WO2012036135A1 - Information-processing method, information-processing device, output device, information-processing system, information-processing program and computer-readable recording medium on which same program is recorded - Google Patents

Abstract

Synchronism of body motion rhythms is continuously evaluated. The method comprises: a first calculation process for calculating the time at which a phase has changed by a predetermined angle from the phase of a signal at a predetermined time, and calculating the time at which the phase has changed by twice the predetermined angle from the phase of the signal at the predetermined time; and a second calculation process for calculating an evaluation coefficient on the basis of those times.

Description

INFORMATION PROCESSING METHOD, INFORMATION PROCESSING DEVICE, OUTPUT DEVICE, INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING PROGRAM, AND COMPUTER-READABLE RECORDING MEDIUM CONTAINING THE PROGRAM

The present invention relates to an information processing method, an information processing device, an output device, an information processing system, an information processing program, and a computer-readable recording medium on which the program is recorded.

• Continuously monitoring body movements in daily life is extremely important from the viewpoint of health management. In addition, knowing how the regular body movement rhythm such as walking or running of a patient is affected by trauma or cranial nervous system disease is very useful for accurately grasping the pathological condition. For example, a patient with Parkinson's disease has a time when symptoms are alleviated (hereinafter sometimes simply referred to as “on”) and a time when symptoms are aggravated (hereinafter sometimes simply referred to as “off”). When off, there are many sudden progress or freezing legs.

Empirically, it is empirically known that the balance of left and right foot movements is lost during abnormal walking such as sudden progress or freezing. For this reason, if the synchronization of the right and left footsteps (walking) can be monitored in real time, it is possible to take an appropriate measure such as preventing falls by detecting the sign of abnormal walking. In addition, there is a large left-right difference in the walking step interval when a patient with a disorder on one leg walks. Therefore, if the left-right difference in the step interval can be quantified, it will lead to an appropriate understanding of the effects of rehabilitation or treatment. Here, the synchrony is an index indicating whether or not the ratio of the time from the right foot landing to the left foot landing and the time from the left foot landing to the right foot landing maintains a constant value (for example, 1) during walking. . That is, if the ratio of the time from the right foot landing to the left foot landing and the time from the left foot landing to the right foot landing is maintained at a constant value (1), it is judged that the synchrony is high, and the time from the right foot landing to the left foot landing is If the ratio from the left foot landing to the right foot landing does not maintain a constant value (1), it is determined that the synchrony is low.

Regarding the evaluation of the synchrony of the left and right foot movements (steps) of Parkinson's disease patients, it is known to evaluate the synchrony of the left and right foot movements of Parkinson's disease patients using a plurality of foot switch type sensors.
For example, the synchronism of walking is determined by determining the ratio of the time required for the rhythm representing walking to change by one cycle to the time required for the rhythm representing walking to change for the next one cycle. Conventionally, this ratio has been obtained from the distance between the feature points of the heel-off or toe-off.

However, in the conventional evaluation of the synchrony of the left and right foot movements of Parkinson's disease patients, since statistical calculation processing is performed on the accumulated data, it is not suitable for continuous processing or real-time processing.
Further, in the conventional evaluation of the synchrony of the left and right foot movements of Parkinson's disease patients, the synchrony is evaluated using the interval between the feature points. For this reason, when the peak of a waveform is used as a feature point, a large error occurs if the above ratio is calculated from a waveform that does not have a clear peak, such as a split of the peak. That is, the evaluation of synchrony using the interval between feature points is not suitable for a waveform having no clear peak. Further, when the feature point interval is used, the ratio can be calculated only for each time corresponding to the peak position of the waveform. That is, the evaluation of synchrony using the interval between feature points is not suitable for continuous processing or real-time processing.

The present invention has been made in view of such problems, and an object thereof is to continuously evaluate the synchrony of body movement rhythm.
In addition, the present invention is not limited to the above-described object, and other effects of the present invention can be achieved by the functions and effects derived from the respective configurations shown in the embodiments for carrying out the invention which will be described later. Can be positioned as one of

As a result of intensive research by the present inventors, it has been found that by focusing on the phase of the signal, it is possible to continuously evaluate the synchronization of the movement rhythm at an arbitrary time interval with the signal measurement time interval as the lower limit.
That is, the gist of the present invention is that a signal representing a repetitive voluntary movement of a living body, a time when the phase of the signal changes by a predetermined angle from a phase of the signal at a predetermined time (hereinafter referred to as a first time), and a predetermined A time calculating process for calculating a time (hereinafter referred to as a second time) when the phase of the signal has changed twice the predetermined angle from the phase of the signal at the time, and the first time calculated by the time calculating process And an evaluation coefficient calculation step of calculating an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the second time.

Further, the gist of the present invention is that a signal representing a repetitive voluntary movement of a living body, a time when the phase of the signal changes by a predetermined angle from a phase of the signal at a predetermined time (hereinafter referred to as a first time), and a predetermined A time calculating unit that calculates a time when the phase of the signal changes twice the predetermined angle from the phase of the signal at a time (hereinafter referred to as a second time), and the first time calculated by the time calculating unit. And an evaluation coefficient calculation unit that calculates an evaluation coefficient for evaluating the synchronization of the repetitive voluntary movement based on the second time.

Furthermore, the gist of the present invention is that the phase of the signal changes by a predetermined angle from the phase of the signal representing the repetitive voluntary movement of the living body at the predetermined time, and the phase of the signal from the phase of the signal at the predetermined time. An output device including an output unit that outputs an evaluation coefficient for evaluating the synchronization of the repeated voluntary movement based on the time when the predetermined angle has been changed twice in a form in which the evaluation coefficient can be identified.
Further, the gist of the present invention is that a signal representing a repetitive voluntary movement of a living body, a time when the phase of the signal changes by a predetermined angle from a phase of the signal at a predetermined time (hereinafter referred to as a first time), and a predetermined A time calculating unit that calculates a time when the phase of the signal changes twice the predetermined angle from the phase of the signal at a time (hereinafter referred to as a second time), and the first time calculated by the time calculating unit. And an evaluation coefficient calculating unit that calculates an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the second time, and the evaluation coefficient calculated by the evaluation coefficient calculating unit is identified as the evaluation coefficient An information processing system having an output unit that outputs in a possible form.

Further, the gist of the present invention is that a signal representing a repetitive voluntary movement of a living body, a time when the phase of the signal changes by a predetermined angle from a phase of the signal at a predetermined time (hereinafter referred to as a first time), and a predetermined A time calculating process for calculating a time (hereinafter referred to as a second time) when the phase of the signal has changed twice the predetermined angle from the phase of the signal at the time, and the first time calculated by the time calculating process And an evaluation coefficient calculation process for calculating an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the second time.

Further, the gist of the present invention is that a signal representing a repetitive voluntary movement of a living body, a time when the phase of the signal changes by a predetermined angle from a phase of the signal at a predetermined time (hereinafter referred to as a first time), and a predetermined A time calculating process for calculating a time (hereinafter referred to as a second time) when the phase of the signal has changed twice the predetermined angle from the phase of the signal at the time, and the first time calculated by the time calculating process And a computer-readable recording medium recording an information processing program for causing a computer to execute an evaluation coefficient calculation process for calculating an evaluation coefficient for evaluating the synchronization of the repetitive voluntary movement based on the second time Exist.

It becomes possible to evaluate the synchrony of exercise rhythm continuously and in real time.

FIG. 1 is a diagram illustrating a configuration of a system as an example of an embodiment. FIG. 2 is a diagram illustrating an exercise rhythm as an example of the embodiment. FIG. 3 is a diagram for explaining the operation of the determination unit as an example of the embodiment. FIG. 4 is a flowchart for explaining the operation of the system as an example of the embodiment. 5 (A) and 5 (B) are diagrams showing body motion signals and exercise rhythms as an example of the embodiment, and FIG. 5 (A) shows a body motion signal detection device at the center of the abdomen of a healthy subject. FIG. 5B shows an example of an exercise rhythm extracted from the body motion signal. FIG. 5B shows a part of the 15-minute walking acceleration signal measured by the body motion signal detection device at 100 Hz sampling. FIG. FIG. 6 is a flowchart for explaining the operation of the time constant determination unit as an example of the embodiment. FIG. 7 is a diagram illustrating a change in dispersion with respect to a change in time constant as an example of an embodiment. FIG. 8 is a flowchart for explaining the operation of the rhythm extraction unit as an example of the embodiment. FIG. 9 is a flowchart for explaining the operation of the evaluation coefficient determination unit as an example of the embodiment. FIGS. 10A to 10C are diagrams for explaining a pattern matching method as an example of the embodiment. FIG. 10A shows a part of the waveform from the data of FIG. FIG. 10 (B) shows the result of calculating the autocorrelation coefficient by selecting a reference wave with a width of 0.4 seconds around the time indicated by * in FIG. 10 (A). FIG. 10C shows a result of calculating an autocorrelation coefficient by selecting a reference wave having a width of 0.4 seconds around the time indicated by a circle in FIG. 10A. FIG. 11 is a diagram illustrating evaluation coefficients as an example of the embodiment. FIG. 12 is a flowchart for explaining the operation of the determination unit as an example of the embodiment. FIG. 13 is a flowchart for explaining the operation of the determination unit as an example of the embodiment. FIG. 14 is a diagram illustrating evaluation coefficients as an example of the embodiment. FIG. 15 is a diagram illustrating an exercise rhythm as an example of the embodiment. FIGS. 16A to 16C are diagrams for explaining the evaluation coefficient obtained by the system as an example of the embodiment. FIG. 16A is detected by the body motion signal detection device 10. FIG. 16B is a diagram showing an acceleration signal after second order integration, that is, a motion trajectory, and FIG. 16C is an evaluation obtained from the acceleration signal after second order integration. It is a figure which shows a coefficient. FIG. 17A to FIG. 17C are diagrams for explaining evaluation coefficients obtained by the system as an example of the embodiment. FIG. 17A is detected by the body motion signal detection device 10. FIG. 17B is a diagram showing an acceleration signal after second-order integration, that is, a motion trajectory, and FIG. 17C is an evaluation obtained from the acceleration signal after second-order integration. It is a figure which shows a coefficient. 18A to 18C are diagrams for explaining the evaluation coefficient obtained by the system as an example of the embodiment. FIG. 18A is detected by the body motion signal detection device 10. 18 (B) is a diagram showing the acceleration signal and angular velocity signal after the second integration, and FIG. 18 (C) is a graph showing the acceleration signal and angular velocity after the second integration. It is a figure which shows the evaluation coefficient each calculated | required. FIG. 19A is a diagram showing the time change of the walking index after the smoothing process is performed, and FIG. 19B is a diagram showing the time change of the body motion index after the smoothing process is performed. It is.

Hereinafter, embodiments of the information processing method will be described with reference to the drawings.
[A] Description of Embodiment FIG. 1 is a diagram illustrating a configuration of a system according to an example of an embodiment. The system 1 includes a body motion signal detection device 10 and an information processing device 20. The body motion signal detection device 10 is communicably connected to the information processing device 20 via, for example, wired such as the Internet or wireless such as a wireless LAN (Local Area Network) or Bluetooth (registered trademark).

The body motion signal detection device 10 detects (measures) a repetitive rhythmic motion accompanying a repetitive voluntary movement of a subject (living body) (hereinafter sometimes simply referred to as a voluntary movement) noninvasively and continuously, for example. . Hereinafter, the repeated rhythmic movement accompanying the voluntary movement may be simply referred to as rhythmic movement.
Here, the voluntary exercise is, for example, walking, jogging, running, cycling, swimming, gymnastics, weight training, physical strength measurement (stepping up / down, repetitive side jumping), juggling (beading a beanbag, lifting a soccer ball) and the like. In addition, the repetitive rhythmic movement accompanying the voluntary movement includes, for example, the rhythmic movement of the walking itself when the voluntary movement is walking.

In addition, when the voluntary movement is a movement that is repeated regularly, the rhythm of the voluntary movement itself can be extracted more accurately. In addition, the voluntary exercise | movement repeated regularly includes not only that the completely same exercise | movement is repeated, but repeating substantially the same exercise | movement.
Non-invasive means, for example, that the body of the subject is not damaged or that the subject is not burdened.

The body motion signal detection device 10 is configured to be portable, for example. In addition, if the attachment position to the test subject of the body motion signal detection apparatus 10 is a site | part which can detect a body movement, there will be no restriction | limiting in particular.
The body motion signal detection device 10 includes, for example, a body motion signal detection unit 11, a storage unit 12, and an interface unit 13. The body motion signal detection unit 11, the storage unit 12, and the interface unit 13 are connected to be communicable with each other.

The body motion signal detection unit 11 detects (measures), for example, a repetitive rhythm motion associated with a voluntary motion as a body motion signal (a signal based on a repetitive rhythm motion associated with a voluntary motion of a living body). That is, the body motion signal detection unit 11 detects a signal based on a repetitive rhythm movement accompanying a voluntary movement of a living body as a body motion signal. From a different point of view, one body motion signal detection unit 11 detects, for example, a repetitive rhythm motion accompanying a voluntary motion of a living body as a body motion signal. Specifically, the subject's rhythmic movements, for example, force changes, spatial body position changes, sounds emitted from the body, waves such as electromagnetic waves or fine energy changes, or field changes around the body, etc. Detect (measure) as a body motion signal. That is, the body motion signal detection unit 11 measures a signal based on repetitive rhythm movement accompanying voluntary movement.

Here, the body motion signal detection unit 11 is realized by an inertial sensor such as an acceleration sensor, a speed sensor, or a gyro sensor, for example. About the inertial sensor which detects the said body motion signal, it selects suitably according to the kind of signal to detect, for example. Usually, when detecting a walking rhythm, an acceleration sensor that measures acceleration of body movement is preferably used, but is not limited to an acceleration sensor.

As the acceleration sensor, a single-axis to three-axis sensor can be arbitrarily used, but the three axes for detecting acceleration acting in three directions of the vertical direction, the horizontal front-rear direction, and the horizontal left-right direction during walking. Although an acceleration sensor is preferably used, it is not limited to a triaxial acceleration sensor.
Note that the body motion signal detection unit 11 measures the body motion signal at a predetermined sampling frequency (for example, 100 Hz), for example.

The storage unit 12 is a storage device capable of storing various information such as RAM (Random Access Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, and the like. Specifically, the storage unit 12 stores the body motion signal obtained by the body motion signal detection unit 11, for example. Moreover, the memory | storage part 12 may be provided detachably with respect to the body motion signal detection apparatus 10, for example, and may be fixed to the body motion signal detection apparatus 10. FIG.

The interface unit 13 is an interface connected to the information processing apparatus 20 so as to be communicable, for example. When the body motion signal detection device 10 is connected to the information processing device 20 via wireless, for example, the interface unit 13 includes an antenna. In addition, when the body motion signal detection device 10 is connected to the information processing device 20 via a wire, for example, the interface unit 13 is a connection terminal that can be connected to a wire. The storage unit 12 is detachably provided, for example, in a slot provided in the body motion signal detection device 10, and the storage unit 12 is detached from the body motion signal detection device 10 and connected to the information processing device 20. In this case, the interface unit 13 may not be provided in the body motion signal detection device 10.
The information processing apparatus 20 is, for example, a PC (Personal Computer), and calculates an evaluation coefficient to be described later from the body motion signal obtained by the body motion signal detection apparatus 10.

The information processing apparatus 20 includes, for example, a central processing unit 21, a storage unit 22, an output unit 23, and an interface unit 24. Here, the central processing unit 21, the storage unit 22, the output unit 23, and the interface unit 24 are connected to be communicable with each other.
The storage unit 22 is a storage device that can store application programs and data, such as RAM, HDD, and SSD, and stores various types of information.

The central processing unit 21 is a processing device that performs various calculations or controls by executing various application programs stored in the storage unit 22, thereby realizing various functions.
For example, the central processing unit 21 executes an information processing program stored in the storage unit 22 to thereby execute a time constant determination unit 211, a rhythm extraction unit 212, an evaluation coefficient determination unit 213, a determination unit 214, and an output control unit 215. Function as. That is, the information processing program is a program that causes the central processing unit 21 to function as the time constant determination unit 211, the rhythm extraction unit 212, the evaluation coefficient determination unit 213, the determination unit 214, and the output control unit 215.

The time constant determination unit 211 acquires, for example, the body motion signal obtained by the body motion signal detection unit 11 from the storage unit 12, and based on the acquired body motion signal, a signal (hereinafter, referred to as a rhythm of voluntary exercise itself). The time constant used to extract the motion rhythm is sometimes calculated. In other words, the movement rhythm corresponds to a signal representing repeated voluntary movement of the living body. In addition, since the body motion signal including the motion rhythm is a signal detected by one body motion signal detection unit 11, a signal representing repeated voluntary movement of the living body is generated by one body motion signal detection unit 11 (detection unit). It is a detected signal.

Specifically, the time constant determination unit 211 determines the time constant by performing the following processing.
(1) The time constant determining unit 211 applies the body motion signal X to a high-pass filter or a band-pass filter characterized by a certain time constant A. Here, for example, as a high-pass filter, when the process of smoothing the body motion signal X with a zero phase moving average filter having a time width (time constant) A is described as F (X, A), the output waveform (signal) The process expressed by Y = XF (X, A) is performed. Further, for example, as a bandpass filter, processing represented by Y = F (XF (X, A), A / 2.5) is performed. Here, the zero phase moving average filter refers to a moving average filter whose phase shift is zero. The zero phase moving average filter can be realized by using various known methods, and detailed description thereof is omitted. An example of the time constant of the bandpass filter is A / 2.5, but the present invention is not limited to this.

(2) Next, the time constant determination unit 211 quantifies the regularity of the output waveform Y. The time constant determination unit 211 obtains, for example, CV (= standard deviation / average value: CoefficientCoofariVariation) of the absolute value of the value at the maximum and minimum peaks of the output waveform Y. More specifically, the time constant determination unit 211 extracts the maximum and minimum peaks from the output waveform Y, obtains the absolute value of the amplitude of the waveform at the peak position, and calculates the standard deviation and the average from the obtained waveform amplitude. Find the value. Then, the time constant determination unit 211 calculates the variance, that is, CV by dividing the standard deviation by the average value.

(3) Furthermore, the time constant determination unit 211 obtains regularity of the output waveform Y when the time constant A is changed. The time constant determination unit 211 graphs the regularity of the output waveform Y when the time constant T is changed, for example. Specifically, the time constant determination unit 211 calculates a change in the regularity of the output waveform Y with respect to the time constant A by, for example, calculating the CV at each time constant A when the time constant A is changed. .
In addition, although the example using the absolute value CV at the peak of the output waveform Y has been described as the quantification of the regularity of the output waveform Y in the time constant determination unit 211, it is not limited to this example.
(4) The time constant determination unit 211 obtains a minimum point from the change in regularity (change in CV) of the output waveform Y with respect to the time constant A obtained in the process of (3) above. Then, the time constant determination unit 211 separates the motion rhythm from the body motion signal, for example, the time constant A corresponding to the minimum point with the smaller time constant A among the obtained minimum points (for example, two minimum points). Thus, it is determined as a time constant for extraction (hereinafter sometimes referred to as a first time constant). That is, the time constant determination unit 211 selects a time constant that enables separation and extraction of a waveform having regularity as much as possible. In other words, the time constant determination unit 211 selects a plurality of minimum points (for example, two points) and determines the time constant corresponding to the minimum point with the smaller time constant A as the first time constant. That is, the time constant determination unit 211 determines the first time constant based on the output when the band pass filter is applied to the body motion signal.

In addition, it is preferable from the viewpoint of noise removal that the process of (1) to (4) includes the process of performing N-order integration (N = 1, 2, 3,...) Of the body motion signal. When performing N-order integration, it is preferable to perform the process (1) (filter process) N times or more. The order of the integration operation and the filtering process may be any order as long as the following conditions are satisfied, for example.
・ Do not perform the filter process more than once in succession.
・ At least once in the filter process, the integration process is completed.
-When the filter process is performed twice or more, the filter is a high-pass filter except for the last one.

For example, the rhythm extraction unit 212 acquires the body motion signal obtained by the body motion signal detection unit 11 from the storage unit 12, and performs filtering using the first time constant (A1) determined by the time constant determination unit 211. By processing, the movement rhythm is extracted from the body movement signal.
Specifically, the rhythm extraction unit 212 extracts an exercise rhythm from the body motion signal by performing the following process (filtering process), for example. In addition, the following process is an illustration and is not limited to this example.

(5) For example, the rhythm extraction unit 212 obtains a low-frequency component of the body motion signal by applying a zero phase moving average filter having a first time constant to the body motion signal. That is, the rhythm extraction unit 212 performs processing represented by F (X, A1) on the body motion signal. Next, the rhythm extraction unit 212 subtracts the low-frequency component obtained by performing the processing represented by F (X, A1) from the body motion signal. In other words, the rhythm extraction unit 212 calculates Y = X−F (X, A1). In other words, the rhythm extraction unit 212 performs high-pass filtering on the body motion signal using the first time constant.

Note that this high-pass filtering makes it possible to prevent divergence, for example, in an integration process described later.
(6) Next, the rhythm extraction unit 212 performs integration on the signal after the high-pass filtering. For example, the rhythm extraction unit 212 performs second-order integration on the signal after the high-pass filtering.

(7) The rhythm extraction unit 212 performs the same processing as (10) on the signal X2 after integration in the processing (11). That is, the rhythm extraction unit 212 performs processing represented by F (X2, A1) on the integrated body motion signal. In other words, the rhythm extraction unit 212 performs high-pass filtering on the integrated signal that is the signal after integration, using the first time constant.
(8) Furthermore, the rhythm extraction unit 212 creates an envelope connecting the maximum values of the signal obtained by the processing of (12) and an envelope connecting the minimum values, and the two envelopes A process of subtracting the average signal from the signal obtained in the process (12) is performed. Since the amplitude of the signal obtained by this processing changes with the amplitude value 0 interposed therebetween, the phase can be accurately obtained when the Hilbert transform method described later is used. This process may be omitted.

Through the processes (5) to (8), the rhythm extraction unit 212 extracts an exercise rhythm from the body motion signal. That is, the rhythm extraction unit 212 functions as a rhythm extraction unit that extracts a rhythm (voluntary rhythm) of voluntary exercise from the body motion signal by performing filtering processing on the body motion signal. Therefore, for example, when evaluating the synchrony of the walking rhythm from the body motion signal, the walking area may be extracted in advance, or such preprocessing may not be performed.
In the process of (6), the body motion signal is second-order integrated. However, the integration is not limited to the integration process, and the integration process may not be performed. Further, the present invention is not limited to the second order integration, and Nth order integration may be performed (N = 1, 2, 3,...). In addition, when performing N-order integration, it is preferable to perform the process (filter process) of (5) [or (7)] N times or more. The order of the integration operation and the filtering process may be any order as long as the following conditions are satisfied, for example.
・ Do not perform the filter process more than once in succession.
・ At least once in the filter process, the integration process is completed.
-When the filter process is performed twice or more, the filter is a high-pass filter except for the last one.

The evaluation coefficient determination unit 213 determines an evaluation coefficient for evaluating the synchronization of the motor rhythm based on, for example, the phase of the motor rhythm. Here, the phase is an index representing how many rotation angles each point on the rhythm waveform corresponds to, assuming that a rhythm that repeats in time is a movement rotating on the circumference. For example, there is a difference of 360 degrees between the phases of two adjacent peak points in a sine wave. FIG. 2 is a diagram illustrating an example of an exercise rhythm, and the peak in the figure corresponds to, for example, the landing of the subject's right foot or the left foot. Here, an example of the evaluation coefficient is, for example, the peak corresponding to one foot and the peak corresponding to the other foot between the peaks corresponding to one foot (for example, the right foot) in the example of the exercise rhythm shown in FIG. Are obtained by the following equation (1), where the time intervals are as the first time T1 and the second time T2, respectively. Note that the evaluation coefficient for evaluating the synchrony of the exercise rhythm is not limited to this evaluation coefficient. For example, in the following formula (1), the value of the evaluation coefficient when the first time T1 and the second time T2 are equal is 0, but is not limited to 0. For example, an evaluation coefficient calculation formula may be used so that the value of the evaluation coefficient when the first time T1 and the second time T2 are equal is a value other than 0 such as 1 or the like. That is, the calculation formula for the evaluation coefficient is not limited to the following formula (1). In addition, the following formula (1) indicates that the difference between the time from the landing of one foot to the landing of the other foot and the time from the landing of the other foot to the landing of one foot, and the landing of one foot Represents the ratio of the time from the landing of the other foot to the sum of the time from the landing of the other foot to the landing of the one foot.

In FIG. 2, the time intervals between the peak corresponding to one foot and the peak corresponding to the other foot between the peaks corresponding to one foot (for example, the right foot) are respectively the first time T1 and the second time. Although it was set as T2, it is not limited to the time interval between peaks. For example, the first time T1 may be a time until the phase changes by 360 degrees from a predetermined position that does not correspond to the peak of the movement rhythm. Further, for example, the second time T2 may be a time from a position where the phase has changed 360 degrees from a predetermined position not corresponding to the peak of the movement rhythm to a further change in phase by 360 degrees.

The first time T1 is the time until the phase changes 360 degrees from the peak of the movement rhythm or a predetermined position not corresponding to the peak, and the second time T2 does not correspond to the peak or peak of the movement rhythm. Although it is the time from the position where the phase has changed 360 degrees from the predetermined position until the phase has changed 360 degrees, the present invention is not limited to this. For example, the first time T1 is a time until the phase changes by -360 degrees from a predetermined position that does not correspond to the peak of the movement rhythm or a peak (a time that goes back 360 degrees from the predetermined position), and the second time T2 Is the time from when the phase changes by -360 degrees from a predetermined position that does not correspond to the peak of the motor rhythm or when the phase changes by -360 degrees (time that goes back from the predetermined position by 720 degrees). May be. Further, for example, the first time T1 is a time until the phase changes by 360 degrees from a predetermined position not corresponding to the peak of the movement rhythm or the peak, and the second time T2 is a peak or peak of the movement rhythm. It may be a time until the phase changes by -360 degrees from a predetermined position that does not correspond (a time that is 360 degrees backward from the predetermined position).

By the way, when the first time T1 is set as a time that goes back 360 degrees from the predetermined position, or when the second time T2 is set as a time that goes back 360 degrees or 720 degrees from the predetermined position, the first time T1 and The value of the second time T2 may be negative. In such a case, it is necessary to pay attention to the first time T1 and the second time T2 that are substituted into Equation 1.

The evaluation coefficient determination unit 213 includes a time calculation unit 223 and an evaluation coefficient calculation unit 233.
For example, the time calculation unit 223 calculates the movement rhythm phase, thereby changing the phase from the movement rhythm phase at a predetermined time (arbitrary time) by a predetermined angle (hereinafter sometimes referred to as a first angle). (First time) and a time (second time) at which the phase changes twice the first angle from the phase of the motion rhythm at a predetermined time.

The time calculation unit 223 can calculate, for example, a time from a predetermined time to the first time (first time: T1). In addition, the time calculation unit 223 can calculate, for example, a time from the first time to the second time (second time: T2).
Here, the first angle is, for example, a positive value of 180 degrees or 360 degrees. In the present application, 360 degrees includes a case of strictly 360 degrees and a case of approximately 360 degrees. Similarly, 180 degrees includes a case of strictly 180 degrees and a case of approximately 180 degrees. Including.

The time calculation unit 223 calculates, for example, the phase of the movement rhythm, for example, the time that the phase goes back the first angle from the phase of the movement rhythm at a predetermined time (arbitrary time) And the time when the phase has changed twice the first angle from the phase of the movement rhythm at the predetermined time (the time that is double the first angle is also included in the second time) May be calculated. That is, the first angle may be a negative value of −180 degrees or −360 degrees, for example. In this case, the time calculation unit 223 can calculate the time from the first time to the predetermined time (first time: T1). In addition, the time calculation unit 223 can calculate the time from the second time to a predetermined time (second time: T2).

Further, the time calculation unit 223 has a phase (first time) when the phase changes from the phase of the motion rhythm at a predetermined time (arbitrary time) and a phase from the phase of the motion rhythm at the predetermined time. You may make it calculate the time which changed -1 time of the angle. In this case, the time calculation unit 223 can calculate the time from the predetermined time to the first time (first time: T1). In addition, the time calculation unit 223 can calculate the time from the second time to a predetermined time (second time: T2).
Hereinafter, a case where the evaluation coefficient calculation unit 233 calculates the first time (the first time is larger than the predetermined time) and the second time (the second time is larger than the first time) will be described. . When calculating the first time (the first time is smaller than the predetermined time) and the second time (the second time is smaller than the first time), and the first time (the first time is smaller than the predetermined time) The calculation method is the same in the case of calculating the (large) and second time (the second time is smaller than the predetermined time).

The time calculation unit 223 calculates the phase of the motion rhythm using, for example, the Hilbert transform method or the pattern matching method. Note that the method for calculating the phase is not limited to the Hilbert transform method or the pattern matching method.
Here, the Hilbert transform method is a method for specifically calculating the phase at a predetermined position of the waveform, and the pattern matching method is not a method for directly specifying the phase, but is a method for finding points where the phases are mutually shifted by 360 degrees. is there. That is, the pattern matching method is a method of finding a point whose phase is shifted by an integral multiple of 360 degrees from a predetermined position of the waveform. In other words, the pattern matching method calculates a relative phase (for example, 360 degrees) with respect to a predetermined position of the waveform.

The evaluation coefficient calculation unit 233 calculates the evaluation coefficient based on the first time and the second time calculated by the time calculation unit 223, for example. For example, the evaluation coefficient calculation unit 233 calculates the first time and the second time based on the first time and the second time calculated by the time calculation unit 223, and sets the first time and the second time to the above (1). An evaluation coefficient is calculated by substituting it into the equation. That is, the evaluation coefficient calculation unit 233 calculates an evaluation coefficient for evaluating the synchrony of repeated voluntary movement based on the first time and the second time.

First, a case where the time calculation unit 223 calculates the phase change amount ratio using the Hilbert transform method will be described.
(A) When using the Hilbert transform method The Hilbert transform is a mathematical method for deriving a corresponding imaginary part Y (t) from an arbitrary real time series signal X (t). Thereby, the time calculation unit 223 can directly obtain the phase θ (t) of X (t) from the following equation (2). That is, the phase calculation unit 224 can obtain the phase of the motion rhythm at a predetermined time (predetermined position) from the following equation (2).

If the phase of the motion rhythm at an arbitrary time t is θG (t) degrees, the time calculation unit 223 obtains the phase of the motion rhythm from the above equation (2), for example, θG (t1) = θG (t) Find a time t1 such that +360. Further, the time calculation unit 223 obtains a time t2 such that, for example, θG) (t2) = θG (t) +720.

And the evaluation coefficient calculation part 233 calculates an evaluation coefficient from the following (3) formula, for example using the time t1 and the time t2 which were calculated by the time calculation part 223, for example.

Note that equation (3) is equivalent to equation (1). Note that the evaluation coefficient calculation unit 233 may calculate the evaluation coefficient from the equation (1) using the first time T1 and the second time T2 calculated based on the time t1 and the time t2.
Next, a case where the time calculation unit 223 calculates the phase change amount ratio using the pattern matching method will be described.
(B) When using the pattern matching method The pattern matching method is a method for quantifying the similarity between two signals. There are many methods for defining or calculating the degree of similarity. Specifically, for example, a method described in “Image processing engineering (Ryoichi Suematsu, Hirohisa Yamada, Corona)” or the like is used. The most representative is the autocorrelation coefficient. For example, the following calculation is performed for a three-dimensional body motion signal.

First, select an appropriate reference wave from body motion data. The coordinate of the reference wave is expressed by the following equation (4).

Suppose that the average value of each of p pieces of x, y, and z is zero. The autocorrelation coefficient is calculated by the following equation (5).

This is a so-called scalar quantity and does not depend on the coordinate system. That is, the same value is obtained even when the body motion signal detection device 10 causes a rotational shift at the wearing site during body motion measurement. The same calculation is performed for a one-dimensional signal.
By using the pattern matching method, it is possible to easily obtain the time t1 when the phase of the rhythm waveform at an arbitrary time t is shifted by 360 degrees, the time t2 when the phase is shifted by 720 degrees, or the like. In other words, by using pattern matching, the time calculation unit 223 can obtain a time whose phase is shifted by an integer multiple of 360 degrees from the phase of the motion rhythm at the time t.

Then, the evaluation coefficient calculation unit 233 calculates the evaluation coefficient from the above equation (3), for example, using the time t1 and the time t2 calculated by the time calculation unit 223, for example. Note that the evaluation coefficient calculation unit 233 may calculate the evaluation coefficient from the equation (1) using the first time T1 and the second time T2 calculated based on the time t1 and the time t2.

判定 Determining unit 214 evaluates (determines) exercise rhythm synchrony based on the evaluation coefficient obtained by evaluation coefficient determining unit 213, for example. That is, the determination unit 214 evaluates the synchronization or balance of the exercise rhythm based on the evaluation coefficient. Specifically, for example, when the voluntary movement is walking or the like, the determination unit 214 evaluates the synchronization of left and right footsteps, that is, the synchronization or balance of left and right footsteps. Here, whether the left and right footing is synchronized or not is determined based on whether the evaluation coefficient maintains a constant value regardless of time or whether the evaluation coefficient changes periodically. For example, the determination unit 214 determines that the left and right footsteps are synchronized when the evaluation coefficient maintains a constant value regardless of time or when the evaluation coefficient changes periodically. Further, whether the right / left footing balance is good or bad is determined based on whether or not the evaluation coefficient is close to 0 when the evaluation coefficient is determined using the equation (1). For example, the determination unit 214 determines that the left and right footing balance is better as the evaluation coefficient is closer to zero.

Specifically, the determination unit 214 calculates, for example, the following three indices R, S, and C from the change in the evaluation coefficient over a predetermined time. For example, when the voluntary movement is walking, the predetermined time is a time required for the subject to walk about 10 steps (for example, 5 seconds), but is not limited to this time.
(I) Evaluation coefficient range (value farthest from 0): 0 ± R
(Ii) Standard deviation (size of fluctuation) of evaluation coefficient: S
(Iii) Autocorrelation coefficient (periodicity of evaluation coefficient) when the time is shifted by the average walking period (T1 + T2 in FIG. 2 or FIG. 15 described later): C
The determination unit 214 evaluates synchrony based on these three indices R, S, and C. FIG. 3 is a diagram for explaining an example of the operation of the determination unit 214.

As shown in FIG. 3, for example, if R is 0.02 or less, the determination unit 214 has a very good balance between left and right footing (walking rhythm) and right and left footing (in FIG. 3). Judgment). That is, the determination unit 214 functions as a determination unit that determines whether the evaluation coefficient is within a predetermined range (for example, 0 ± 0.02) over a predetermined time (for example, 5 seconds).
Further, for example, when R is not less than 0.02 but S is not more than 0.01, the determination unit 214 has a good balance between right and left footsteps and a right and left footstep balance (FIG. 3). Judgment)

Further, for example, in the case where R is not 0.02 or less and S is not 0.01 or less, the determination unit 214 synchronizes left and right footsteps and right and left footsteps based on C. Assess balance.
For example, when C is 0.5 or more, the determination unit 214 determines that the left and right footsteps are synchronized, but the left and right footsteps are not well balanced (see x in FIG. 3), and C is 0. If it is smaller than .5 and greater than 0.2, it is determined that the left and right footsteps are normally synchronized (see Δ in FIG. 3), but the left and right footsteps are not well balanced (see × in FIG. 3). Furthermore, for example, when C is less than 0.2, the determination unit 214 determines that the left and right footsteps are not synchronized (see “X” in FIG. 3), and the right and left footstep balance cannot be determined. .

The numerical values (0.02, 0.01, 0.5, and 0.2) in FIG. 3 are examples, and are not limited to these numerical values. Further, in FIG. 3, five patterns are determined using three indexes R, S, and C, but the determination method is not limited to this, and finer determination may be performed. A rough evaluation may be performed. For example, two patterns may be determined based only on the value of R.
In addition to the above, the determination unit 214 can directly index the synchronization of left and right footsteps and the balance of left and right footsteps from the change in the evaluation coefficient over a predetermined time, for example, as follows. For example, when the voluntary movement is walking, the predetermined time is a time required for the subject to walk about 10 steps (for example, 5 seconds), but is not limited to this time.

Here, the evaluation coefficient calculated from the phase of the movement rhythm is denoted as H (t) as a function of time t. Let t2 be the time when the phase of the motion rhythm has changed by 720 degrees starting from an arbitrary time t1.
(I) Balance of left and right foot travel: S1
The standard deviation of H (t) between time t1 ≦ t ≦ time t2 is obtained. The time change of the standard deviation is calculated by sequentially changing the time t1 over a predetermined time. With respect to the time series data of the standard deviation thus obtained, an average value within a predetermined time is obtained and used as an index S1 for the balance of left and right foot travel. When the evaluation coefficient is calculated by the expression (3), this index becomes 0 if the balance of the left and right foot travel is perfect, and approaches 0.5 if the balance is lost. For adults who have no problems walking, this value is usually less than 0.02.
(Ii) Left and right footing synchronization: S2
Find the absolute value of H (t2) -H (t1). The time change of the absolute value is calculated by sequentially changing the time t1 over a predetermined time. Next, an envelope of time series data of absolute values obtained in this way is obtained. Specifically, for example, a Hilbert transform equation is used. That is, A (t) where the absolute value is X (t) in equation (2) corresponds to the envelope. An average value of envelopes within a predetermined time is obtained and used as an index S2 for synchronization of left and right foot travel. When the evaluation coefficient is calculated by equation (3), this index is 0 if the left and right footsteps are perfectly synchronized, and approaches 1 if the synchronization is lost. For adults who have no problems walking, this value is usually less than 0.02.

In addition, as the quantification of the regularity of the output waveform Y in the time constant determination unit 211 described above, the phase obtained by the time calculation unit 223 or the index obtained by the determination unit 214 can be used. For example, the CV of the phase period or the synchronization index S2 of the left and right footing is considered to reflect the regularity of the output waveform Y. When the time constant A of the filter is changed (for example, between 0 and 1 second in the case of walking rhythm), the CV or index S2 of the phase period is obtained and the value takes the minimum value. The constant A is determined as the optimal time constant.

The output control unit 215 controls the output unit 23. For example, when the output unit 23 includes a display unit of the display, the output control unit 215 controls the display state of the output unit 23 to display various information on the output unit 23. For example, the output control unit 215 causes the output unit 23 to display the evaluation coefficient obtained by the exercise rhythm extracted by the rhythm extraction unit 212 or the evaluation coefficient determination unit 213. The evaluation coefficient may be displayed as a graph on the output unit 23 or may be displayed as a message such as “Evaluation coefficient is **”. Further, for example, the output control unit 215 may cause the output unit 23 to display the determination result by the determination unit 214 in addition to the display of the evaluation coefficient or in place of the display of the evaluation coefficient.

Further, if the output unit 23 warns, for example, by an alarm or vibration, the output control unit when the determination unit 214 determines that the evaluation coefficient is not a value within a predetermined range, for example. 215 controls the output unit 23 to cause the output unit 23 to issue a warning.
When the output unit 23 is provided outside the information processing apparatus 20, the output control unit 215 is, for example, an evaluation coefficient obtained by the exercise rhythm or evaluation coefficient determination unit 213 extracted by the rhythm extraction unit 212. The display state of the output unit 23 is controlled by transmitting various information such as the above to the output unit 23 via wireless or wired communication.

The output unit 23 outputs various types of information under the control of the output control unit 215. For example, the output unit 23 is a display, and when the evaluation coefficient obtained by the evaluation coefficient determination unit 213 or the determination unit 214 determines that the evaluation coefficient is not a value within a predetermined range, for example. , Display a warning.
Further, the output unit 23 may be a unit that notifies about a change in state or a sudden abnormality by, for example, an alarm or vibration, and the evaluation unit 214 does not have a value within a predetermined range, for example. If it is determined, a warning is given by an alarm or vibration.

Note that the output unit 23 may not be provided in the information processing apparatus 20 but may be provided outside the information processing apparatus 20. Here, the case where the output unit 23 is provided outside the information processing device 20 is, for example, the case where the output unit 23 is included in the body motion signal detection device 10, or the body motion signal detection device 10 and the information processing. This is a case where the device 20 is not included in either. When provided outside the information processing apparatus 20, for example, the information processing apparatus 20 is connected to the output unit 23 via wireless or wired connection. That is, the output unit 23 functions as an output unit that outputs a result (for example, an evaluation coefficient) obtained by the information processing apparatus. The output unit 23 functions as an output device including an output unit that outputs a result (for example, an evaluation coefficient) obtained by the information processing device.

The interface unit 24 is, for example, an interface that is communicably connected to the body motion signal detection device 10. When the information processing apparatus 20 is connected to the body motion signal detection apparatus 10 via wireless, for example, the interface unit 24 includes an antenna. Further, when the information processing apparatus 20 is connected to the body motion signal detection apparatus 10 via a wired line, for example, the interface unit 24 is a wired connection terminal. For example, the storage unit 12 is provided detachably with respect to the body motion signal detection device 10, and when the storage unit 12 is detached from the body motion signal detection device 10 and connected to the information processing device 20, The interface unit 24 also functions as a slot to which the storage unit 12 is connected, for example.

The operation of the system 1 as an example of the embodiment configured as described above will be described with reference to the flowchart (steps A1 to A6) shown in FIG. Hereinafter, an example of a specific method for evaluating the synchronization of movement rhythm based on the integral signal will be described in detail for the case of using an acceleration signal during walking. The same processing can be applied to the above signal.

First, the body motion signal detection unit 11 detects a repetitive rhythm motion accompanying a voluntary motion as a body motion signal (step A1). FIG. 5A shows that the body motion signal detection device 10 (body motion signal detection unit 11) performs 100 Hz sampling with the body motion signal detection device 10 (triaxial acceleration sensor) attached to the center of the abdomen of a healthy subject. It is a part of the acceleration signal during walking for 15 minutes measured by. In FIG. 5A, only the acceleration change in the vertical direction among the three axes is shown. Next, based on the body motion signal detected by the body motion signal detection unit 11, the time constant determination unit 211 determines a time constant for extracting an exercise rhythm (step A2). Then, using the time constant determined in step A2, the rhythm extraction unit 212 extracts an exercise rhythm from the body motion signal (step A3). FIG. 5B is a diagram illustrating an example of an exercise rhythm extracted from a body motion signal.

Then, the evaluation coefficient determination unit 213 determines an evaluation coefficient from the extracted exercise rhythm (step A4). Based on this evaluation coefficient, the determination unit 214 determines, for example, the synchronization of the exercise rhythm (step A5: determination process). Next, based on the determination result of the determination unit 214, the output control unit 215 causes the output unit 23 to output the determination result (step A6).
Next, details of the time constant determination unit 211, that is, detailed operations of step A2 in FIG. 2 will be described with reference to the flowchart (steps A21 to A24) shown in FIG.

First, the time constant determination unit 211 performs filtering on the body motion signal detected by the body motion signal detection unit 11 using a predetermined time constant A (step A21). For example, the time constant determining unit 211 performs high-pass filtering represented by Y = XF (X, A) on the body motion signal, performs second-order integration, and again generates Y = XF ( Perform high-pass filtering represented by X, A). Furthermore, the time constant determination unit 211 performs low-pass filtering represented by Y = F (X, A / 2.5) after high-pass filtering, for example. That is, the time constant determination unit 211 performs bandbus filtering on the body motion signal.

Next, the time constant determination unit 211 obtains the absolute value CV of the values at the maximum and minimum peaks of the output waveform Y, for example (step A22). Then, the time constant determining unit 211 obtains a change in CV with respect to a change in the time constant A by changing the time constant A and performing the processes of steps A21 and A22, for example (step A23). FIG. 7 is a diagram illustrating an example of a change in CV (dispersion) with respect to a change in time constant (filter time) A obtained in step A23. FIG. 7 is a diagram illustrating an example of a change in CV (dispersion) with respect to a change in time constant (filter time) A when the body motion signal includes a respiratory rhythm. When the respiratory signal is not included in the body motion signal, the minimum point indicated by ♦ in FIG. 7 does not appear, and the minimum point is only the minimum point indicated by ●. Next, the time constant determination unit 211 obtains a minimum point from the change in CV with respect to the change in the time constant T obtained in step A23. For example, as shown in FIG. 7, the time constant determination unit 211 obtains two local minimum points (marks ● and ♦ in FIG. 7) having a small CV value. Then, the time constant determination unit 211 calculates, for example, a time constant value (for example, 0.4) in the vicinity of the time constant A corresponding to the minimum point (marked with ● in FIG. 7) having the smaller time constant A. It is determined as a time constant (step A24). As described above, the time constant determination unit 211 may determine the time constant in the vicinity of the minimum point as the first time constant instead of setting the time constant corresponding to the minimum point itself as the first time constant. Even in this case, there is no great difference in the results obtained.

Next, the details of the rhythm extraction unit 212, that is, the detailed operation of step A3 in FIG. 4 will be described with reference to the flowchart (steps A37 to A39) shown in FIG.
First, the rhythm extraction unit 212 performs high-pass filtering on the body motion signal detected by the body motion signal detection unit 11 using the first time constant (step A37). The rhythm extraction unit 212 performs N (for example, 2) order integration (step A38). Then, high-pass filtering is again performed on the signal after integration using the first time constant (step A39). FIG. 5B is a diagram illustrating an example of the signal obtained in step A39. This waveform corresponds to the voluntary movement of the subject who is walking, for example. More specifically, the waveform obtained in step 39 corresponds to the relative motion trajectory of each step by walking. That is, the waveform shown in FIG. 5B corresponds to a waveform indicating a movement rhythm.

Next, the details of the evaluation coefficient determination unit 213, that is, the detailed operation of step A4 in FIG. 4 will be described with reference to the flowchart (steps A41 to A43) shown in FIG.
First, the time calculation unit 223 selects an arbitrary point at an arbitrary time t of the exercise rhythm (step A41). The time calculation unit 223 calculates the phase using, for example, Hilbert transform or pattern matching, so that the time t1 at which the phase is shifted 360 degrees from the arbitrary point and the phase is shifted 720 degrees from the arbitrary point The point time t2 is calculated (step A42: time calculation process). Next, the evaluation coefficient calculation unit 233 calculates the evaluation coefficient from, for example, the above equation (1) using the times t1 and t2 (step A43: evaluation coefficient calculation process). That is, in step A42, from the signal representing the repetitive voluntary movement of the living body, a time (first time) when the phase of the signal changes by a predetermined angle (first angle) from the phase of the signal at a predetermined time, It is an example of the time calculation process which calculates the time (2nd time) when the phase of the signal changed twice the 1st angle from the phase of the signal in time. Step A43 is an example of a second calculation process for calculating an evaluation coefficient for evaluating the synchrony of repeated voluntary movement based on the first time and the second time.

Here, FIGS. 10A to 10C are diagrams for explaining an example of a signal obtained when the pattern matching method is used. An example of detailed operation of the evaluation coefficient determination unit 213 when pattern matching is used will be described with reference to FIGS. 10 (A) to 10 (C).
FIG. 10A is a partial waveform extracted from the data of FIG. Here, FIG. 10B shows the result of the time calculation unit 223 selecting the reference wave having a width of 0.4 seconds around the time indicated by * and calculating the autocorrelation coefficient. Similarly, FIG. 10C shows an autocorrelation coefficient obtained from a reference wave having a width of 0.4 seconds around the circle mark in FIG. In FIG. 10B and FIG. 10C, the positions of three points whose phases are shifted by 360 degrees are indicated by dotted lines.

For example, the evaluation coefficient calculation unit 233 calculates the first time T1 and the second time T2 from the waveforms shown in FIG. 10B or FIG. 10C, and sets the times T1 and T2 to the above formula (1). The evaluation coefficient is calculated by substituting for.
Next, for the integrated signal illustrated in FIG. 5B, the result of an example in which the evaluation coefficient is obtained every time the phase of the motion rhythm increases by 3.6 degrees using the Hilbert transform method is shown by the solid line in FIG. This is equivalent to obtaining an evaluation coefficient every 0.05 seconds on average when converted to a time interval. From FIG. 11, it can be seen that the evaluation coefficient fluctuates periodically around 0. This is because, in any living body, it is rare that the body is completely left-right symmetric, and even if there is no disease or disorder, there is a difference in the step interval between the left and right, and the body walks lamely. That is, T1 / T2 ≠ 1 in FIG. However, since the fluctuation of the evaluation coefficient itself changes very regularly, it can be said that the synchronization of the left and right footing is high.

In addition, with respect to the integral signal illustrated in FIG. 5B, the result of obtaining the evaluation coefficient at a time interval of 0.05 seconds using the pattern matching method is indicated by a broken line in FIG. From FIG. 11, it can be seen that results having the same tendency as the results obtained by the Hilbert transform method are obtained.
Next, details of the determination unit 214, that is, detailed operations of step A5 in FIG. 4 will be described with reference to the flowchart (steps A51 to A53) shown in FIG.

First, the evaluation coefficient determination unit 213 calculates an evaluation coefficient over a predetermined time (step A51), that is, the above steps A41 to A44 are repeated over a predetermined time. Next, the determination unit 214 calculates three indexes R, S, and C using the evaluation coefficient calculated over a predetermined time (step A52). Then, the determination unit 214 determines the synchronization of the exercise rhythm based on these three indicators (step A53).

Further, the detailed processing for determining the synchronism by the determination unit 214, that is, the detailed operation of step A53 in FIG. 12, will be described with reference to the flowchart (steps A531 to A539) shown in FIG.
First, the determination unit 214 determines whether or not R is 0.02 or less (step A531). When R is 0.02 or less (see the Yes route in step A531), the determination unit 214 determines that the synchronization and balance of the exercise rhythm are very good, assuming that the evaluation coefficient is substantially zero over a predetermined time (step S531). A532). That is, step A531 is an example of a determination process for determining whether the evaluation coefficient is within a predetermined range (for example, 0 ± 0.02) over a predetermined time. On the other hand, when R is larger than 0.02 (see No route in Step A531), the determination unit 214 determines whether S is 0.01 or less (Step A533). When S is 0.01 or less (see the Yes route in step A533), the determination unit 214 determines that the synchronization and balance of the exercise rhythm are good, assuming that the evaluation coefficient is not substantially 0 but remains constant. (Step A534). On the other hand, when S is larger than 0.01 (see No route in step A533), the determination unit 214 determines whether C is 0.5 or more (step A535). When C is equal to or greater than 0.5 (see the Yes route in step A535), it is determined that the evaluation coefficient fluctuates periodically, and the determination unit 214 determines that the exercise rhythm is synchronized but the balance is poor (step A536). ). On the other hand, when C is less than 0.5 (see No route in step A535), the determination unit 214 determines whether C is 0.2 or more (step A537). When C is 0.2 or more (see the Yes route in step A537), the determination unit 214 determines that the synchronization of the exercise rhythm is normal and the balance is bad (step A538). On the other hand, when C is less than 0.2 (see No route of step A537), the determination unit 214 determines that the synchronization of the exercise rhythm is poor and the balance cannot be determined (step A539).

[B] Explanation of real-time processing Data for changing the body movement rhythm by exactly two cycles is necessary for calculating the phase shift. In the case of the walking data illustrated in FIGS. 5A and 5B, this is about 1 second, so there is a time delay of at least 1 second from the measurement of the data to the output of the result. The tolerance is sufficient for processing.

Also, for real-time processing, it is necessary to perform analysis using data measured and accumulated up to the current time. The problem is the effect of data boundaries. That is, when signal processing such as spectrum analysis or Hilbert transform is performed, the error increases near the end points (measurement start point and end point) of the data. That is, the evaluation coefficient at the current time may include an error based on signal processing. In order to estimate this, the result of calculating the evaluation coefficient from the phase change by the Hilbert transform method using only the data up to this time as the current time for the acceleration data of FIG. This is indicated by a broken line. Compared with the result of the accurate calculation method using the entire 15-minute data (solid line in FIG. 14), a value with almost no error is obtained in a time that goes back about 1.3 seconds from the current time. This is sufficient performance for practical real-time processing.

As described above, according to the system 1 in the example of the present embodiment, since the evaluation coefficient for evaluating the synchrony of the movement rhythm is calculated based on the change of the phase by paying attention to the phase, a clear peak An evaluation coefficient can be calculated from data that does not have rhythm, and the synchronization of the motor rhythm can be evaluated. That is, according to the system 1 in the example of the present embodiment, since attention is paid to the phase, the evaluation coefficient can be obtained and the synchronization of the exercise rhythm can be evaluated without paying attention to the peak of the exercise rhythm.

Further, according to the system 1 in the example of the present embodiment, since it is not necessary to pay attention to the peak of the movement rhythm, the evaluation coefficient can be obtained almost continuously at an arbitrary time, and further, the evaluation coefficient can be obtained in real time. Can be requested. That is, according to the system 1 in the example of the present embodiment, the synchronization of the movement rhythm can be continuously evaluated, and further, the synchronization of the movement rhythm can be evaluated in real time.

In addition, according to the system 1 in the example of the present embodiment, by using pattern matching, the waveform after autocorrelation has less noise than the original body motion signal and thus has a clear peak. Since the peak position can be accurately specified, the evaluation coefficient can be obtained with high accuracy.
[C] Others The disclosed technique is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present embodiment.

For example, in the example of the present embodiment, in FIG. 5A, only the acceleration change in the vertical direction is shown among the three axes, and the movement rhythm is extracted based on this signal, and the evaluation coefficient is calculated. However, the signal is not limited to the signal from only the acceleration change in the vertical direction. For example, an evaluation coefficient may be calculated by extracting a motion rhythm from an acceleration signal in the front-rear direction or the left-right direction.
In the example of the present embodiment, as shown in FIG. 2, a point where the phase is shifted by 360 degrees from the predetermined position in the range where the phase changes from the predetermined position by 720 degrees is searched, and the evaluation coefficient is calculated. However, it is not limited to 360 degrees. For example, when the evaluation coefficient is calculated from the acceleration signal in the left and right direction, the adjacent acceleration signal in the left and right direction corresponds to the landing of the same foot as shown in FIG. In the range where the phase changes 360 degrees, the point where the phase is shifted from the predetermined position by 180 degrees is searched, and the evaluation coefficient is calculated with the time interval between them as the first time T1 and the second time T2, respectively. To do. Similarly, when an angular velocity signal around the vertical axis measured by the gyro sensor is used as the body motion signal detection unit 11, the same processing as when calculating the evaluation coefficient from the lateral acceleration signal is performed.

When pattern matching is used, a point where the correlation value is minimized is a point whose phase is shifted by 180 degrees from a predetermined position.
Furthermore, in the example of the present embodiment, the body motion signal detection device 10 does not include the central processing unit 21, but is not limited to this configuration, and the body motion signal detection device 10 includes the central processing unit 21. May be. In this case, the central processing unit 21 executes an information processing program stored in a storage device (for example, the storage unit 12) in the body motion signal detection device 10 or a storage unit (not shown) outside the body motion signal detection device 10. Thus, the above-described function is exhibited. That is, in this case, the body motion signal detection device 10 also functions as an information processing device.

Further, in the example of the present embodiment, the body motion signal detection device 10 includes the body motion signal detection unit 11 and the storage unit 12, but is not limited to this configuration. For example, the body motion signal detection device 10 may include the body motion signal detection unit 11 but may not include the storage unit 12. In this case, for example, the body motion signal detection device 10 and the storage unit 12 are connected by wire or wirelessly, and the body motion signal detected by the body motion signal detection unit 11 is transferred to the storage unit 12 via wire or wirelessly. Stored. In this case, the storage unit 12 and the information processing device 20 are connected by wire or wirelessly, and the information processing device 20 acquires a body motion signal from the storage unit 12.

Further, when the body motion signal detection device 10 and the information processing device 20 (or the storage unit 12) are connected via wireless, the body motion signal detection device 10 detects the body detected by the body motion signal detection unit 11. It has a function of transmitting a moving signal to the information processing apparatus 20 (or the storage unit 12) via the interface unit 13 that is an antenna. This transmission function is realized by executing a program stored in a storage unit (not shown) by a processing unit (not shown) provided by the body motion signal detection device 10 such as a central processing unit.

In the example of the present embodiment, the information processing apparatus 20 determines the first time constant from the body motion signal and extracts the exercise rhythm based on the first time constant, but is limited to this extraction method. It is not a thing. For example, the frequency characteristic of the body motion signal is obtained by spectrum analysis such as FFT (Fast Fourier Transform) or wavelet analysis, and the frequency range of the motion rhythm is specified from the result of the spectrum analysis. Then, for example, a movement rhythm may be extracted by applying a bandpass filter corresponding to the specified frequency range to the body motion signal.

In addition, the body motion signal is decomposed into each mode waveform (Intrinsic Mode Function) by EMD (Empirical Mode Decomposition) or EEMD (Ensemble Empirical Mode Decomposition), and from this result, the mode waveform corresponding to the exercise rhythm (for example, strong intensity) (Waveform) may be selected.
Furthermore, in the example of the present embodiment, after the time constant determination unit 211 determines the time constant, the rhythm extraction unit 212 extracts the body motion rhythm from the body motion signal by the filtering process, but this is not limitative. It is not a thing. For example, in the process (3), all output waveforms Y when the time constant T is changed are stored in the storage unit 22. The rhythm extraction unit 212 may select a waveform corresponding to the first time constant determined in the process (4) from all the output waveforms Y.

An example of this embodiment is applicable not only to people but also to animals such as pets, livestock, and horses.
Furthermore, although an example of the present embodiment mainly describes the case where the voluntary movement is walking, the present invention is not limited to walking, and the example of the present embodiment can be applied to other voluntary movements. For example, if the voluntary exercise is juggling, it is only necessary to focus on the exercise rhythm of the hand, not the foot.

Various application programs for realizing each function of the central processing unit 21 provided in the information processing apparatus 20 are, for example, a flexible disk, a CD (CD-ROM, CD-R, CD-RW, etc.), DVD (DVD -Recorded in a computer-readable recording medium such as a ROM, DVD-RAM, DVD-R, DVD + R, DVD-RW, DVD + RW, HD DVD), Blu-ray disc, magnetic disc, optical disc, magneto-optical disc, etc. Provided. Then, the computer reads the program from the recording medium, transfers it to the internal storage device or the external storage device, and uses it. The program may be recorded in a storage device (recording medium) such as a magnetic disk, an optical disk, or a magneto-optical disk, and provided from the storage device to the computer via a communication path.

Various application programs for realizing each function of a central processing unit (not shown) provided in the body motion signal detection apparatus 10 are, for example, a flexible disk, a CD (CD-ROM, CD-R, CD-RW, etc.), Recorded on computer-readable recording media such as DVD (DVD-ROM, DVD-RAM, DVD-R, DVD + R, DVD-RW, DVD + RW, HD DVD, etc.), Blu-ray disc, magnetic disc, optical disc, magneto-optical disc, etc. Provided in different forms. Then, the computer reads the program from the recording medium, transfers it to the internal storage device or the external storage device, and uses it. The program may be recorded in a storage device (recording medium) such as a magnetic disk, an optical disk, or a magneto-optical disk, and provided from the storage device to the computer via a communication path.

Hereinafter, the present invention will be described in more detail with reference to examples. However, the present invention is not limited to the following examples without departing from the gist thereof.

As the body motion signal detection device 10, an acceleration recorder “Mitami Gate” manufactured by Mitsubishi Chemical Corporation was put on a dedicated belt and wound around the abdomen of a Parkinson's disease patient, and the body motion signal of one day was sampled at a sampling frequency of 100 Hz. The recorder was positioned at the center of the abdomen. The vertical acceleration signal during walking on and off was extracted from the data, and the evaluation coefficient was obtained by the Hilbert transform method and the pattern matching method. FIG. 16A to FIG. 16C are diagrams showing walking results when on. FIG. 16A is a diagram illustrating acceleration detected by the body motion signal detection device 10. FIG. 16B is a diagram showing an acceleration signal after second-order integration, that is, a motion trajectory. FIG. 16C is a diagram showing the evaluation coefficient obtained from the acceleration signal after second-order integration. In FIG. 16C, since the evaluation coefficient has a regular fluctuation consistently, it can be said that there is a left-right difference in the walking step, but the left-right footing synchronization is high.

On the other hand, FIGS. 17 (A) to 17 (C) are diagrams showing walking results when the walking is relatively stable during the time of 3 to 17 seconds, but sudden progress is recognized before and after the walking. It is. FIG. 17A is a diagram illustrating acceleration detected by the body motion signal detection device 10. FIG. 17B is a diagram illustrating an acceleration signal after the second-order integration, that is, a motion trajectory. FIG. 17C is a diagram showing the evaluation coefficient obtained from the acceleration signal after second-order integration. In FIG. 17C, the solid line indicates the evaluation coefficient calculated using the Hilbert transform method, and the broken line indicates the evaluation coefficient calculated using the pattern matching method. Comparing FIG. 16 (A) to FIG. 16 (C) and FIG. 17 (A) to FIG. 17 (C), in FIG. 17 (C), the fluctuation of the evaluation coefficient during stable walking becomes irregular. It can be seen that the synchronization between the left and right steps is broken. Further, in FIG. 17C, it can be seen that the phase shift fluctuation is very large at the time of sudden progress, and the balance between the left and right steps is deteriorated.

Wearing a small wireless hybrid sensor WAA-006 manufactured by Wireless Technology as a body motion signal detection device 10 in the center of the abdomen of a healthy subject, sampling a 3-axis acceleration signal and 3-axis angular velocity signal when walking in the city with a heavy bag on the shoulder Simultaneous measurement at a frequency of 200 Hz. The acceleration signal in the vertical direction and the angular velocity signal around the vertical axis are: • High-pass filtering using time constant 1 • Second-order integration • Hilbert transform method after extracting motion rhythm by applying high-pass filtering using time constant 1 To obtain the phase. Evaluation coefficients were calculated from two points where the phase difference was 360 degrees for the acceleration signal and two points where the phase difference was 180 degrees for the angular velocity signal. FIG. 18A is a diagram showing acceleration and angular velocity detected by the body motion signal detection device 10. FIG. 18B is a diagram showing an acceleration signal and an angular velocity signal after second-order integration. FIG. 18C is a diagram showing evaluation coefficients obtained from the acceleration signal and the angular velocity after the second-order integration. In FIGS. 18A to 18C, the solid line indicates the acceleration signal, and the broken line indicates the angular velocity signal. The evaluation coefficient shows almost the same fluctuation in strength. It can also be seen that the balance between the left and right is getting worse, reflecting the heavy bag on his shoulder.

Thus, the synchrony of body movement rhythm can be evaluated regardless of the type of inertial sensor.

As the body motion signal detection device 10, an acceleration recorder “Watching Gate” manufactured by Mitsubishi Chemical Corporation was put on a dedicated belt and wound around the abdomen of a Parkinson's disease patient, and body motion signals were continuously sampled at a sampling frequency of 100 Hz for 38 hours. At the same time, the subjects were asked to fill in a diary of the degree of ease of movement and the time when they fell.

First, we extracted an area that was continuously walking for more than 10 seconds from the time-series data of body motion signals. This area was time-divided at 10-second intervals to create a partial time series. The filter time constant was optimized for each partial time series, and the left and right footing synchronization S2 was obtained. The optimization of the time constant was performed by the method of minimizing S2. From the obtained S2, exp (-10 × S2) was calculated and used as a walking index. The walking index is an index that takes a larger value as the left and right footsteps are more synchronized, and is 1 when the synchronization is perfect. FIG. 19A shows the time change of the walking index after the smoothing process is performed. In FIG. 19 (A), there is a five-step evaluation of ease of movement by the subject's self-report (movable, difficult to move, unable to move in three stages, easy to move-intermediate evaluation of difficult to move, and difficult to move-not to move 5 grades that include 2 grades of intermediate assessment), and the actual times t, tb, tc, and td of the fall. In FIG. 19A, the change of the walking index is indicated by a dotted line, and the change of the self-report of the subject is indicated by a solid line.

Referring to FIG. 19 (A), between 9 am and 10 am on the first day, the subject's self-report is an intermediate evaluation of “not easy to move” or “easy to move” — “not easy to move” at times ta and tb. A fall is happening. In accordance with this, the walking index is also low. From 17:00 to 18:00 on the first day, from 10:00 to 11:00 on the second day, the subject's self-report is “easy to move”, but the gait index is lower than the time zone before and after, A fall occurred at times tc and td. Moreover, it can be seen that the walking index tends to decrease considerably before the fall time.

Thus, the synchrony of walking rhythm is an index for predicting the risk of falls.

Next, the same time series data was time-divided at 10 second intervals to create a partial time series. Here, the walking area is not taken out. The filter time constant was optimized for each partial time series, and the left and right footing synchronization S2 and the phase period CV were obtained. The optimization of the time constant was performed by the method of minimizing S2. From the obtained S2, exp (-10 × S2) was calculated and used as a body motion index. Of these, those with a phase period CV of 0.12 or more were removed. This means that partial time series with poor periodicity are excluded, and attention is paid to the nature of regular rhythms (especially walking rhythms). FIG. 19B shows a time change of the body motion index after the smoothing process is performed. In FIG. 19 (B), the subject's self-reported five-step evaluation of ease of movement (movable, difficult to move, unable to move in three steps, easy to move-intermediate evaluation of difficult to move, and difficult to move-unmovable 5 grades that include 2 grades of the intermediate assessment)), and the actual fall times are indicated as ta, tb, tc, and td. In FIG. 19B, the transition of the body motion index is indicated by a dotted line, and the transition of the subject's self-report is indicated by a solid line.

Referring to FIG. 19 (B), in the vicinity of the fallen area, the same behavior as in FIG. 19 (A) is shown. That is, between 9 am and 10 am on the first day, the subject's self-report is an intermediate evaluation of “not easy to move” or “easy to move” — “not easy to move”, and a fall occurred at times ta and tb. In accordance with this, the body motion index is also low. From 17:00 to 18:00 on the first day, from 10:00 to 11:00 on the second day, the subject's self-report is “easy to move”, but the body motion index is lower than before and after, A fall occurred at times tc and td. Moreover, it can be seen that the body motion index tends to decrease considerably before the fall time.

Thus, the synchrony of body movement rhythm is an index for predicting the risk of falls.

Although the present invention has been described in detail and with reference to specific embodiments, it will be apparent to those skilled in the art that various changes and modifications can be made without departing from the spirit and scope of the invention.

This application is based on a Japanese patent application filed on September 17, 2010 (Japanese Patent Application No. 2010-209112), the contents of which are incorporated herein by reference.

According to the information processing method, information processing apparatus, output device, information processing system, information processing program, and computer-readable recording medium storing the program according to the present invention, the synchronization of the movement rhythm is continuously evaluated in real time. There is an effect that can be done.

DESCRIPTION OF SYMBOLS 1 System 10 Body motion signal detection apparatus 11 Body motion signal detection part 12 Memory | storage part 13 Interface part 20 Information processing apparatus 21 Central processing part 22 Memory | storage part 23 Output part 24 Interface part 211 Time constant determination part 212 Rhythm extraction part 213 Evaluation coefficient determination Unit 214 determination unit 215 output control unit 223 time calculation unit 233 evaluation coefficient calculation unit

Claims (28)

1. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation process for calculating a time (hereinafter referred to as a second time) when the phase of the signal changes twice the predetermined angle;
   An evaluation coefficient calculation process for calculating an evaluation coefficient for evaluating the synchronization of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation process. Characteristic information processing method.
2. The evaluation coefficient calculation process includes a time from the predetermined time to the first time (hereinafter referred to as a first time) and a time from the first time to the second time (hereinafter referred to as a second time). The information processing method according to claim 1, wherein the evaluation coefficient is a process based on the information.
3. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation process for calculating a time when the phase of the signal has changed by −1 times the predetermined angle (hereinafter referred to as a second time);
   An evaluation coefficient calculation process for calculating an evaluation coefficient for evaluating the synchronization of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation process. Characteristic information processing method.
4. The evaluation coefficient calculation process includes a time from the predetermined time to the first time (hereinafter referred to as a first time) and a time from the second time to the predetermined time (hereinafter referred to as a second time). The information processing method according to claim 3, wherein the evaluation coefficient is a process based on the method.
5. 5. The time calculation process according to claim 1, wherein the time calculation process is a process of calculating the first time and the second time using a Hilbert transform method or a pattern matching method. The information processing method described.
6. The time calculation process includes the first time when the phase of the signal has changed 360 degrees from the phase of the signal at the predetermined time, and the phase of the signal has changed 720 degrees from the phase of the signal at the predetermined time. 6. The information processing method according to claim 1, wherein the second time is calculated.
7. In the time calculation process, the phase of the signal changes by 180 degrees from the phase of the signal at the predetermined time, and the phase of the signal changes by 360 degrees from the phase of the signal at the predetermined time. 6. The information processing method according to claim 1, wherein the second time is calculated.
8. 8. The time calculation process according to claim 1, wherein the first time and the second time are calculated from a signal representing a repetitive voluntary movement of a living body detected by one detection unit. The information processing method according to any one of claims.
9. 9. The information according to claim 1, further comprising a determination step of determining whether the evaluation coefficient calculated by the evaluation coefficient calculation step is within a predetermined range over a predetermined time. Processing method.
10. 10. The information processing method according to claim 1, wherein the repeated voluntary exercise is any one of walking, jogging, running, cycling, swimming, and gymnastics.
11. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation unit for calculating a time at which the phase of the signal has changed twice the predetermined angle (hereinafter referred to as a second time);
    An evaluation coefficient calculation unit that calculates an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation unit. An information processing apparatus is characterized.
12. The evaluation coefficient calculation unit includes a time from the predetermined time to the first time (hereinafter referred to as a first time) and a time from the first time to the second time (hereinafter referred to as a second time). The information processing apparatus according to claim 11, wherein the evaluation coefficient is calculated based on the information.
13. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation unit for calculating a time when the phase of the signal has changed by −1 times the predetermined angle (hereinafter referred to as a second time);
    An evaluation coefficient calculation unit that calculates an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation unit. An information processing apparatus is characterized.
14. The evaluation coefficient calculation unit includes a time from the predetermined time to the first time (hereinafter referred to as a first time) and a time from the second time to the predetermined time (hereinafter referred to as a second time). The information processing apparatus according to claim 13, wherein the evaluation coefficient is calculated based on the information.
15. The time calculation unit according to any one of claims 11 to 14, wherein the time calculation unit is a process of calculating the first time and the second time by using a Hilbert transform method or a pattern matching method. The information processing apparatus described.
16. The information processing apparatus according to any one of claims 11 to 15, wherein the first angle is 360 degrees.
17. The information processing apparatus according to any one of claims 11 to 15, wherein the first angle is 180 degrees.
18. The information processing apparatus according to any one of claims 11 to 17, wherein the signal representing the repetitive voluntary movement is a signal detected by one detection unit.
19. The information according to any one of claims 11 to 18, further comprising a determination unit that determines whether or not the evaluation coefficient calculated by the evaluation coefficient calculation unit is within a predetermined range over a predetermined time. Processing equipment.
20. The information processing apparatus according to any one of claims 11 to 19, wherein the repetitive rhythmic exercise is any one of walking, jogging, running, cycling, swimming, and gymnastics.
21. The time when the phase of the signal changes by a predetermined angle from the phase of the signal representing the repetitive voluntary movement of the living body at a predetermined time, and the phase of the signal changes by twice the predetermined angle from the phase of the signal at the predetermined time An output device comprising: an output unit that outputs an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on time in a form in which the evaluation coefficient can be identified.
22. The time when the phase of the signal changes by a predetermined angle from the phase of the signal representing the repeated voluntary movement of the living body at the predetermined time, and the phase of the signal changes by −1 times the predetermined angle from the phase of the signal at the predetermined time An output device comprising: an output unit that outputs an evaluation coefficient for evaluating the synchronization of the repetitive voluntary movement based on the determined time in a form in which the evaluation coefficient can be identified.
23. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation unit for calculating a time at which the phase of the signal has changed twice the predetermined angle (hereinafter referred to as a second time);
    An evaluation coefficient calculation unit that calculates an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation unit;
    An information processing system comprising: an output unit that outputs the evaluation coefficient calculated by the evaluation coefficient calculation unit in a form in which the evaluation coefficient can be identified.
24. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation unit for calculating a time when the phase of the signal has changed by −1 times the predetermined angle (hereinafter referred to as a second time);
    An evaluation coefficient calculation unit that calculates an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation unit;
    An information processing system comprising: an output unit that outputs the evaluation coefficient calculated by the evaluation coefficient calculation unit in a form in which the evaluation coefficient can be identified.
25. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation process for calculating a time (hereinafter referred to as a second time) when the phase of the signal changes twice the predetermined angle;
    Causing the computer to execute an evaluation coefficient calculation step of calculating an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation step An information processing program characterized by the above.
26. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation process for calculating a time when the phase of the signal has changed by −1 times the predetermined angle (hereinafter referred to as a second time);
    Causing the computer to execute an evaluation coefficient calculation step of calculating an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation step An information processing program characterized by the above.
27. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation process for calculating a time when the phase of the signal has changed twice the first angle (hereinafter referred to as a second time);
    Causing the computer to execute a second calculation step of calculating an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation step. A computer-readable recording medium on which an information processing program is recorded.
28. From the signal representing the repetitive voluntary movement of the living body, the time when the phase of the signal changes by a predetermined angle from the phase of the signal at a predetermined time (hereinafter referred to as the first time), and the phase of the signal at the predetermined time A time calculation process for calculating a time when the phase of the signal has changed by -1 times the first angle (hereinafter referred to as a second time);
    Causing the computer to execute a second calculation step of calculating an evaluation coefficient for evaluating the synchrony of the repetitive voluntary movement based on the first time and the second time calculated by the time calculation step. A computer-readable recording medium on which an information processing program is recorded.

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