 2, a marker 3, a laser range sensor (laser range sensor, distance information acquisition unit) 10, a pressure sensor 15, and an electronic control device 20. , a monitor 30 and a pole 40 . The gait measurement system 100 is a system that tracks the leg positions of a walking subject (subject) 1, and is capable of performing quantitative gait measurement and walking ability evaluation. A walking measurement system 100 is a system used for a walking test of the subject 1 . The gait measurement system 100 can be suitably applied to the TUG test, which is one of the gait tests for evaluating the functional mobility ability that integrates walking ability, dynamic balance, agility, and the like, for example.

In the TUG test, the subject 1 stands up from a chair 2, walks toward a marker 3 such as a cone ahead of a predetermined distance (for example, 3 m), turns the marker 3, and then returns to the chair 2. Sit in chair 2 again. The TUG test is one of the physical fitness measurement manuals of the Ministry of Health, Labor and Welfare. The TUG test requires walking at maximum walking speed without running. As the marker 3, various markers can be used as long as they mark the turn position. In the TUG test, a plurality of (here, two) poles 40 as reference members are used to align the marker 3 and the laser range sensor 10 . The pole 40 has a cylindrical outer shape with a predetermined height. The predetermined height is a height at which the upper end of the pole 40 is located above the height position of the laser light emitted from the laser range sensor 10 (the height of the light window portion of the laser range sensor 10). As the pole 40, the shape and the like are not particularly limited, and various poles are used. The walking measurement system 100 measures and evaluates the movement history and walking parameters of both legs of the subject 1 in such a TUG test.

By detecting the two poles 40 with the laser range sensor 10, the walking measurement system 100 can define a planar coordinate system having a straight line connecting the centers of the two poles 40 as one axis. The floor on which it is installed can be used as this plane coordinate system. In this case, if the positional relationship between the two poles 40 and the marker 3 is strictly adjusted (specifically, from the midpoint of the line segment connecting the two poles 40, the distance of 3 m on the vertical line of this line segment) If the marker 3 is installed at the position), even if the installation position and/or orientation of the laser range sensor 10 are roughly aligned, the relative positional relationship between the laser range sensor 10 and the marker 3 can be strictly grasped. It becomes possible.

The walking measurement system 100 determines leg observation positions (hereinafter referred to as “observed values ” or “observation position”) is obtained, tracking is performed using a Kalman filter, and walking parameters are calculated based on the obtained movement trajectory of both legs.

Specifically, in the walking measurement system 100, in a situation in which both legs of the subject 1 are approaching and a situation in which the legs cannot be temporarily observed by the laser range sensor 10, loss of sight of the legs and erroneous identification are reduced. Secondly, observation values are obtained based on five kinds of observation patterns. Also, the observed values and predicted values are associated with each other using an effective region (gate) that takes into account the state of the leg. Hereinafter, the "leg" may be simply referred to as the "leg", the "standing leg" may be simply referred to as the "standing leg", and the "free leg" may be simply referred to as the "free leg". The "left leg" is also simply referred to as the "left leg", and the "right leg" is also simply referred to as the "right leg". "One leg" may be simply referred to as "one leg", and "both legs" may be simply referred to as "both legs".

Actual human walking (speed of both legs) changes periodically. In particular, in the TUG test, the test subject 1 was instructed to walk at the maximum walking speed without running, so the speed change of both legs was large. Therefore, in the walking measurement system 100, an acceleration/deceleration model considering the walking phase is applied as the walking model. BACKGROUND ART When a person walks, he/she performs a periodic motion such as swinging one leg (a stance leg) and swinging the other leg (a free leg). "Walking phase" is a classification of the periodic movements of both legs during walking based on the state of swinging of the stance leg and the free leg (see FIG. 8, for example).

In the walking measurement system 100, a particle filter is used to associate observed values with predicted values, and a Kalman filter is used to update the state quantities of both legs for each hypothesis (association). At that time, the weight as the likelihood of each particle is calculated in consideration of the transition of the walking phase and the transition of the observation pattern.

FIG. 3 is a plan view illustrating the laser range sensor 10 of the walking measurement system 100 of FIG. 1. FIG. The laser range sensor 10 is a sensor for acquiring the locus of movement of both legs. The laser range sensor 10 acquires two-dimensional plane distance information (hereinafter also simply referred to as “distance information”) that is the distance to an object around the sensor on a two-dimensional plane at a certain height. As shown in FIG. 3, the laser range sensor 10 emits a laser beam (detection wave) L so as to scan along the horizontal direction, and reflects the laser beam L based on the reflection state of the laser beam L. Two-dimensional plane distance information regarding the distance to the legs F (right leg F _R and left leg F _L ) of the subject 1 is acquired over time.

Specifically, the laser range sensor 10 emits the laser light L and reflects the laser light L with a rotating mirror, thereby scanning the measurement area with the laser light L in a fan shape in the horizontal direction. More specifically, the laser range sensor 10 horizontally scans the laser light L in a fan shape so that the subject 1 and the poles 40 installed on both sides of the subject 1 are included in the measurement area. Then, for example, the reflected light of the laser light L reflected by the leg F of the subject 1 is received, and the detection angle (scanning angle) of the reflected light and the time from emission to reception of the laser light L (propagation time) are measured. Then, distance information including information on the angle and distance to the leg F is detected.

For example, the laser range sensor 10 scans the subject 1 with the laser light L before the TUG test trial or after the TUG test trial, and obtains leg information regarding the width of the leg F of the subject 1 (length corresponding to the diameter of the shin). To detect. The laser range sensor 10 outputs the detected distance information and leg information to the electronic control unit 20 .

The laser range sensor 10 is configured such that the height of the light window through which the laser light L is emitted can be adjusted. The laser range sensor 10 is installed so that the laser beam L is emitted at a height position corresponding to the height of the shin of the subject 1 (that is, the height from the ankle to the knee). The height of the light window portion of the laser range sensor 10 is higher than the height of the marker 3 so that the laser light L is not reflected by the marker 3 . The height of the light window portion of the laser range sensor 10 is such that the leg F of the subject 1 can be observed even when the leg F of the subject 1 leaves the floor during the swing phase, and the width of the leg F is the maximum. Considering the average height, for example, 0.15 m to 0.27 m (here, 0.27 m) from the floor surface (ground).

The laser range sensor 10 is arranged in a space formed below the seat portion of the chair 2 so that the emission direction of the laser light L is directed toward the marker 3 side. The laser range sensor 10 emits a laser beam L to the subject 1 from the rear side of the subject 1 approaching the marker 3 (moving away from the chair 2), that is, from the front side of the subject 1 moving away from the marker 3 (approaching the chair 2). Irradiate. The laser range sensor 10 is installed so that the emission direction of the laser light L is horizontal to the floor surface.

As the laser range sensor 10, a sensor with a ranging range of 0.1 to 30 m, a ranging angle of 270 deg, a ranging accuracy of ±30 mm, an angular resolution of 0.25 deg, and a sampling period of 25 ms/scan is used. there is Incidentally, the type and specifications of the laser range sensor 10 are not limited, and various types can be used according to the measurement environment, for example. Note that the laser range sensor 10 may be surrounded by a housing in order to suppress adverse effects of EMI (electromagnetic noise), for example.

The pressure sensor 15 is a sensor that detects an event in which the subject 1 stands up from the chair 2 and an event in which the subject 1 sits down on the chair 2 . The pressure sensor 15 is provided so as to be laid on the seat surface of the chair 2 . The pressure sensor 15 detects pressure information regarding the applied pressure and outputs the pressure information to the electronic control unit 20 . The pressure information of the pressure sensor 15 here is a voltage value, and the voltage value is A/D converted via the microcomputer board and input to the electronic control unit 20 . The electronic control unit 20 associates and stores the rising event and its time, and associates and stores the seating event and its time.

The electronic control unit 20 performs calculations based on the distance information acquired by the laser range sensor 10 to specify the position and speed of the leg F of the subject 1 over time, and acquires the walking characteristics of the subject 1 . The electronic control unit 20 outputs at least one of a start signal (described later) of sound (voice) and display to the subject 1 . The start signal is output from the electronic control unit 20 according to the operation of the measurer. The electronic control unit 20 stores the time of the start signal. Note that the start signal may be given by a device other than the electronic control unit 20 or by a measurer. In this case, the time of the start signal is input to the electronic control unit 20 by a device other than the electronic control unit 20 or by a measurer.

As the electronic control unit 20, for example, a personal computer or a dedicated control computer is used, and includes a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory), and the like. The electronic control unit 20 has a sensor data acquisition unit 21, a storage unit 22, a seating determination unit 23, and a data analysis unit 24 as functional elements.

The sensor data acquisition unit 21 acquires the distance information detected by the laser range sensor 10 and the pressure information detected by the pressure sensor 15 in synchronization. Further, the sensor data acquisition unit 21 may acquire the distance information in synchronization with the start signal time stored in the electronic control unit 20 . The sensor data acquisition unit 21 acquires leg information detected by the laser range sensor 10 . The sensor data acquisition unit 21 outputs the acquired distance information, leg information, and pressure information to the storage unit 22 . The sensor data acquisition unit 21 outputs the acquired pressure information to the seating determination unit 23 .

The storage unit 22 stores the distance information and the pressure information acquired by the sensor data acquisition unit 21 in association with the processing time (sampling time). The storage unit 22 stores leg information of the subject 1 acquired by the sensor data acquisition unit 21 . The seating determination unit 23 determines whether or not the subject 1 is seated based on the pressure information of the pressure sensor 15 acquired by the sensor data acquisition unit 21 . For example, the seating determination unit 23 determines that the person is seated when the voltage value from the pressure sensor 15 rises higher than the threshold value. The seating determination unit 23 determines that the person is not seated when the voltage value from the pressure sensor 15 is equal to or less than the threshold. The seating determination unit 23 outputs the determination result to the storage unit 22, and the storage unit 22 stores the determination result.

The data analysis unit 24 acquires the walking parameters of the subject 1 by analyzing the stored data stored in the storage unit 22 after the trial of the TUG test. Based on at least the distance information acquired by the laser range sensor 10, the data analysis unit 24 acquires the movement trajectory of the leg F of the subject 1 using a particle filter. The data analysis unit 24 has a leg detection unit (observed value detection unit) 26 , a leg tracking unit 27 , and a walking parameter calculation unit 28 . Gait parameters include general gait parameters and gait parameters for the TUG test.

General gait parameters are gait parameters for evaluating fall risk in a general gait test, and include landing position, walking speed [m / sec], number of steps [steps], walking rate [steps / sec], It includes stride length [m], stride length [m], stride interval [m], and stance time (at least one of single leg stance time [s] and double leg stance time [s]). Gait parameters for the TUG test are parameters for evaluating fall risk in the TUG test, reaction time (rise time) [seconds], TUG execution time [seconds], turn direction, first step time (first step leaving the floor time) [seconds] and the shortest distance to the marker 3 [m].

A walking parameter can be calculated by classifying it into the following five phases. In other words, gait parameters are calculated for each of the following five phases. At this time, the mean, standard deviation, and coefficient of variation of the stride length, stride length, step interval, single leg stance time, and double leg stance time are calculated for each phase. In addition, the walking parameter is not limited to the case where it is calculated by dividing into these five phases, and may be calculated as a whole (not divided), or calculated by dividing into two phases, the first half and the latter half (left and right). may Each phase can be divided based on the position of the leg F of the subject 1, for example.
(1) Starting phase
(2) Forward phase
(3) Turning phase
(4) Return phase
(5) Sitting down phase

The starting phase is the phase from the sitting state to the start of walking. For example, the starting phase is a phase from the chair 2 until the subject 1 leaves a certain range.
The forward phase is a phase from the start of walking to just before the turning motion. For example, the forward phase is a phase from when the subject 1 starts walking to before going around the marker 3 . The turning phase is the phase during turning motion. For example, the turning phase is the phase during which subject 1 turns around marker 3 . The return phase is a phase from after turning motion to before sitting down. For example, the return phase is a phase from after subject 1 turns around marker 3 to before sitting down. The sitting down phase is the phase when you return to your chair and turn around to sit down. For example, the sitting-down phase is a phase when the subject 1 leaves the certain range from the chair 2 once and then enters the certain range again. The certain range is, for example, a range of 0.25 m from the chair 2 to the marker 3 side.

The landing position is the position where the leg F has landed on the floor surface. The walking rate is the number of steps per unit time. The single leg stance time is the time from landing (contacting) to leaving the same leg. The double leg stance time is the time from landing of one leg to leaving the other leg. The stride is the distance from the landing position of one leg (for example, the heel position) to the landing position of the other leg. Stride length is the distance between adjacent landing positions for the same leg. The step distance is the distance between the heels of both legs on the frontal plane (when the subject 1 is viewed from the front).

The reaction time is the time from the start of the walking test (the time of the start signal) until the subject 1 stands up (the buttocks are separated from the seat surface of the chair 2). The TUG execution time is the execution time of the TUG test, which is the time from the start time to the end of the walking test (from when the subject 1 sitting on the chair 2 starts to move until he sits down again). The first step time is the time from the start time of the walking test to when the subject 1 raises the leg F for the first step (leaving the floor). The shortest distance to the marker 3 is the shortest distance between the center of the marker 3 and the locus of movement of the leg F.

In this embodiment, the data analysis unit 24 detects one or more observation values based on the distance information acquired by the laser range sensor 10 . The data analysis unit 24 randomly associates each of the predicted positions of the legs of the subject 1 with one of the observed values and the observation result of no observed value for each of the plurality of particles, and Get the position of the in relation to time. Along with this, the data analysis unit 24 calculates the weight from the transition of the walking phase and the transition of the observation pattern corresponding to both legs. The data analysis unit 24 selects particles with some association based on the weight from among the plurality of particles, and based on the predicted positions of both legs of the selected particles, the movement trajectory of the legs F of the subject 1 to get The processing of the data analysis unit 24 will be described in detail below.

[Leg detection]
4 to 7 are diagrams for explaining examples of observation patterns used for detecting observation values. FIGS. 4A and 6A are diagrams illustrating examples of SL (Single Leg) patterns. FIGS. 4B and 6B are diagrams illustrating examples of LT (twoLegs Together) patterns. FIGS. 4C and 7A are diagrams illustrating examples of FS_O (Forward Straddle Observable) patterns. FIGS. 5A and 7B are diagrams illustrating examples of FS_U (Forward Straddle Unobservable) patterns. FIG. 5B is a diagram illustrating an example of a UO (Unobservable) pattern.

The leg detection unit 26 uses a preset observation pattern to obtain an observation value (observation value vector y _k ^j :=(x _k ^j , y _k ^j ), (j=1...J)). Specifically, in view of the fact that the two-dimensional plane distance information of the leg F has a characteristic shape, the leg detection unit 26 performs edge detection processing on the two-dimensional plane distance information to determine the edge E of the leg F. Detect location. The leg detection unit 26 detects an observation value from the position of the edge E by pattern recognition using a plurality of observation patterns. At this time, the leg detection unit 26 uses the leg information regarding the width wl of the leg F of the subject ₁ , and _assumes that the width wl of the leg F is known. Leg detection by the leg detection unit 26 will be described in detail below. In addition, below, an "observation value vector" is also simply referred to as an "observation value."

When the subject 1 exists within the measurement range of the laser range sensor 10 and there is no obstacle between the laser range sensor 10 and the subject 1, the two-dimensional planar distance information acquired by the laser range sensor 10 is shown in FIG. It has a characteristic shape as shown in a) to FIG. 5(b). The leg detection unit 26 calculates an observed value by a leg detection method considering five types of observation patterns.

The leg detection unit 26 first sets the i-th distance information from the right acquired by the laser range sensor 10 to l _i , and the two-dimensional plane distance information of the adjacent laser range sensor 10 is the width w _l of the leg F of the subject 1. , edge e ^h _m (h=B, F) is detected when the following formula 1 is satisfied. At this time, if l _i >l _i+1 , edge e ^B _m =i and edge e ^F _m+1 = i+1. If l _i <l _i+1 , let edge e ^F _m =i and edge e ^B _m+1 = i+1. The superscript B indicates the edge E on the far side with respect to the laser range sensor 10, and the superscript F indicates the edge E on the front side. A subscript m (m=1, . . . , M _k ) indicates that the edge E is detected m-th. M _k is the total number of edges E detected at time k (hereinafter also referred to as “processing time”).

Then, based on the positional relationship of the four continuous edges _ehn , ^ehn ₊₁ , ^ehn ₊₂ , ^ehn ₊₃ and the width w _e between the ^{edges ehn} ₊₁ , ^ehn ₊₂ , it is classified into five types of observation patterns. and calculate the observed value _yk . However, n=1, . . . , M _k −3, and the subscript k indicates the observation position at time k.

As shown in FIGS. 4A and 6A, the observation pattern O1 as the SL pattern is an observation pattern when the leg F can be completely observed by the laser range sensor 10 alone. Observation pattern O1 has a combination of edges E satisfying ( ^eBn , _{eFn +1} , ^eFn ₊₂ , ^eBn ₊₃ ) and a width between edges ^E _{satisfying 0.5w1} < _we≤1.5wl . Fulfill. At this time, the _observed value _yk is obtained as shown in FIG. For example, the observed value y _k ^j is obtained from the line passing through (e ^F _n+1 +e ^F _n+2 )/2 and 1/2 the width w _l of the leg F, as shown.

As shown in FIGS. 4B and 6B, the observation pattern O2 as the LT pattern is an observation pattern when all the legs F are completely observable from the laser range sensor 10. FIG. Observation pattern O2 is such that the combination of edges ^E is ( ^eBn , _{eFn +1} , ^eFn ₊₂ , ^eBn ₊₃ ), ( ^eFn , ^eBn ₊₁ , _{eFn +2} , ^{eBn +} ₃ ) or ₍ ^eBn , e ^F _n+1 , e ^B _n+2 , e ^F _n+3 ) and the width between edges E satisfies _1.5wl <w _e < _3.0wl . At this time, the observed value _yk is obtained as shown in FIG. For example, the observed value y _k ^j+1 is obtained from the line passing through (e ^F _n+1 +3e ^F _n+2 )/4 and 1/2 the width w _l of the leg F, as shown. For example, the observed value y _k ^j is obtained from the line passing through (3e ^F _n+1 +e ^F _n+2 )/4 and 1/2 the width w _l of the leg F, as shown.

As shown in FIGS. 4(c) and 7(a), the observation pattern O3 as the FS_O pattern has a stepped pattern due to the influence of one of the legs F, a cane, etc., and the width between the positions of the edge E is 1/2 or more of the width wl of the leg F of the subject ₁ and the central position of the leg F can be observed from the laser range sensor 10. FIG. Observation pattern O3 is such that the combination of edges ^E satisfies ( ^eFn , _{eBn +1} , ^eFn ₊₂ , ^eBn ₊₃ ) or ( ^eBn , ^eFn ₊₁ , _{eBn +2} , ^{eFn +} ₃ ) and edges The width between E satisfies 0.5w _l ≤ w _e <1.5w _l . At this time, the observed value _yk is obtained as shown in FIG. For example, the observed value y _k ^j is obtained from the line passing through (e ^B _n+1 +e ^F _n+2 )/2 and ½ the width w _l of the leg F, as shown.

As shown in FIGS. 5A and 7B, the observation pattern O4 as the FS_U pattern has a width between the edges E that is the width of the leg F of the subject 1, as opposed to the observation pattern O3 as the FS_O pattern. This is an observation pattern when _wl is smaller than 1/2 and the center position of the leg F cannot be directly observed from the laser range sensor 10 . Observation pattern O3 is such that the combination of edges ^E satisfies ( ^eFn , _{eBn +1} , ^eFn ₊₂ , ^eBn ₊₃ ) or ( ^eBn , ^eFn ₊₁ , _{eBn +2} , ^{eFn +} ₃ ) and edges The width between E satisfies 0.3w _l <w _e <0.5w _l . At this time, unlike the observation patterns O1 to O3, the central position of the leg F cannot be directly observed by the laser range sensor 10, so the observed value _yk is virtually calculated as shown in FIG. 5(a). For example, the observed value y _k ^j is determined from the lines passing through e ^B _n+1 and e ^F _n+2 and ½ of the width _wl of the leg F, as shown. Hollow squares in FIG. 7(b) indicate virtual edges.

As shown in FIG. 5B, the observation pattern O5 as the UO pattern is when one leg F is hidden behind the other leg F or when the leg F is raised higher than the laser range sensor 10. , is the observation pattern when it cannot be observed.

Even in a situation where the leg F cannot be directly observed from the laser range sensor 10, the measurement accuracy of the position of the leg F can be improved by defining the observation pattern O4, which is the _{FS_U} pattern, and virtually obtaining the observed value yk. Expected to improve. In detecting the observed value _yk , it can be determined that the position of the edge E that does not satisfy the observed patterns O1 to O4 is not the leg F. Also, in order to avoid unnecessary correlation processing, an area that is wider than the area of the walking path by a predetermined length is set as the measurement area, and the observed value _yk outside the measurement area and the position of the edge E corresponding thereto are excluded.

In the detection of the observed value _yk , the observed value _yk is detected not at the center between the positions of the edge E but, for example, at a position inside by 1/2 of the width _wl of the leg F from the position of the edge E. be. As described above, by detecting the observed value _yk of the leg F according to a plurality of observation patterns O1 to O5, even when one of the legs F overlaps and is hidden when viewed from the laser range sensor 10, the leg It becomes possible to suitably detect the part F.

[Leg Tracking]
The leg tracking unit 27 performs prediction processing using a gait model that considers the gait phase, and estimates the positions and velocities of both legs. The prediction process uses a Kalman filter.

FIG. 8 is a diagram for explaining a walking model. BACKGROUND ART When a person walks, one leg is used as a pivot (standing leg) and the other leg (a free leg) is swung to proceed with walking. Both legs periodically move with acceleration and deceleration while changing between a free leg and a stance leg upon landing. As shown in FIG. 8, therefore, the leg tracking unit 27 applies a gait model including the following six gait phases including the time of rest, focusing on changes in the movement speed v and acceleration a of the leg F. .
・Phase 0: Standing with both legs at rest ・Phase 1: Left leg swinging (acceleration, swinging motion), right leg standing ・Phase 2: Left leg swinging (deceleration, motion toward landing) , Right leg standing ・Phase 3: Left leg standing, right leg swinging (acceleration, swinging motion)
・Phase 4: Left leg in stance, right leg in swing (deceleration, movement toward landing)
・Phase 5: Both legs swing (not seen when walking)

In the TUG test, the test subject 1 was instructed to walk at a maximum walking speed that did not run, so the speed change of both legs was large. Therefore, a model that accelerates and decelerates is applied as a walking model. In addition, when tracking the center of gravity of a person, a nonlinear model that includes angle and angular velocity as state variables is often used. , it is desirable to apply a linear motion model that has no directivity with respect to the direction of motion. Therefore, as the walking model, a linear movement model that does not have directivity with respect to the direction of movement is applied.

The leg tracking unit 27 sets a state equation for each of the left and right legs F using the position and velocity as state quantity vectors, and performs tracking processing on each leg F. FIG. The leg tracker 27 defines three motion models: stance, swing (acceleration) and swing (deceleration). A discrete-time system equation of each leg F when considering such a walking model involving acceleration and deceleration is given by Equation 2 below. The superscript f indicates the left and right legs with f=L, R, respectively.

The state quantity vector is shown in Equation 3 below. Equation 4 below is the position and velocity of each leg F of the subject 1 at time k.

A vector u ^f,m _k is an unknown external input (acceleration input) given according to the walking phase, and is shown in Equation 5 below. The superscript m indicates the mth motion model. A model 1 is a motion model of a standing leg (still), a model 2 is a motion model of a free leg (acceleration), and a model 3 is a motion model of a free leg (deceleration). The motion model 1 at the time of standing is given as 0, the motion model 2 accelerating with the free leg is given a positive value in the traveling direction, and the motion model 3 decelerating with the free leg is given a negative value in the traveling direction. The magnitude of the acceleration input is calculated as the average value of the norms of the acceleration vectors (see Equation 6 below) during the free leg for the past _Nac time steps.

In addition, since the movement of the leg ^F fluctuates, including the direction of movement, the fluctuation from the motion model is treated as an acceleration disturbance and included in the system noise (see Equation 7 below). shall give. The state transition matrix A ^f , the input matrix B ^f _u , and the drive matrix B ^f of the state equation are represented by Equation 8 below.

Further, when the observed value y ^f _k of each leg F obtained using the laser range sensor 10 is represented by Equation 9 below, the observation equation is given by Equation 10 below. Here, Δy ^f _k is normal white noise with mean 0 and covariance R ^f . At this time, the matrix C ^f is given by Equation 11 below.

As shown in FIG. 1, the leg tracking unit 27 includes a stance free leg determination unit 27a, a walking phase determination unit 27b, a predicted position calculation unit 27c, a correlation processing unit 27d, an update processing unit 27e, a weight calculation unit 27f, and a It has a resampling section 27g.

The stance/swing determination unit 27a performs stance/swing determination to determine whether the leg F is a stance (supporting leg) or a swing based on the speed of the leg. The stance/free leg determination unit 27a determines that the right leg is in the stance position when the following formula 12 is satisfied, and determines that the right leg is in the free position when the following formula 13 is satisfied. v _{st_th} indicates the stance speed threshold and v _{sw_th} indicates the swing speed threshold. The stance swing determination unit 27a similarly performs the stance swing determination for the left leg, that is, according to the following formulas 12 and 13 in which the right leg and the left leg are interchanged.

The walking phase determination unit 27b determines the walking phase based on the relative positional relationship and speed of both legs, and specifies which of phases 0 to 5 the walking phase is. Specifically, first, phase 0 is specified when both legs are standing. Next, when the left leg is a free leg and the right leg is a stance leg, when the inner product of the position vector of the left leg with respect to the right leg and the velocity vector of the left leg (see Equation 14 below) takes a positive value, Identify phase 1 and identify phase 2 when it takes a negative value. Similarly, when the left leg is the stance leg and the right leg is the free leg, phase 3 is specified when the inner product of the position vector of the right leg with respect to the left leg and the velocity vector of the right leg takes a positive value. and specifies phase 4 when it takes a negative value. If both legs are free legs, specify phase 5. When the walking phase determination unit 27b identifies phase 5 as the walking phase, the data analysis unit 24 determines that the subject 1 is running.

Here, in the leg tracking unit 27 of the present embodiment, a Rao-Blackwellized particle filter (hereinafter also simply referred to as a "particle filter") is used to consider the transition of the observation pattern of the gait phase and the leg F. It employs a two-legged tracking method based on the hypothesis of matching across In tracking multiple objects, it is necessary to estimate the correspondence c _k (c _k is the index of the correspondence) between the tracked object and the observed value y _k obtained by the sensor at time k, and the state quantity x _k of the tracked object. There is The correspondence and the state of the tracked object can be obtained only by the particle filter, but it is necessary to generate a large number of particles with respect to the state quantity for each combination of correspondences, and the amount of calculation is large. Therefore, in the particle filter, the state quantity and the correspondence of the tracked object are obtained by the following Equation 15. Note that the number of particles may be 100, for example.

In the particle filter, when a certain observation is obtained in p(c _1:k |vector y _1:k ), only the correspondence is calculated by the particle filter. Next, in p(vector x _k |c _1:k , vector y _1:k ), the state quantity is updated by the Kalman filter according to the associated observed value y _k . Since the state quantity update process is performed by the Kalman filter, it is not necessary to generate a large number of particles for each combination of associations. The leg tracking unit 27 calculates the weight as the likelihood of each particle based on the walking phase and the state transition probability of the observed pattern, thereby improving both-leg tracking performance in walking including turning movements of the subject 1, such as an elderly person. do.

The predicted position calculator 27c calculates the predicted positions of both legs by predicting the positions of both legs using a Kalman filter. The predicted position calculation unit 27c calculates, for each of a plurality of particles, the predicted leg position (hereinafter simply referred to as "predicted position"), which is the predicted position of each leg F at time k, based on the positions of both legs at past times. (see "x" in FIG. 9) is calculated. Superscripts (n), (n=1, . . . , N) indicate n-th particles. The predicted position vector ŷ ^f,(n) _k/k−1 of each leg F is given by Equation 16 below from the state equation shown in Equation (2) above. The vector x̂ ^f,(n) _k/k−1 denotes the prior state estimate of the nth particle at time k. The vector x̂ ^f,(n) _k−1/k−1 is the posterior state estimate of the nth particle at time k−1. In FIG. 9, "◇" indicates the observed value _yk , the line indicates the movement trajectory of the leg F, and "□" and "■" on the line indicate the positions of the left leg and the right leg. Each is shown (the same applies to subsequent figures). Note that, hereinafter, the "predicted position vector" is also simply referred to as the "predicted position".

The correlation processing unit 27d associates the predicted positions of both legs with the observed value _yk . The correlation processing unit 27d randomly associates the predicted positions of both legs with the observed value _yk for each particle. However, the correlation processing unit 27d sets a gate GA (see FIG. 10) at the predicted position in order to limit the observed value yk to be associated, and "( _i ) one or more contained in the gate GA Observed value y _k ”, and considering the case where the observed value y _k includes false detection, “(ii) Observation result with no observed value y ^f _k (when there is no observed value y _k )” Each of the predicted positions of both legs is randomly associated with each other.

In this embodiment, the gates GA include a first gate GA1 and a second gate GA2. That is, the correlation processing unit 27d sets the first gate GA1 that expands based on the predicted positions of both legs, and sets the second gate GA2 that expands based on the predicted positions of both legs according to the observed value at the past time. do. In the case of (i) above, the correlation processing unit 27d associates the predicted positions of both legs with the observed value yk present in the region where the first gate _GA1 and the second gate GA2 overlap.

The first gate _GA1 includes a gate that expands based on the predicted position of the subject's 1 right leg FR and a gate that expands based on the predicted position of the subject's 1 left leg _FL . Here, the first gate GA1 is two circular areas extending around each predicted position of both legs (see solid line "x" in FIG. 10). The range of the first gate GA1 is variable. For example, the range of the first gate GA1 when the leg F is determined to be in the free leg state is changed to be wider than the range of the first gate GA1 when the leg F is determined to be in the stance state. good too. For example, the range of the first gate GA1 may be changed so as to increase as the velocity v of the leg F increases.

The second gate GA2 has a shape elongated in the direction from the chair 2 toward the marker 3 . The second gate GA2 is an ellipse with the major axis extending from the chair 2 toward the marker 3 . The ellipse of the second gate GA2 has a ratio of major axis to minor axis of 8:5, taking into account that the caregiver is often present on the lateral side of the subject 1 . The range of the second gate GA2 is fixed. The range of the second gate GA2 is a predetermined range determined in advance based on the stride length of a typical person.

For example, if the horizontal direction from the chair 2 to the marker 3 is the x-coordinate and the horizontal direction from the chair 2 to the marker 3 is the y-coordinate, the ellipse of the second gate GA2 is ((xx _c )/a) ² +((y−y _c )/b) ² <1. At this time, the center coordinates are (xc, yc). a is a constant, for example 0.8. b is a constant, for example 0.5.

The setting of the second gate GA2 will be specifically described. The correlation processing unit 27d sets a second gate GA2 that expands based on the predicted positions of both legs according to the presence or absence of each observed value _yk of both legs at a past time. As shown in FIG. 10, the correlation processing unit 27d performs the observation value y _k associated with the right leg F _R of the subject 1 at the immediately preceding processing time and the left leg FL of the subject 1 with the observed value y _k associated with the If both observations _yk and _yk exist, set a second gate _GA2 that spreads around a position between the predicted position of the right leg FR and the predicted position of the left leg FL at the current processing time. do. As shown, the center of the second gate _GA2 here is the intermediate position between the predicted position of the right leg FR and the predicted position of the left leg _FL (dotted line "x" in the figure). reference)). The immediately preceding processing time is the processing time immediately before the current processing time. For example, the current processing time is time k and the previous processing time is time k-1.

As shown in FIG. 11, the correlation processing unit 27d determines that, at the current processing time, if there is only an observed value _yk associated with one of the two legs at the immediately preceding processing time, both legs A second gate GA2 is set that spreads around the predicted position of one of the parts.

The correlation processing unit 27d determines that both the observed value y _k associated with the right leg F _R of the subject ₁ and the observed value y _k associated with the left leg FL of the subject 1 are If not, at the current processing time, set a second gate _GA2 that extends around a position between the predicted position of the right leg FR and the predicted position of the left leg _FL . The center of the second gate _GA2 here is the intermediate position between the predicted position of the right leg FR and the predicted position of the left leg _FL .

Whether or not the observed value yk is included in the first gate _GA1 and whether or not the observed value _yk is included in the second gate GA2 are determined using Equation 17 below. d ^{(n) 2} _f,j is the Mahalanobis distance and is given by Equation 18 below. S ^f,(n) _k is the covariance matrix of the observed prediction errors (y ^j _k −ŷ ^f,(n) _k/k−1 ). G corresponds to each of the first gate GA1 and the second gate GA2. G is determined based on a chi-square (χ ² ) distribution with two degrees of freedom in the present embodiment where the observations y _k are two-dimensional vectors and a Kalman filter is used.

The update processing unit 27e updates the state quantity of the leg F after the correlation processing unit 27d associates the plurality of particles with each other. Specifically, the update processing unit 27e updates the Kalman filter (for example, state quantities such as covariance matrices) using observed values _yk associated by the correlation processing unit 27d for each of a plurality of particles. update). However, if there is no observed value _yk associated with the update processing unit 27e (for example, in the case of the observation pattern O5 as the UO pattern), the a priori state estimation value and the a priori covariance matrix are changed to the posterior state estimation value and the a priori covariance matrix, respectively. Let be the posterior covariance matrix. As a result, the positions and velocities of both legs are estimated for each of a plurality of particles. Further, the update processing unit 27e determines the walking phase by the walking phase determining unit 27b based on the estimated position and speed information of both legs for each of the plurality of particles.

The weight calculator 27f calculates the weight w ⁽ⁿ⁾ _k from the transition of the walking state of the subject 1 after the updating by the update processor 27e for each of the plurality of particles. Specifically, the weight calculation unit 27f calculates the weight w ⁽ⁿ⁾ _k of each particle by calculating the walking phase (phases 0 to 5) of each leg from one time earlier and the observation pattern of the leg F (observation pattern O1 to From the transition probability of O5), it is calculated (updated) as shown in Equation 19 below.

The second term in Equation 19 is calculated by multiplying the likelihood of the observed value _yk of the Kalman filter of each leg F and the state transition probabilities of the observed patterns O1 to O5. In this embodiment, the transition probability matrix from the observation patterns O1 to O5 one hour before is defined as shown in Equation 20 below. The horizontal arrangement of Equation 20 below corresponds to observation patterns O1 to O5 (SL pattern, LT pattern, FS_O pattern, FS_U pattern, UO pattern) before the transition (one time before) in order from the left. The values in the vertical columns of Equation 20 below indicate the probabilities of transition from the observation pattern before transition to the observation patterns O1 to O5 in order from the top. As represented by Equation 20 below, the transition of the observation patterns O1 to O5 for calculating the weight w ⁽ⁿ⁾ _k has the following features. In the case of the observation pattern O1 in which the leg is observed alone one time before, the probability of the observation pattern O1 being observed at the next time is set to be high. It is set so as not to transition to the hidden observation pattern O4. In the case of the observation pattern O2 in which both legs are aligned one time ago, the probability of being in the observation pattern O2 at the next time and the probability of transition to the observation pattern O1 in which the leg is observed alone are both high. is designed to be In the case of the observation pattern O3 in which both legs are observed in a stepped manner one time before, the probability that the observation pattern O3 will be observed at the next time as well as the observation pattern O1 in which the leg is observed alone or the other leg. It is designed to increase the probability of transition to the observation pattern O4, which is almost hidden in the . In the case of the observation pattern O4 that is almost hidden behind the other leg one time earlier, the probability that it will be the same observation pattern O4 at the next time, and the observation pattern O3 that will be out of the back of the other leg and can be observed. Alternatively, it is set to increase the probability of the observation pattern O5 being completely hidden behind one leg. In the case of the observation pattern O5 in which the leg was not observed one time ago, the probability that it will be the observation pattern O5 also at the next time is high, and it is set to transition to the other observation patterns with almost the same probability. .

The third item in Equation 19 relates to the state transition probability of the walking phase. In the present embodiment, the transition probability matrix from phases 0 to 5 one time earlier is defined as shown in Equation 21 below. The horizontal arrangement of Equation 21 below corresponds to phases 0 to 5 before the transition (one time before) in order from the left. The values in the vertical columns of Equation 21 below indicate the probability of transition from the phase before the transition to the phases 0 to 5 after the transition in order from the top. As represented by Equation 21 below, the transition of phases 0 to 5 for calculating the weight w ⁽ⁿ⁾ _k has the following characteristics. In the case of phase 0, which is the stationary state one time ago, the setting is made so that the probability of being in phase 0 at the next time and the probability of transitioning to phases 1 and 3 when starting to walk with either the left or right leg are high. ing. In the case of phase 1, in which the left leg is free and accelerating one time before, the probability of being in phase 1 and the probability of transitioning to phase 2, in which the left leg is decelerating, are set to be high at the next time. In the case of phase 2 in which the left leg is decelerating with a free leg one time before, the probability that it will be phase 2 at the next time as well as the phase 3 where the right leg starts to accelerate with a free leg or phase 0 where it stops as it is It is set so that the probability of transition is high. Phases 3 and 4, in which the right leg is the free leg, are also designed considering the same transition as phases 1 and 2. Moreover, in the case of phases 0 to 4, the probability of transition to phase 5, which does not appear during walking, is designed to be very small at the next time. In addition, if the phase 5 is one time before, it is designed to transition to the phases 0 to 4 at the next time with the same probability.

In this embodiment, when the phase of the walking test of the subject 1 is the starting phase, the weight calculator _27f selects the estimated position of the right leg FR from among the plurality of particles after the update by the update processor 27e. is to the left of the estimated position of the left leg _FL (in other words, the estimated position of the left _{leg FL} is to the right of the estimated position of the right leg FR). In other words, the weight calculator 27f selects some particles that would have been erroneously estimated because the left and right legs F were switched in the starting phase. Then, the weight calculation unit 27f reduces the weight w ⁽ⁿ⁾ _k for some of the selected particles (for example, to 1/10). Specifically, the weight calculator 27f calculates the weight w ⁽ⁿ⁾ Calculate (update) _k .

Whether or not the estimated position of the right leg FR is on the left side of the estimated position of the left _{leg FL} is determined by determining which estimated position y It can be determined based on whether the coordinates are large.

The resampling unit 27g resamples a plurality of particles so as to be selected and duplicated based on the weight w ⁽ⁿ⁾ _k . For example, the resampling unit 27g resamples a plurality of particles such that the smaller the weight w ⁽ⁿ⁾ _k , the smaller the particles are eliminated and the larger the weight w ⁽ⁿ⁾ _k , the larger the particles are replicated. Specifically, the resampling unit 27g normalizes the weight w ⁽ⁿ⁾ _k of each particle, and calculates the effective sample size by Equation 22 below. The resampling unit 27g performs resampling when N ^eff _k is smaller than the threshold. For example, the resampling unit 27g performs equal-interval resampling. The specific method of resampling performed by the resampling unit 27g is not particularly limited, and various known resampling methods may be used.

The leg tracking unit 27 estimates the positions and velocities of the legs of the plurality of particles at all times, and after the final weight w ⁽ⁿ⁾ _k is updated and resampled, and Particles of any correspondence are selected from among them based on the weight w ⁽ⁿ⁾ _k . Here, the leg tracking unit 27 selects one of the particles with the largest weight w ⁽ⁿ⁾ _k . The leg tracking unit 27 acquires the movement trajectory of the legs F of the subject 1 based on the positions and velocities of the legs of the selected particles.

[Calculation of walking parameters]
The walking parameter calculation unit 28 calculates walking parameters from the obtained movement trajectories of both legs. The walking parameter calculation unit 28 includes a stance time calculation unit 28a, a landing position calculation unit 28b, a step length, step interval and stride length calculation unit 28c, a first step time calculation unit 28d, a reaction time calculation unit 28e, and a TUG. and an execution time calculator 28f.

The stance time calculator 28a obtains the one-leg stance time and the two-leg stance time. In the present embodiment, in order to obtain the single-leg stance time and the double-leg stance time, pressure sensors were attached to the heels and metatarsals of both legs. is strictly calculated after obtaining the movement trajectory.

The landing position calculator 28b first obtains the landing position in order to calculate the stride length, stride length, and stride interval. It is difficult for the landing position calculation unit 28b to directly measure the position of the leg F because the laser range sensor 10 acquires the locus of movement of the leg F at the height of the shin. Therefore, the landing position calculation unit 28b considers that the leg F becomes perpendicular to the floor at the time when the speed of the leg F becomes minimum during standing, and calculates the position of the leg F at that time. The position is calculated as the landing position.

The step length, step interval and stride length calculation unit 28c calculates the step length, the step interval and the stride length based on the calculated landing position. The first step time calculation unit 28d calculates the time difference from the time when the start signal is given to the time when the leg F leaves the floor for the first step as the first step time. The reaction time calculator 28e calculates the time when the voltage value from the pressure sensor 15 installed on the chair 2 fluctuates from the sitting state (the time when it drops below the threshold value) as the reaction time. The reaction time calculator 28e calculates the time difference from the start signal time to the reaction time as the reaction time.

The TUG execution time calculation unit 28f utilizes the fact that the voltage value of the pressure sensor 15 fluctuates when the subject 1 sits on the chair 2 again, and sets the rise time of the voltage value as the end time. That is, based on the pressure information detected by the pressure sensor 15, seating of the subject 1 on the chair 2 is detected. The TUG execution time calculation unit 28f calculates the time difference from the start signal to the end time as the test execution time of the TUG test.

Also, the walking parameter calculator 28 calculates the shortest distance to the marker 3 based on the movement trajectory of both legs and the center position of the marker 3 . The walking parameter calculator 28 calculates other general walking parameters from the obtained movement trajectory of both legs.

The monitor 30 is an output unit that outputs the analysis result analyzed by the data analysis unit 24 . The monitor 30 displays the analysis results analyzed by the data analysis unit 24 and feeds them back to the subject 1 . The monitor 30 can also output distance information, leg information, and pressure information acquired by the sensor data acquisition unit 21 or stored in the storage unit 22 . Note that the monitor 30 may be configured to output audio or the like in place of or in addition to the display. Further, instead of or in addition to the monitor 30, a printer may be provided for printing out the calculation results and the like on a paper medium. The monitor 30 may be provided integrally with the electronic control device 20, and may be, for example, a display unit of a personal computer, a dedicated control computer, or the like.

[Walk measurement by walking measurement system 100]
Next, a case of measuring walking characteristics in a TUG test using the walking measurement system 100 of this embodiment will be described with reference to the flowchart shown in FIG.

As shown in FIG. 12, the measurement process in the TUG test includes a calibration phase (calibration before the trial), a measurement phase (measurement during the trial of the TUG test), and an analysis phase (data analysis after the trial). , are divided into three phases. In the calibration phase, two poles 40 are used to align the laser range sensor 10 and to measure the width wl of the leg F of the subject ₁ . After the calibration is finished, for example, the electronic control unit 20 notifies the subject 1 of a start signal (instruction to start the TUG test) by sound, and shifts to the measurement phase. In the measurement phase, the distance information from the laser range sensor 10 and the pressure information from the pressure sensor 15 are acquired and stored in synchronization. After the seating of the subject 1 is detected from the pressure information, the analysis phase is entered. In the analysis phase, the data analysis unit 24 tracks the positions of both legs based on the stored data, acquires the movement trajectory, and calculates the walking parameters based on the acquired movement trajectory. A specific description will be given below.

First, calibration is performed before the subject 1 starts to move. In calibration, scanning by the laser range sensor 10 is performed. That is, the laser light L is emitted from the laser range sensor 10 so as to scan along the horizontal direction, and distance information is acquired based on the reflection state of the laser light L (step S1). Thereby, the positions of the legs F of the subject 1 and the two poles 40 are observed.

The width (leg information) wl of the leg F of the subject ₁ is detected from the observed width between the edge positions of the leg F (step S2). The detected leg information is stored in the storage unit 22 . By obtaining the positional relationship between the two poles 40 with the laser range sensor 10, the measurer or the like can confirm whether or not the laser range sensor 10 is installed in an appropriate position and direction with respect to the marker 3. Then, the laser range sensor 10 is aligned (step S3). As described above, as long as the relative positional relationship between the two poles 40 and the marker 3 is strict, the installation accuracy of the laser range sensor 10 may be rough.

A series of processes in steps S1 to S3 are repeated until the calibration is completed (step S4). A few seconds after the end of the calibration, a start signal is sent to the subject 1 automatically (or manually by the measurer), and the TUG test is started (step S5).

During the trial of the TUG test, scanning is performed by the laser range sensor 10, and distance information regarding the distance of the leg F of the subject 1 is acquired over time by the sensor data acquisition unit 21 (step S6). Pressure information is acquired from the pressure sensor 15 by the sensor data acquisition unit 21 (step S7). The seating determination unit 23 determines whether or not the subject 1 has stood up based on the pressure information (step S8). If it is determined that the subject 1 is not standing, the process returns to step S6. If it is determined that the subject 1 is standing, the seating determination unit 23 determines whether or not the subject 1 is seated based on the pressure information (step S9). If it is determined that the person is not seated, the TUG test is still in progress, and the process returns to step S6. If it is determined that Subject 1 is seated, the TUG test is considered complete and the following data analysis is performed.

The data analysis includes a leg position tracking process and initialization of multiple particles in the particle filter (step S10). At time _k , the leg detector 26 detects the observed value yk based on the stored distance information (step S11). At time k, the predicted position is calculated for each particle by the predicted position calculator 27c (step S12). At time k, each particle is randomly associated by the correlation processing unit 27d (step S13). At time k, the state quantity is updated for each particle by the update processing unit 27e (step S14). This estimates the position and velocity of both legs at time k. At time k, the weight calculator 27f calculates the weight w ⁽ⁿ⁾ _k for each particle (step S15). At time k, resampling is performed by the resampling unit 27g (step S16).

Subsequently, for all of the stored data (distance information stored in association with time) stored in the storage unit 22, a data end determination is performed to determine whether each process related to the data analysis is completed (step S17). If No in step S17, the process proceeds to step S11 at the next time. On the other hand, if Yes in step S17, it is determined that the leg position tracking process has ended.

After that, the leg tracking unit 27 selects one of the particles with the largest weight w ⁽ⁿ⁾ _k . The leg tracking unit 27 acquires the movement trajectory of the legs of the subject 1 in the TUG test based on the positions and velocities of the legs of the selected particles. Then, the walking parameter calculation unit 28 calculates walking parameters from the obtained movement trajectory of both legs (step S18). The analysis result of the stored data analysis and/or walking parameters are output to the monitor 30 (step S19).

As described above, in the walking measurement system 100, even if the leg position of another person such as a caregiver is erroneously detected as the observed value _yk , the observed value _yk is associated with the predicted positions of both legs. can be suppressed by the first gate GA1 and also by the second gate GA2. Therefore, erroneous tracking of the leg position of the other person as the leg position of the subject 1 can be suppressed, and the leg position of the subject 1 can be accurately tracked.

In the walking measurement system 100, the correlation processing unit _27d combines the observed value _yk associated with the right leg F _R of the subject 1 and the observed value associated with the left leg FL of the subject 1 at the immediately preceding processing time. _yk are present, then set a second gate _GA2 that _spans a position between the predicted position of the right leg FR and the predicted position of the left leg FL. In this case, the second gate GA2 can be set around the position between the predicted positions of both legs with high accuracy.

In the walking measurement system 100, if there is only an observed value _yk associated with one of the legs at the immediately preceding processing time, the correlation processing unit 27d A second gate GA2 that expands based on the predicted position is set. In this case, the second gate GA2 can be set based on the predicted position of one of the legs, which has a high degree of accuracy.

In the walking measurement system 100, the correlation processing unit _27d determines that both the observed value _yk associated with the right leg F _R and the observed value _yk associated with the left leg FL at the immediately preceding processing time are If not, then set a second gate _GA2 that extends around a position between the predicted position of the right leg FR and the predicted position of the left leg _FL . As a result, when it can be assumed that the predicted positions of both legs are not highly accurate, the second gate GA2 can be set centering on the position between the predicted positions of both legs.

In the walking measurement system 100, the second gate GA2 is an ellipse with a major axis to minor axis ratio of 8:5. As a result, the effect of accurately tracking the leg position of the subject 1 is particularly effective when there is likely to be another person close to the subject 1 in the short axis direction of the second gate GA2.

A walking measurement system 100 is a system used for a TUG test of a subject 1, and a second gate GA2 has an elongated shape in the direction from the chair 2 toward the marker 3. As shown in FIG. In this case, the second gate GA2 can have a shape suitable for the TUG test.

In the walking measurement system 100, the first gate _GA1 includes a gate that expands based on the predicted position of the right leg FR of the subject 1 and a gate that expands based on the predicted position of the left leg _FL of the subject 1. I'm in. In this case, the two first gates _GA1 can effectively prevent the erroneously detected observed value yk from being associated with the predicted positions of both legs.

In the walking measurement system 100, the range of the first gate GA1 is variable, and the range of the second gate GA2 is fixed. In this case, the observation value yk can be flexibly limited by the variable first gate _GA1 , and the observation value _yk can be reliably limited by the fixed second gate GA2.

In the walking measurement system 100, the weight w ⁽ⁿ⁾ _k of some particles that would have been erroneously estimated due to the switching of the left and right legs F in the starting phase is reduced. As a result, it is possible to suppress erroneous tracking in which the left and right legs F are tracked while being reversed from the actual ones in the starting phase. The behavior of the leg F of the subject 1 in the starting phase is characteristic, and erroneous tracking in which the left and right legs F are switched is particularly likely to occur in the starting phase, so the effect of suppressing the erroneous tracking in the starting phase. is valid.

In the walking measurement system 100, each predicted position of both legs of the subject 1 is associated with an observed value for each of a plurality of particles. This allows for multiple association possibilities. Then, particles with some association are selected from among the plurality of particles based on the weight w ⁽ⁿ⁾ _k , the predicted positions of both legs are determined, and the movement trajectory is obtained. This weight w ⁽ⁿ⁾ _k is calculated from the unique features found in the leg F of the subject 1 walking, that is, the transition of the walking phase and observation pattern. Therefore, it is possible to acquire the movement trajectory based on which of the plurality of possible associations is most likely for the subject 1 . Therefore, it is possible to prevent the movement trajectory from being acquired based on an erroneous association, and it is possible to accurately track the leg position of the subject 1 . It is possible to reduce erroneous tracking.

The walking measurement system 100 calculates the weight w ⁽ⁿ⁾ _k based on the transition of the walking phase. In this case, the weight w ⁽ⁿ⁾ _k of each particle can be calculated in consideration of the fact that the walking phase changes periodically.

In the walking measurement system 100, the weight w ⁽ⁿ⁾ _k is calculated based on the observation pattern transition corresponding to both legs. In this case, the weight w ⁽ⁿ⁾ _k of each particle can be calculated in consideration of the fact that the observation patterns of both legs change periodically.

In the walking measurement system 100, the data analysis section 24 has a leg detection section 26, a predicted position calculation section 27c, and a correlation processing section 27d. The leg detection unit 26 detects the observed value _yk from the distance information. The predicted position calculator 27c calculates the predicted positions of both legs for each of a plurality of particles based on the positions of the legs at the past time. The correlation processing unit 27d sets a gate GA based on the predicted position for each of a plurality of particles, and for any of the observed value _yk existing in the gate GA and the observed value _yk not present, Then, each predicted position of both legs of the subject 1 is randomly associated. In this case, each particle can be associated using the gate GA set based on the predicted position.

In the walking measurement system 100, the data analysis section 24 has an update processing section 27e and a weight calculation section 27f. The update processing unit 27e updates the state quantity of the leg F for each of a plurality of particles after the correlation processing unit 27d performs the association. After updating by the update processing unit 27e, the weight calculation unit 27f calculates the weight w ⁽ⁿ⁾ _k from the transition of the walking phase and the observation pattern for each of the plurality of particles. In this case, the weight w ⁽ⁿ⁾ _k of each particle can be specifically calculated in tracking the leg position using the particle filter.

In the walking measurement system 100, the predicted position calculator 27c calculates the predicted positions of both legs by predicting the positions of both legs using a Kalman filter. The update processing unit 27e updates the Kalman filter. In this case, the Kalman filter can be used to accurately track the leg position of the subject 1 .

In the walking measurement system 100, the data analysis section 24 has a resampling section 27g. The resampling unit 27g resamples a plurality of particles so that they are selected and duplicated based on the weight w ⁽ⁿ⁾ _k . In this case, the number of particles required for tracking leg positions using a particle filter can be reduced. With a small number of particles, it is possible to hold the correspondence hypothesis and estimate the state quantity.

13 and 14 are diagrams for explaining association using a particle filter. 13(a) shows the result of matching for the first particle PT1 at time k, FIG. 13(b) shows the result of matching for the second particle PT2 at time k, and FIG. 13(c) shows the result of matching at time k. 3 shows the result of association in the third particle PT3 of . 14(a) shows the result of matching for the first particle PT1 at time k+1, FIG. 14(b) shows the result of matching for the second particle PT2 at time k+1, and FIG. 14(c) shows the result of matching at time k+1. 3 shows the result of association in the third particle PT3 of .

In the examples of FIGS. 13(a) to 13(c), erroneously detected observation values y _k ¹ and y _k ² are detected. For the first particle P1, the predicted position of the left ^leg is associated with the observed value _yk1 , and the predicted position of the right ^leg is associated with the observed value _yk2 . For the second particle P2, the predicted position of the left ^leg is associated with the observed value _yk2 , and the predicted position of the right _leg is associated with the observed value ^yk1 . In the third particle P3, the predicted position of the left leg is associated with the observation result of no observed value, and the predicted position of the right ^leg is associated with the observed value _yk2 . For the third particle, since there is no observed value associated with the predicted position of the left leg, the predicted position of the left leg is acquired as the position of the left leg. In such an example, weight w ⁽¹⁾ _k > weight w ⁽²⁾ _k > weight w ⁽³⁾ _k .

In the examples of FIGS. 14(a) to 14(c), the observed value y _k+1 ¹ and the observed value y _k+1 ² are detected. For the first particle P1, the predicted position of the left leg is associated with the observed value y _k+1 ² , and the predicted position of the right leg is associated with the observed value y _k+1 ¹ . For the second particle P2, the predicted position of the left leg is associated with the observed value y _k+1 ² , and the predicted position of the right leg is associated with the observed value y _k+1 ¹ . For the third particle P3, the predicted position of the left leg is associated with the observed value y _k+1 ¹ , and the predicted position of the right leg is associated with the observed value y _k+1 ² . In such an example, weight w ⁽³⁾ _k+1 >> weight w ⁽²⁾ _k+1 > weight w ⁽¹⁾ _k+1 . As a result, by resampling, for example, the first particle PT1 is eliminated and the third particle PT3 is duplicated.

FIG. 15 is a diagram showing the tracking results of the TUG test by the gait measurement system according to the comparative example and the gait measurement system 100 according to the example. In the test results, the example corresponds to the walking measurement system 100 . Comparative Example 1 corresponds to a system similar to the walking measurement system 100 except that the matching using the particle filter is not performed and the second gate GA2 is not set. Comparative Example 2 does not perform the above process of reducing the weight w ⁽ⁿ⁾ _k for some particles misestimated due to the switching of the left and right legs F in the starting phase, and the second gate GA2 is set to It corresponds to the same system as the walking measurement system 100 except that it is not set.

The number of subjects 1 to be tested is 138 persons. In the figure, the ``swapping of left and right legs'' in the _mistracking content means that the left leg FL of the subject 1 is _tracked as the right leg FR and/or the right leg FR is _tracked as the left leg _FL . This is when the situation tracked as occurs. In the mistracking content in the figure, "wrong tracking of the leg of the attendant" is a case where the leg of the attendant is erroneously tracked as the leg F of the subject 1. FIG.

As shown in FIG. 15, in the example, compared with the first and second comparative examples, the overall tracking success rate is improved. This makes it possible to confirm the effect of the walking measurement system 100 that the leg positions of the subject 1 can be tracked with high accuracy.

Moreover, in the example, the number of erroneous tracking of the attendant's leg is reduced compared to the comparative examples 1 and 2. Accordingly, it is possible to confirm the effect of the walking measurement system 100 that it is possible to suppress erroneous tracking of the leg position of the attendant (another person) as the leg position of the subject 1 . In particular, in the embodiment, the number of erroneous tracking of the leg of the _attendant ^is reduced regardless of the phase. It can be seen that the setting of the second gate GA2 contributes more than the processing.

Moreover, in the example, the number of changes of the left and right legs F in the starting phase is greatly reduced compared to the second comparative example. As a result, it is possible to confirm the action and effect of the walking measurement system 100 that it is possible to suppress erroneous tracking in which the left and right legs F are tracked in the starting phase while being actually switched. Since the effect is brought about in the starting phase, it can be seen that the above process of reducing the weight w ⁽ⁿ⁾ _k in the starting phase particularly contributes.

In the walking measurement system 100, the laser range sensor 10 and the pressure sensor 15, which are compact and capable of acquiring distance information on a two-dimensional plane, are used to track the position of both legs with little misidentification in TUG test measurement and to improve measurement accuracy. can be realized. In the gait measurement system 100, analysis (post-analysis) is performed by the data analysis unit 24 after the gait test is completed, rather than performing real-time analysis during the gait test. This makes it possible to calculate precise movement trajectories and walking parameters. In addition, in the walking measurement system 100, it is of course possible to analyze the walking characteristics by the data analysis unit 24 while trying the walking test.

[Modification]
Although one aspect of the present invention has been described above, one aspect of the present invention is not limited to the above-described embodiments, and may be modified or applied to other things within the scope of not changing the gist described in each claim. .

In the above embodiment, the weight w ⁽ⁿ⁾ _k is calculated from the transition of the walking phase of the subject 1 and the transition of the observation pattern, but the weight w ⁽ⁿ⁾ _k may be calculated from only one of them. In the above embodiment, the above Equation 19 for calculating the weight w ⁽ⁿ⁾ _k is represented by the product of the second item related to the transition of the observation pattern and the third item related to the transition of the walking phase. It may be represented by the sum or difference between the item and the third item. In the above embodiment, the transition of the walking state is not limited to the transition of the walking phase and the transition of the observation pattern, and may be a transition of the walking state based on the positions of other legs.

Although a plurality of particles are resampled by the resampling unit 27g in the above embodiment, the resampling unit 27g may be omitted in some cases. In the above embodiment, the movement trajectory of the leg F is obtained from the positions and velocities of both legs of the particle with the largest weight w ⁽ⁿ⁾ _k , but the present invention is not limited to this. For example, the movement trajectory may be acquired from any of the particles whose weight w ⁽ⁿ⁾ _k is equal to or greater than a predetermined value. In short, it suffices to select a particle of any correspondence based on the weight w ⁽ⁿ⁾ _k and acquire the movement trajectory from the selected particle.

In the above embodiment, the gait measurement system 100 is applied to measure gait characteristics in the TUG test, but is not limited to this, for example MTST (Multi-target stepping test) and measurement of gait characteristics in various other walking exercises The walking measurement system 100 can be applied to

In the above embodiment, the shape, size, center position, etc. of the first gate GA1 and the second gate GA2 are not particularly limited. Various shapes, sizes and center positions may be employed as the shape, size and center position of the first gate GA1 and the second gate GA2 depending on various factors. Although the size of the first gate GA1 is variable in the above embodiment, the size of the first gate GA1 may be fixed. Although the size of the second gate GA2 is fixed in the above embodiment, the size of the second gate GA2 may be variable.

For example, when the gait measurement system 100 is applied to measure gait characteristics in MTST, the shape of the second gate GA2 may be a perfect circle in view of the characteristics of MTST. Further, for example, the shape of the second gate GA2 may be elongated in the direction in which the subject 1 moves. In this case, when the subject 1 turns the marker 3, the length of the second gate GA2 The shaku direction changes to rotate 180 degrees according to the turn. Also, for example, at least one of the shape, size and center position of the second gate GA2 may be different for each phase of the walking test.

In the above embodiment, the second gate GA2 is set to expand based on the predicted positions of both legs according to the observed value _yk at the immediately preceding processing time, but is not limited to the observed value _yk at the immediately preceding processing time. A second gate GA2 that expands according to the observed value _yk of is set. In the above embodiment, the movement trajectory of the leg F of the subject 1 was acquired by the data analysis unit 24 using the particle filter, but without using the particle filter (that is, without performing association using the particle filter) ) The locus of movement of the leg F of the subject 1 may be obtained.

In the above embodiment, the laser range sensor 10 is used as the distance information acquisition unit, but various sensors and devices can be used as long as they can acquire distance information at a position at a predetermined height from the floor surface. For example, an infrared sensor or the like capable of acquiring the two-dimensional plane distance information described above may be used as the distance information acquisition unit. Although one laser range sensor 10 is provided in the above embodiment, a plurality of laser range sensors may be provided. Although the pole 40 is used as the reference member in the above embodiment, the present invention is not limited to this. As the reference member, various members may be used as long as they can align the laser range sensor 10 with the marker 3 .

In the above embodiment, the laser range sensor 10 is arranged so that the laser beam L is emitted from the rear side of the subject 1 approaching the marker 3 (moving away from the chair 2). The laser range sensor 10 may be arranged so that the light L is emitted. In some cases, the laser range sensor 10 may be arranged so that the laser beam L is emitted from the lateral side of the subject 1 approaching the marker 3 . In the above embodiment, the laser range sensor 10 is arranged under the chair 2, but the arrangement position of the laser range sensor 10 is not limited. Just do it.

In the above-described embodiment, whether the subject 1 stands up from the chair 2 (starts to move) or sits down on the chair 2 is determined by the pressure information of the pressure sensor 15. , the standing up and the sitting may be determined based on the detection result of the laser range sensor 10 . In the above embodiment, standing up and sitting down of the subject 1 are detected based on the pressure information, but only standing up may be detected based on the pressure information, or only sitting down may be detected based on the pressure information.

The present invention can be regarded as a walking measurement method implemented by the walking measurement system 100 described above. The present invention can be regarded as a walking measurement program that causes a computer to execute the walking measuring method (each process of the walking measuring system 100).

Various materials and shapes can be applied to each configuration in the above embodiments and modifications without being limited to the materials and shapes described above. Each configuration in the above embodiment or modification can be arbitrarily applied to each configuration in another embodiment or modification. A part of each configuration in the above embodiment or modification can be omitted as appropriate without departing from the gist of one aspect of the present invention.

Reference Signs List 1 test subject (subject), 10 laser range sensor (distance information acquisition unit), 24 data analysis unit, 26 leg detection unit (observed value detection unit), 27c predicted position calculation unit, 27d correlation processing unit , 27e... update processing unit, 27f... weight calculation unit, 27g... resampling unit, 100... walking measurement system, F... leg, GA1... first gate, GA2... second gate, L... laser light (detection wave) .

Claims (8)

A walking measurement system for tracking the leg positions of a walking subject,
a distance information acquisition unit that emits detection waves so as to scan in the horizontal direction and acquires distance information over time regarding the distance to an object that reflected the detection waves based on the reflection state of the detection waves;
a data analysis unit that acquires a movement trajectory of the subject's leg based at least on the distance information acquired by the distance information acquisition unit;
The data analysis unit
a leg detection unit that detects one or more observation values that are position candidates of the leg based on the distance information;
a predicted position calculation unit that calculates predicted positions of both legs of the subject based on at least the observed values at past times;
a correlation processing unit that associates the observed values with the predicted positions of the legs,
The correlation processing unit
setting a first gate that expands based on the predicted positions of the legs, and setting a second gate that expands based on the predicted positions of the legs according to the observed value at a past time;
A walking measurement system, wherein the predicted positions of the legs are associated with the observed values present in an area where the first gate and the second gate overlap. When both the observed value associated with the right leg of the subject and the observed value associated with the left leg of the subject exist at the immediately preceding processing time, the correlation processing unit 2. The walking measurement system according to claim 1, wherein said second gate is set so as to spread around a position between said predicted position of said right leg and said predicted position of said left leg. When there is only the observed value associated with one of the legs at the immediately preceding processing time, the correlation processing unit performs 3. The walking measurement system according to claim 1, wherein said widening second gate is set. If the correlation processing unit does not have both the observed value associated with the right leg of the subject and the observed value associated with the left leg of the subject at the immediately preceding processing time, 4. The walking measurement system according to any one of claims 1 to 3, wherein the second gate is set so as to spread around a position between the predicted position of the right leg and the predicted position of the left leg. The walking measurement system according to any one of claims 1 to 4, wherein said second gate is an ellipse having a major axis to minor axis ratio of 8:5. A system for use in a gait test of said subject, comprising:
The walking test is a test in which the subject is seated on a chair, walks toward a marker placed away from the chair, turns the marker, then returns to the chair and sits down again. can be,
The walking measurement system according to any one of claims 1 to 5, wherein the second gate has a shape elongated in a direction from the chair toward the marker. The first gate includes a gate that expands based on the predicted position of the subject's right leg and a gate that expands based on the predicted position of the subject's left leg. 1. The walking measurement system according to 1. the range of the first gate is variable;
The walking measurement system according to any one of claims 1 to 7, wherein the range of said second gate is fixed.

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