JP2016220922A - Locomotive motion analysis apparatus and system, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To make it possible to precisely recognize heel landing in the data obtained by an acceleration sensor attached to a person during locomotive motion of the person, such as walking.SOLUTION: Provided is a locomotive motion analysis apparatus which comprises: specification means for specifying a temporal interval K corresponding to one step in a first waveform generated based on the data representing an acceleration during a person's locomotive motion, the acceleration being obtained by an acceleration sensor; and detection means for detecting, as the time HC of heel landing of the person's foot, the time corresponding to one the maximum and minimum values in each of the intervals K in a second waveform WJz` which represents, in the same time period as the first waveform, the vertical acceleration during the locomotive motion of the person obtained based on the data.SELECTED DRAWING: Figure 17

Description

  The present invention relates to a technique for evaluating a moving motion such as walking of a person using an acceleration sensor.

  Conventionally, rehabilitation for patients with gait disorders has been performed in medical facilities, nursing homes, and the like.

  In this rehabilitation, an instructor, such as a physical therapist, generally creates a gait training program (program) suitable for the patient and understands the patient's recovery level. Is repeatedly evaluated.

  In recent years, an acceleration sensor is used to measure acceleration generated during a moving motion such as walking of a person, and an attempt is made to quantitatively and objectively evaluate the moving motion based on the measurement result ( For example, see Patent Document 1, Abstract, etc.).

JP 2013-059489 A

  As described above, when the moving motion is evaluated based on the measurement result of the acceleration, it is very important to recognize the timing at which the foot lands in the acceleration data.

  However, complex waveforms often appear in the acceleration data, and it is difficult to accurately recognize the timing at which the feet land.

  Under such circumstances, there is a demand for a technique capable of recognizing the timing at which a foot lands with high accuracy in data from an acceleration sensor attached to a person who performs a moving motion.

The invention of the first aspect
In the first waveform generated based on the data representing the acceleration during the moving motion of the person obtained using the acceleration sensor, a specifying means for specifying a time interval corresponding to one step;
In the second waveform representing the vertical acceleration during the movement of the person obtained based on the data in the same time as the first waveform, either the maximum value or the minimum value of each of the sections There is provided a mobile motion analysis apparatus comprising detection means for detecting a time corresponding to the time when a foot has landed.

The invention of the second aspect is
In the first waveform generated based on the data representing the acceleration during the moving motion of the person obtained using the acceleration sensor, a specifying means for specifying a time interval corresponding to one step;
In the second waveform representing the amount calculated based on the vertical acceleration during the movement movement of the person based on the data in the same time as the first waveform, the maximum value of each of the sections And a detecting means for detecting the time corresponding to one of the minimum values as the time when the foot has landed.

  Here, the “time when the foot has landed” is a time when at least a part of the foot, for example, the entire heel or the sole of the foot, substantially contacts the road surface on which the person walks. Note that “landing” includes a case where a foot indirectly touches a road surface through shoes or the like.

The invention of the third aspect is
The mobile motion analysis apparatus according to the second aspect, wherein the calculated amount is calculated by an arithmetic expression including an acceleration term in the vertical direction.

The invention of the fourth aspect is
The moving motion analysis according to the second aspect or the third aspect, wherein the calculated amount is calculated by an arithmetic expression including a term obtained by second-order differentiation of the vertical acceleration to a power of k (k ≧ 1). Providing equipment.

The invention of the fifth aspect is
The mobile motion analysis apparatus according to any one of the first to fourth aspects further includes means for removing a high-frequency component from the second waveform.

The invention of the sixth aspect is
From the first aspect, the specifying means includes means for specifying the section based on a third waveform representing a linear combination of vertical acceleration and longitudinal acceleration obtained from the data. A moving motion analysis apparatus according to any one of the five aspects is provided.

The invention of the seventh aspect
The mobile motion analysis apparatus according to the sixth aspect, wherein the specifying means includes means for specifying, as the section, a time period corresponding to any one of a maximum value and a minimum value in the third waveform. provide.

The invention of the eighth aspect
The specifying means subtracts a waveform obtained by moving and averaging the waveform of the position from the waveform of the position obtained by second-order integration of the acceleration in the vertical direction, the longitudinal direction, or the horizontal direction obtained from the data. Provided is a mobile motion analysis apparatus according to any one of the first to fifth aspects including means for specifying the section based on the waveform.

The invention of the ninth aspect is
The mobile motion analysis device according to any one of the first to eighth aspects is provided, wherein the mobile motion is walking.

The invention of the tenth aspect is
A mobile motion analysis system comprising the acceleration sensor and the mobile motion analysis device according to any one of the first to ninth aspects is provided.

The invention of the eleventh aspect is
A program for causing a computer to function as the mobile motion analysis apparatus according to any one of the first to ninth aspects is provided.

  According to the invention of the above aspect, it is possible to recognize the timing at which a foot lands with high accuracy in data from an acceleration sensor attached to a person who performs a moving exercise.

It is a figure showing roughly composition of a walk analysis system. It is a figure which shows the structure of the hardware of an acceleration sensor module and a walk analysis apparatus. It is a functional block diagram which shows the functional structure of an acceleration sensor module and a walk analysis apparatus. It is a functional block diagram which shows the functional structure of an acceleration data analysis part. It is a flowchart which shows the flow of the process in a walk analysis system. It is a figure which shows the left-right direction acceleration waveform, the front-back direction acceleration waveform, and the up-down direction acceleration waveform which were produced | generated based on the sample acceleration data. It is an enlarged view of a left-right direction acceleration waveform, a front-rear direction acceleration waveform, and a vertical direction acceleration waveform generated based on sample acceleration data. FIG. 6 is a diagram in which a walking period R ′ determined as an analysis target is superimposed on acceleration waveforms W _x , W _y , W _z generated based on sample acceleration data. It is a figure which shows the relationship between the up-down direction acceleration waveform _Wz and a walk phase. It is a functional block diagram which shows the functional structure of a step reference time detection part. It is a flowchart which shows the flow of a step reference time detection process. It is an enlarged view of the longitudinal acceleration waveform W _y ′ and the vertical acceleration waveform W _z ′ based on sample acceleration data. It is a figure which shows the walk synchronous waveform WB based on sample acceleration data. It is a figure which shows the one step area K in the walk synchronous waveform WB. Is a diagram illustrating a walking synchronization waveform WB and vertical acceleration reflected waveform WJ _z '. It is a figure which shows the intra-section maximum value _Mk specified for every one step section K in the vertical acceleration reflecting waveform WJ _z '. It is a figure which shows the time of the saddle landing detected as step reference | standard time. It is a figure which shows the functional structure of the saddle landing right / left discrimination | determination part. It is a flowchart which shows the flow of a saddle landing left-right discrimination | determination process. It is an enlarged view of the left-right direction acceleration waveform _Wx 'based on sample acceleration data. It is a figure which shows the comparative example of the frequency distribution before and behind the trend removal of the horizontal direction position waveform. It is a diagram illustrating the processed waveform WL _x in the lateral direction of the position 'and the various waveforms. It is a figure which shows the example of the left-right discrimination | determination of a saddle landing. It is a functional block diagram which shows the functional structure of a step time graph production | generation part. It is a flowchart which shows the flow of a step time graph production | generation process. It is a figure which shows the specific example of left-right step time. It is a figure which shows the example of the step time graph at the time of normal walking. It is a figure which shows the example of the step time graph at the time of right knee fixation.

  Embodiments of the invention will be described below. The invention is not limited thereby.

  FIG. 1 is a diagram schematically showing a configuration of a gait analysis system (system) 1. The gait analysis system 1 is an example of the movement analysis system in the invention.

  The walking analysis system 1 includes an acceleration sensor module 2 and a walking analysis device 3 as shown in FIG. The acceleration sensor module 2 is attached to the center of the lower back of the patient 10 using an adhesive pad or a band. The walking analysis device 3 is used by the operator 11 to carry or operate. The walking analysis device 3 is an example of the mobile motion analysis device in the invention.

  FIG. 2 is a diagram illustrating a hardware configuration of the acceleration sensor module 2 and the walking analysis device 3.

  As shown in FIG. 2, the acceleration sensor module 2 includes a processor 21, an acceleration sensor 22, a memory 23, a communication I / F (interface) 24, and a battery 25. doing. The gait analysis device 3 is a computer terminal such as a smart phone, a tablet computer, or a notebook PC, and includes a processor 31, a display 32, an operation unit 33, and the like. , A memory 34, a communication I / F 35, and a battery 36. Note that the processor 21 and the processor 31 are not limited to a single processor, but may be a plurality of processors.

  FIG. 3 is a functional block diagram showing functional configurations of the acceleration sensor module 2 and the walking analysis device 3.

  As shown in FIG. 3, the acceleration sensor module 2 includes an acceleration sensor unit 201, a sampling unit 202, and a transmission unit 203. The sampling unit 202 and the transmission unit 203 are realized by the processor 21 reading out and executing a predetermined program stored in the memory 23.

  The acceleration sensor unit 201 outputs an analog signal corresponding to the acceleration component in almost real time with respect to the acceleration component in each of the x, y, and z axes in the three-dimensional orthogonal coordinate system based on the sensor body. Output to.

The sampling unit 202 samples the analog signal at a predetermined sampling frequency and converts it into digital acceleration data. The sampling frequency is, for example, 128 Hz. The sampling unit 202 outputs acceleration data at a scale where, for example, 1 g (gravitational acceleration) = 9.8 m / s ² = acceleration data value 128.

  The transmission unit 203 wirelessly transmits the acceleration data representing the sampled acceleration component at each time in almost real time.

  In this example, in the acceleration sensor module 2, the x-axis direction, the y-axis direction, and the z-axis direction of the sensor body are the RL (Right-Left) direction, AP (Anterior-Posterior) direction, and SI of the patient 10, respectively. (Superior-Inferior) It is attached to match the direction. The RL direction, the AP direction, and the SI direction are also referred to as a sagittal direction, a coronal direction, and an axial direction, respectively. In this example, it is assumed that the posture (inclination) of the acceleration sensor module 2 does not change while the patient 10 is walking.

  As shown in FIG. 3, the gait analysis device 3 includes an operation unit 301, a display unit 302, a patient information reception unit 303, a reception unit 304, an acceleration data acquisition control unit 305, an acceleration data analysis unit 307, A display control unit 310 and a storage unit 312 are included. The patient information reception unit 303, the acceleration data acquisition control unit 305, the acceleration data analysis unit 307, and the display control unit 310 are realized by the processor 31 reading and executing a predetermined program stored in the memory 34.

  The operation unit 301 receives an operation of the operator 11. The operation unit 301 includes, for example, a touch panel, a touch pad, a keyboard, a mouse, and the like. The operator 11 is an instructor such as a physical therapist, for example.

  The display unit 302 displays an image. The display unit 302 is configured by, for example, a liquid crystal panel, an organic EL panel, or the like.

  The patient information accepting unit 303 accepts input of patient information and causes the storage unit 312 to store the input patient information.

  The reception unit 304 wirelessly receives the acceleration data transmitted from the transmission unit 203 of the acceleration sensor module 2. Note that standards such as Bluetooth (registered trademark) can be used for wireless communication between the transmission unit 203 and the reception unit 304, for example.

  The acceleration data acquisition control unit 305 controls the reception unit 304 and the storage unit 312 to acquire acceleration data based on an operation by the operator 11.

  The acceleration data analysis unit 307 analyzes the acquired acceleration data and outputs the analysis result. Details of the acceleration data analysis unit 307 will be described later.

  The display control unit 310 controls the display unit 302 to display various images, character information, and the like including at least the analysis result of the acceleration data on the screen of the display unit 302.

  The storage unit 312 stores input patient information, acquired acceleration data, an analysis result of acceleration data, and the like. These pieces of information are transferred to a database 41 connected to the gait analysis device 3 as necessary, or a medium such as an external DVD-ROM or a memory card, the Internet ( or stored in a storage medium 42 including an external medium connected via the internet.

  Here, details of the acceleration data analysis unit 307 will be described. The acceleration data analysis unit 307 performs analysis processing on the acquired acceleration data and outputs the analysis result. A plurality of analysis processes are prepared. The acceleration data analysis unit 307 executes analysis processing specified by the operator 11. In this example, as an analysis process to be executed, an acceleration waveform representing a time change of the acceleration component carried by the acquired acceleration data is generated, and the time taken for each step forward of the patient 10 from the acceleration waveform. Assume a process of obtaining a step time and graphing the obtained step time.

  It should be noted that, in general, in the forward movement of each step on the left and right, there is one action (walking phase) of the right foot heel landing, left foot toe kick, left foot heel landing, and right foot toe kick. Included one by one. One step forward movement is defined as the movement from the landing of one foot to the landing of the other foot. One step forward movement is also called a step or one step. Further, a two-step forward movement consisting of one step on the left and right is defined as a movement from the landing of one foot to the landing of the other foot through the landing of the other foot. The two-step forward movement is also called a stride or one stride. The one-step cycle is defined as the time required for one step of forward movement. The one-step cycle is a time required from a specific walking phase of one foot (for example, saddle landing or toe kick) to the same walking phase of the other foot. The two-step cycle or the walking cycle is a time required from a specific walking phase of one foot to the same walking phase of the next one foot.

  FIG. 4 is a functional block diagram showing a functional configuration of the acceleration data analysis unit 307. As shown in FIG. 4, the acceleration data analysis unit 307 implements the acceleration component calculation unit 71, the acceleration waveform generation unit 72, the walking period specification unit 73, and the step reference time detection unit 74 in order to realize the above function. A landing landing left / right discriminating unit 75 and a step time graph generating unit 76 are provided.

The acceleration component calculation unit 71 calculates acceleration components a _x , a _y , and a _z in the left-right direction, the front-rear direction, and the vertical direction of the patient 10 at each sampling time in the data acquisition period based on the acquired acceleration data. . In this example, it is assumed that these acceleration components a _x , a _y , and a _z are calculated as acceleration components generated by pure motion of the patient 10 by removing the gravitational acceleration g component. However, it may be specified more simply in a form including the component of gravitational acceleration g. Further, the horizontal direction, the front-rear direction, and the vertical direction are assumed to be a horizontal left-right direction, a horizontal traveling direction, and a vertical direction, respectively. However, the x-axis direction, the y-axis direction, and the z-axis direction based on the sensor body of the acceleration sensor module 2 may be more simply used. Here, the positive / negative of the acceleration component is positive in the left-right direction, close to the front in the front-rear direction, and upward in the vertical direction.

The acceleration waveform generation unit 72 generates a left-right acceleration waveform W _x representing a temporal change in the left-right acceleration component a _x based on the calculated acceleration components a _x , a _y , a _z at each sampling time in each direction. A longitudinal acceleration waveform W _y representing a temporal change in the acceleration component a _{y in} the direction and a vertical acceleration waveform W _z representing a temporal change in the acceleration component a _{z in} the vertical direction are generated.

  The walking period specifying unit 73 specifies a period during which the patient 10 is actually walking in the acceleration data acquisition period (hereinafter also referred to as a walking period).

  The step reference time detection unit 74 detects a plurality of step reference times, each corresponding to one step forward movement of the patient 10, based on the acceleration waveform during the walking period determined as the analysis target. The detected step reference time is used when extracting a partial waveform corresponding to the forward movement for one step or a plurality of steps in the acceleration waveform. Here, the time corresponding to the landing timing is detected as the step reference time.

  The saddle landing left / right discriminating unit 75 generates a walking synchronization waveform, and discriminates whether the specified saddle landing is due to the left or right foot based on the waveform.

  Based on the detected step reference time, the step time graph generation unit 76 obtains a step time, which is a time taken for the one-step forward motion, for each one-step forward motion constituting the walking motion. And the graph showing the time-sequential change of this step time is produced | generated. In addition, the step time graph generation unit 76 calculates various feature amounts useful for walking evaluation of the patient 10 based on the obtained step time.

  Hereafter, the flow of processing in the walking analysis system 1 will be described.

  FIG. 5 is a flow diagram showing the flow of processing in the gait analysis system 1.

  In step S <b> 1, the patient information reception unit 303 receives input of patient information and causes the storage unit 312 to store the input patient information. Here, the operator 11 directly inputs the patient information of the patient 10 by operating the operation unit 301 of the gait analyzer 3. The patient information receiving unit 303 stores the directly input patient information in the storage unit 312. The patient information includes, for example, the patient ID number, name, age, sex, date of birth, and the like. It should be noted that acceleration data of the patient 10 to be described later, an analysis result of the acceleration data, and the like are stored in the storage unit 312 in association with the patient information.

In step S2, the acceleration data acquisition control unit 305 controls the reception unit 304 and the storage unit 312 to acquire acceleration data of the patient 10 at each time t _i . Here, first, the operator 11 attaches the acceleration sensor module 2 to the waist of the patient 10. Then, the operator 11 performs an acceleration data acquisition start operation using the operation unit 31 of the walking analysis apparatus 3. In response to this operation, the acceleration data acquisition control unit 305 causes the reception unit 304 to start receiving acceleration data, and causes the storage unit 312 to start storing the received acceleration data. Next, the patient 10 is allowed to walk for a while at his / her standard walking speed. Walking is usually about 5 to 20 m in distance, about 20 seconds to 3 minutes in time, and about 10 to 40 steps in number of steps. Based on the output of the acceleration sensor unit 201, the sampling unit 202 of the acceleration sensor module 2 calculates acceleration components A _x , A _y , and A _z in the x-axis direction, the y-axis direction, and the z-axis direction during walking of the patient 10. Sampling and measuring. The transmission unit 203 of the acceleration sensor module 2 transmits acceleration data representing the measured acceleration component almost in real time. During this time, the receiving unit 304 sequentially receives the acceleration data transmitted from the transmitting unit 203, and the storage unit 312 stores the received acceleration data. When the walking of the patient 10 is completed, the operator 11 performs an operation for ending acquisition of acceleration data using the operation unit 31. In response to this operation, the acceleration data acquisition control unit 305 causes the reception unit 304 to finish receiving the acceleration data. As a result, the period from when the acceleration data acquisition start operation is performed to when the acquisition end operation is performed is substantially the acceleration data acquisition period, and acceleration data in each direction at each sampling time in this period is acquired. .

  In step S3, the acceleration data analysis unit 307 sets an analysis process to be performed on the acceleration data. For example, the operator 11 operates the operation unit 301 to display the acquired acceleration data as a graph, or analyzes the acquired acceleration data and displays the result. Select the desired function you want to have. The acceleration data analysis unit 307 sets an analysis process to be executed according to the selected function. In this example, it is assumed that the operator 11 selects a function that displays a graph representing a time-series change in the step time of the patient 10 and a feature amount using the step time. According to such a function, it is possible to easily recognize the time required for a step-by-step advance operation while the patient 10 is walking, the time change thereof, the difference between left and right, the balance between left and right. As a result, the operator 11 can understand the balance and stability of the left and right foot movements in the walking motion of the patient 10, the degree of fatigue, and the like.

In step S4, the acceleration component calculation unit 71 reads out the acquired acceleration data from the storage unit 312, and based on the acceleration data, the left-right direction, the front-rear direction, and the vertical direction of the patient 10 at each sampling time in the acceleration data acquisition period. Acceleration components a _x , a _y , and a _z are calculated or specified. Here, the acceleration components in each sampling time and each direction are calculated using a predetermined algorithm (algorithm) including a process of removing the gravitational acceleration g component from the acceleration represented by the acceleration data. The calculated acceleration component is transmitted to and stored in the storage unit 312.

In step S5, the acceleration waveform generation unit 72 generates a left-right acceleration component a _x , a longitudinal acceleration component a _y , and a vertical acceleration component a _z at each sampling time of the patient 10 calculated in step S4. A direction acceleration waveform W _x , a longitudinal acceleration waveform W _y , and a vertical acceleration waveform W _z are generated. In the present example, the acceleration waveform generation unit 72 performs each time t in a two-dimensional coordinate system having an elapsed time (time) from the acceleration data acquisition start time and an acceleration component for each direction of the acceleration component. _An acceleration waveform is generated by plotting data points [a (i), t _i ] corresponding to the acceleration component a (i) at i. The acceleration waveform is smoothed or smoothed as necessary to make a smooth curve.

FIG. 6 is a diagram showing a left-right acceleration waveform W _x , a longitudinal acceleration waveform W _y , and a vertical acceleration waveform W _z generated based on the sample acceleration data. The horizontal axis represents time t (seconds) elapsed from the start of acceleration data acquisition, and the vertical axis represents acceleration data values a _x , a _y , a _z (gravity acceleration g / 128). This sample acceleration data is acquired for about 40 seconds. When acquiring this sample acceleration data, the subject starts walking around time t = 7 seconds, pauses walking around time t = 20-23 seconds, and crouches, and time t = Walking has been completed around 35 seconds. In the generated acceleration waveform, a change in acceleration component due to such movement of the subject appears.

FIG. 7 is an enlarged view of the lateral acceleration waveform W _x , the longitudinal acceleration waveform W _y , and the vertical acceleration waveform W _z generated based on the sample acceleration data.

  In a human walking movement, normally, four operations are repeated in this order: landing on one foot, kicking the toe of the other foot, landing on the other foot, and kicking the toe of one foot.

In the vertical acceleration waveform _Wz , as shown in FIG. 7, it is known that a maximum value, that is, a peak waveform, in which the peak value becomes a certain value or more corresponding to each of the above four operations constituting the walking motion is taken. ing. Also, the time from the foot landing of one (other) foot to the toe kick of the other (one) foot is relatively short, and the same one (other) foot from the one (other) foot toe kick The time to landing is relatively long.

  In the longitudinal acceleration waveform Wz, as shown in FIG. 7, one step forward movement from the landing position of one foot to the toe kick of the other foot, and from the landing position of the other foot to the toe kick of one foot Corresponding to the one-step forward movement, it is known to take a maximum value, that is, a peak waveform, at which the wave height becomes a certain level or more.

  Returning to FIG. 5, in step S <b> 6, the walking period specifying unit 73 specifies the walking period in the acceleration data acquisition period. In general, the acceleration data acquisition period includes a period during which the patient 10 is walking and a period during which the patient 10 is not walking. Examples of the period during which walking is not performed include, for example, a period from the start of acquisition of acceleration data until the patient 10 starts to walk, and a period from the end of walking of the patient 10 to the end of acquisition of acceleration data. A period during which the patient 10 temporarily stops walking while walking can be cited. On the other hand, if the analysis target includes acceleration data during a period when walking is not performed, correct analysis cannot be performed. Therefore, here, before performing the analysis process, the walking period is specified in the acceleration data acquisition period, and the acceleration data in the walking period is determined as an analysis process target.

  Generally, as a method for specifying the walking period, the following method can be considered.

  The first walking period specifying method is a method in which the operator 11 manually specifies a period considered as a walking period by looking at an acceleration waveform, and specifies the specified period as a walking period.

The second walking period specifying method obtains a feature amount representing the magnitude of acceleration generated in the patient 10 at each sampling time, and from the time when this feature amount becomes equal to or greater than a predetermined threshold to the time when the feature amount becomes equal to or less than the threshold Is specified as the walking period. As the feature amount representing the magnitude of the acceleration, for example, the sum of squares of the acceleration components a _x , a _y , and a _z in each direction from which the component of the gravitational acceleration g is removed can be considered.

FIG. 8 is a diagram in which the acceleration waveform W _x , W _y , W _z generated based on the sample acceleration data is overlapped with the walking period R ′ determined as the analysis target.

Returning to FIG. 5, in step S _< b> 7, the step reference time detection unit 74 has a plurality of step reference times tb _j each corresponding to a step-by-step advance operation of the patient 10 based on the acceleration waveform in the walking period to be analyzed. Is detected. The detection method according to this example will be described below after describing a generally considered detection method.

In general, as a method of detecting a step reference time, for example, by detecting the vertical acceleration waveform W peak at _z (peak) waveform and the particular waveform having periodicity such as waveform pattern, the step reference time tb A method of detecting _j can be considered.

FIG. 9 is a diagram showing the relationship between the vertical acceleration waveform _Wz and the walking phase. As a method for detecting the step reference time tb _j , for example, a local maximum value exceeding a predetermined threshold is specified in the vertical acceleration waveform _Wz , and the time corresponding to the local maximum value is recognized as the time of landing, Is detected as the step reference time tb _j .

  This method has a simple algorithm and is easy to understand.

  However, since the determination is made only by the acceleration threshold determination, the specific accuracy is limited. For example, if the body of the patient 10 is moving by a motion different from the one-step forward motion during walking, some acceleration may occur and erroneous detection may occur. Also, for example, the detection timing (timing) varies depending on the threshold setting. In addition, for example, when the peak value of the peak waveform synchronized with the one-step forward operation in the acceleration waveform has weakened in a single shot, a detection failure occurs, or conversely, the operation differs from the one-step forward operation. False detection occurs when a relatively strong peak waveform appears. In addition, for example, since the magnitude of acceleration generated during operation varies depending on the age, physique, and degree of gait disorder of the patient, the detection accuracy varies depending on the patient 10.

  Therefore, in this example, the step reference time detection unit 74 detects the step reference time using a method devised so that the step reference time can be detected with high accuracy. Hereinafter, the functional configuration of the step reference time detection unit 74 and the step reference time detection process will be described.

  FIG. 10 is a functional block diagram showing a functional configuration of the step reference time detection unit 74. As shown in FIG. 10, the step reference time detection unit 74 includes an acceleration waveform reading unit 741, a walking synchronization waveform generation unit 742, a one-step section specifying unit 743, a vertical acceleration reflected waveform generation unit 744, and an intra-section maximum value. A specific unit 745 and a saddle landing recognition unit 746 are provided.

  The walking synchronization waveform generating unit 742 and the one-step section specifying unit 743 are examples of specifying means in the invention, and the vertical acceleration reflected waveform generating unit 744, the intra-section maximum value specifying unit 745, and the saddle landing recognition unit 746 are in the invention. It is an example of a detection means.

  FIG. 11 is a flowchart showing the flow of the step reference time detection process.

In step S 71, the acceleration waveform reading unit 741 reads the longitudinal acceleration waveform W _y and the vertical acceleration waveform W _z from the storage unit 312.

Acceleration waveform reading unit 741, further, a waveform portion of the read walking period to be analyzed of the longitudinal acceleration waveform W _y and vertical acceleration waveform W _z, longitudinal acceleration waveform W _y ', respectively, vertical direction Cut out as an acceleration wave W _z ′.

FIG. 12 is an enlarged view of the longitudinal acceleration waveform W _y ′ and the vertical acceleration waveform W _z ′ based on the sample acceleration data. The horizontal axis represents time t (relative value), and the vertical axis represents the longitudinal acceleration component a _y and the vertical acceleration component a _z (relative value).

In step S72, the walking synchronization waveform generation unit 742 generates a walking synchronization waveform. The walking synchronization waveform is a waveform in which a predetermined pattern is repeated substantially synchronously with the operation of each step during walking. According to the applicant's experimental results, it has been confirmed that such a waveform is obtained as a waveform representing a linear combination of the longitudinal acceleration component a _y and the vertical acceleration component a _z . Therefore, here, based on the acceleration component a _y in the longitudinal acceleration waveform W _y ′ obtained in step S71 and the acceleration component a _z in the vertical acceleration waveform W _z ′ obtained in step S71, the following equation is obtained. A value B is obtained by the above, and a waveform WB representing the time change is generated as a walking synchronization waveform.
(Equation 1)
B = denoise (α · a _y + β · a _z )

  Here, denoise () is a function for reducing noise and is a function for mainly reducing high-frequency components. As such a function, a function corresponding to a Gaussian filter or the like can be considered. Α and β are coefficients, both of which are about 1 to 3.

  FIG. 13 is a diagram showing the walking synchronization waveform WB generated based on the sample acceleration data.

  In step S <b> 73, the one-step section specifying unit 743 specifies the one-step section K. The one-step section K is a time section corresponding to a one-step motion in walking. There are various methods for specifying the one-step section K, but the basic idea is to find a predetermined waveform pattern or waveform condition corresponding to the one-step operation in the walking synchronization waveform, and time corresponding to the waveform pattern or waveform condition. A specific range is specified as a one-step section K. In order to detect a time corresponding to a specific phase in walking, the one-step section K needs to be a section that clearly includes the time. In this example, since the time of the landing HC is detected, the one-step section K needs to be a section including the timing of the landing HC.

  Here, as shown in FIG. 14, in the walking synchronization waveform WB, the section between the time at which the minimum value is taken and the time at which the next minimum value is taken is specified as a one-step section K. Of course, when the walking synchronization waveform is a waveform representing a time change of −B, the interval between the time at which the maximum value is taken and the time at which the next maximum value is taken is a one-step interval K. As specified. Alternatively, the walking synchronization waveform WB may be approximated by a low frequency curve, and a section corresponding to a waveform portion having a value larger than the approximate curve may be specified as the one-step section K.

In step S74, the vertical acceleration reflected waveform generation unit 744 generates a vertical acceleration reflected waveform WJ _z '. The vertical acceleration reflected waveform WJ _z ′ is a waveform in which the vertical acceleration component a _z of the patient 10 is reflected, and can be said to be a waveform representing a time change of the vertical acceleration reflected component J _z ′. Here, the vertical acceleration reflected waveform WJ ′ is a waveform in which an increase in acceleration in the vertical direction appears as a change to the positive side. The vertical acceleration reflected waveform WJ ′ may be a waveform that represents the vertical acceleration component a _z in the same time as the walking synchronization waveform, or the amount calculated based on the vertical acceleration component a _z is the walking synchronization waveform. It may be a waveform represented by the same time. For example, the calculated amount is calculated by an arithmetic expression including a term of the vertical acceleration component _az . Further, for example, the amount to be calculated is calculated by an arithmetic expression including a term obtained by multiplying a term obtained by second-order time differentiation of the vertical acceleration component a _z to the kth power (k ≧ 1) in order to emphasize the change in acceleration. Here, as the vertical acceleration reflected waveform WJ _z ′, a waveform obtained by applying a filter for reducing a high-frequency component to the vertical acceleration waveform W _z ′ representing the temporal change of the vertical acceleration component a _z is used.

FIG. 15 is a diagram showing the walking synchronization waveform WB and the vertical acceleration reflected waveform WJ _z ′ generated based on the sample acceleration data on the same time axis.

In step S75, the intra-section maximum value specifying unit 74 specifies the intra-section maximum value _Mk for each one-step section K. The intra-section maximum value M _k is the maximum value in the one-step section K in the vertical acceleration reflected waveform WJ _z ′. It can be considered that there is always one anchor landing HC in one step section K. Further, it is known that the landing HC appears as a local peak waveform in the vertical acceleration reflected waveform WJ _z ′. Therefore, the time corresponding to the intra-section maximum value M _k can be associated with the time of the landing HC. If the vertical acceleration reflected waveform WJ _z ′ is a waveform in which the increase in the upward acceleration in the vertical direction appears as a change to the negative side, the minimum value in the section is specified and the minimum value in the section is supported. Is associated with the time of the landing HC.

FIG. 16 is a diagram showing the intra-section maximum value M _k specified for each one-step section K in the vertical acceleration reflected waveform WJ _z ′. In the figure, the intra-section maximum value M _k is indicated by a circle.

In step S76, the saddle landing recognition unit 746 recognizes the time corresponding to the intra-section maximum value _Mk specified in step S75 in association with the time of the saddle landing HC. Here, the time of the landing HC is set as the step reference time.

  FIG. 17 is a diagram illustrating the time of the landing HC detected as the step reference time. In the figure, the step reference time, that is, the time of landing is indicated by a square mark.

  As the walking synchronization waveform, a waveform different from the above waveform WB can be used. According to the experiment result of the present applicant, for example, a waveform generated by the following method can be used.

  First, a speed waveform is obtained by time integration processing of a lateral acceleration component, a longitudinal acceleration component, or a vertical acceleration component. Next, the linear component and the low frequency component are removed from the waveform of the speed. The waveform obtained in this way is time-integrated again to obtain a position (displacement) waveform. Then, a moving average process is performed on the waveform at the position with a time width corresponding to one step cycle or two step cycles as an average width, that is, a window width, and a low frequency component of the waveform at the position is obtained The trend is removed by subtracting the low frequency component from the waveform at the original position.

  Here, the one-step cycle is, for example, a time corresponding to the time between the landing of one foot and the landing of the other foot. The two-step cycle is a time corresponding to, for example, the time between the landing of one foot and the landing of the same one foot through the landing of the other foot.

  The waveform obtained in this manner is known to be a waveform that is very well synchronized with the operation of each step during walking, and is suitable as a walking synchronization waveform.

  Note that the removal of the linear component and the removal of the low-frequency component may be omitted depending on circumstances. The removal of the linear component may be performed at any stage as long as it is after the generation of the velocity waveform and before the trend removal using the moving average. The removal of the low-frequency component may be performed at any stage as long as it is after the acceleration waveform is identified and before the trend removal using the moving average. However, since it is preferable to remove unnecessary components at the earliest possible stage, it is desirable to carry out in the above order. Further, the time width corresponding to the one-step cycle or the two-step cycle may be obtained from, for example, the waveform cycle obtained by Equation 1 above, or may be obtained by applying an autocorrelation function to the waveform at the position.

  As described above, according to the method of specifying the time interval corresponding to each step in the walking synchronization waveform based on the characteristics of the waveform and specifying the time corresponding to the maximum value in the interval in the acceleration reflection waveform, the acceleration reflection waveform Detect even if the peak value of the peak waveform corresponding to the one-step operation varies in each step, or the peak waveform accompanying the operation different from the one-step operation appears close to the peak waveform corresponding to the one-step operation Leakage and false detection can be suppressed, and the time of landing can be recognized with high accuracy. As a result, for example, the accuracy of the acceleration component for each step of the patient 10 can be improved when observing the waveform or analyzing the waveform.

  Returning to FIG. 5, in step S <b> 8, the saddle landing left / right determination unit 75 determines whether each saddle landing recognized in step S <b> 7 is due to the left or right foot. In the following, after explaining a generally considered discrimination method, a discrimination direction according to this example will be explained.

Generally, as a method for determining the left and right foot heel landing, for example, in the lateral direction acceleration waveform W _x, detects a characteristic waveform shape one step characteristic waveform shape and the right foot on the step of the left foot Thus, a method of identifying a time corresponding to one step of the left foot and a time corresponding to one step of the right foot and comparing these times with the time of the landing HC can be considered.

  This method is easy to use because it can use a waveform that is already acquired and has a simple idea.

However, the left-right acceleration waveform W _x often shows a more complex waveform than the acceleration waveforms in other directions, and a characteristic waveform shape is found from such a waveform, and the time corresponding to one step of the left foot and the right foot It is not easy to specify the time corresponding to one step.

On the other hand, according to the experiment results of the present applicant, in the waveform L _x in the horizontal direction obtained by second-order integration of the waveform of the horizontal acceleration component a _x , there are noise components and components unnecessary for analysis. When properly removed, it has been confirmed that one step on the right foot and one step on the left foot appear fairly accurately corresponding to the waveform portion that is convex upward and the waveform portion that is convex downward.

  Therefore, in this example, the saddle landing left / right discriminating unit 75 discriminates the right / left of the saddle landing using the following method devised so that the right and left of the saddle landing can be accurately discriminated.

  Hereinafter, the functional configuration of the saddle landing left / right determination unit 75 and the saddle landing left / right determination processing will be described.

  FIG. 18 is a diagram illustrating a functional configuration of the landing landing left / right determination unit 75. As shown in FIG. 18, the saddle landing left / right determination unit 75 includes an acceleration waveform reading unit 751, a left / right position waveform generation unit 752, and a left / right determination unit 753.

  FIG. 19 is a flowchart of the landing right / left discrimination process.

At step S81, the acceleration waveform reading unit 751 identifies reads the lateral direction acceleration waveform W _x from the storage unit 312.

In step S82, the acceleration waveform reading unit 751 further cuts out the waveform portion of the walking period to be analyzed from the specified lateral acceleration waveform W _x as the lateral acceleration waveform W _x ′.

FIG. 20 is an enlarged view of the lateral acceleration waveform W _x ′ based on the sample acceleration data. The horizontal axis indicates the time t in the walking period determined as an analysis target as a relative value, and the vertical axis indicates the left-right acceleration component a _x as a relative value.

In step S83, the left-right position waveform generation unit 752 performs time integration processing on the left-right acceleration waveform W _x ′ to obtain a left-right speed waveform V _x .

In step S84, the left and right position waveform generator 752, relative to the horizontal direction of wave speed V _x, performs a process of removing a linear component. To remove the linear component, for example, a method of approximating (linear approximation) the waveform based on the data with a linear function and subtracting the component of the linear function approximated from the original waveform is used. Here, the main purpose of removing the linear component is to remove an offset component of the waveform before integration, and to eliminate an integration error caused by digital sampling.

In step S85, the left-right position waveform generation unit 752 performs a process of removing the low-frequency component on the left-right velocity waveform V _x ′ obtained in step S84. For example, a high pass filter or a low cut filter is used to remove the low frequency component. These filters are set so as to remove, for example, components below a predetermined frequency that is 0.4 hertz (Hz) or less. The predetermined frequency is, for example, 0.2 hertz. Here, the main purpose of removing low frequency components is to remove errors caused by numerical integration. In numerical integration, low frequency noise is amplified with an intensity proportional to the inverse of frequency ω. For this reason, when the frequency ω is in the vicinity of 0, there is a possibility that large noise that cannot be removed even by removal of the linear component or removal of a trend described later may occur. Forcibly removing this with a high-pass filter is thought to improve the calculation accuracy.

In step S86, the left-right position waveform generation unit 752 further performs time integration processing on the left-right speed waveform V _x ″ from which the linear component and the low-frequency component have been removed, and the left-right position (displacement). The waveform _Lx is obtained.

In step S87, the left and right position waveform generator 752, the waveform L _x position of the obtained lateral direction at step S86, the low of the waveform L _x of the position by performing the moving average process in the time axis direction A frequency component is obtained, and a process of subtracting the low frequency component from the waveform L _x at the original position is performed. The window width in the moving average process, that is, the time width to be averaged is, for example, a time width substantially corresponding to a walking cycle (two-step cycle). The time width corresponding to the walking cycle is, for example, a time corresponding to a period from the landing of one foot to the landing of the other foot through the landing of the other foot. By this processing, a waveform L _x ′ at the horizontal position from which the trend is removed is obtained. Here the main purpose of removing low-frequency components obtained by the moving average, from the waveform L _x position in the horizontal direction, by removing the waveform components with real gait cycle different from the cycle of the patient 10, walking period Is to extract only waveforms having the same period as possible. In actual walking, only specific motions synchronized with the walking cycle are not regularly repeated, but the robot moves forward including motions that do not synchronize with the walking cycle, for example, motions that swing irregularly from side to side and back and forth. Therefore, the waveform of the position includes a waveform that is synchronized with a walking cycle that occurs with walking of the patient 10 and a waveform that is not synchronized with the walking cycle. The waveform to be handled here is only the waveform synchronized with the walking cycle. Therefore, waveforms that are not synchronized with the walking cycle are eliminated as much as possible.

Figure 21 is a comparison of the frequency distribution DL _x, the frequency distribution DL _x 'in' waveform WL _x 'of the position in the lateral direction after detrending L _x in the waveform WL _x position L _x in the lateral direction of the front detrending It is a figure which shows an example. The horizontal axis represents the frequency, and the vertical axis represents the Fourier component norm.

  The vibration of 1 Hz or less in acceleration is noise mainly due to the translational motion of the center of gravity of the patient 10, and is not essential information for estimating the walking motion. By removing the trend using the moving average processing here, it is possible to strongly attenuate a long-period vibration of 1 Hz or less. That is, it is possible to obtain a waveform at a processed position from which components unnecessary for analysis other than the target component are removed.

FIG. 22 is a diagram illustrating the processed waveform WL _x ′ in the left-right direction and the various waveforms obtained in step S7 on the same time axis.

It can be seen that the waveform WL _x ′ in the left-right direction obtained in this way is a waveform in which the waveform portion protruding upward or downward corresponds well in time with the movement of one step during walking. In addition, the waveform is a waveform that can determine whether the one-step operation corresponding to the waveform portion is due to the left or right foot depending on whether the shape of the waveform portion is convex upward or downward. I understand that.

  Note that the process of removing the linear component in step S84 and the process of removing the low-frequency component in step S85 are not essential processes. This is because it is considered that the linear component and the low frequency component can be removed to some extent by the process of removing the trend in step S87. However, it is not possible to remove all unnecessary linear components and low-frequency components only by removing the trend. Therefore, in order to improve the extraction accuracy of the waveform synchronized with the walking cycle, it is naturally better to remove the linear component and the low frequency component.

  Further, the timing for performing the process of removing the linear component and the timing of performing the process of removing the low frequency component are not limited to the timing of this example.

  For example, the process of removing the linear component may be performed at any timing as long as it is between the process of generating the velocity waveform and the process of removing the trend using the moving average. That is, the process of removing the linear component can be a process of removing the linear component in the waveform obtained by the process after the process of generating the velocity waveform or the process of generating the position waveform.

  Further, for example, the process of removing the low frequency component may be performed at any timing after the process of specifying the acceleration waveform and before the process of removing the trend using the moving average. That is, the process of removing the low frequency component is to remove the low frequency component in the waveform obtained by the process after the process of specifying the acceleration waveform, the process of generating the velocity waveform, or the process of generating the position waveform. It can be a process.

  However, from the viewpoint of suppressing the diffusion and integration of errors due to noise components, or increasing the calculation efficiency by reducing the value to be handled, the processing for removing linear components and the processing for removing low-frequency components are as much as possible. It is desirable to do it at an early stage. Therefore, as in this example, it is one of the preferred examples that these processes are performed before the time waveform is subjected to the time integration process.

In step S88, the left / right discriminating unit 753 discriminates the left and right of each recognized landing based on the waveform L _x ′ in the left / right direction obtained in step S87.

  FIG. 23 is a diagram illustrating an example of the right / left discrimination of the saddle landing.

As described above, here, the positive and negative acceleration components in the left-right direction are positive on the right side. Therefore, in the waveform WL _x ′ in the left-right direction, the waveform portion that is closer to the positive side, that is, convex upward, corresponds to the right foot step, and the waveform portion that is closer to the negative side, that is, downward, corresponds to the left foot step. . Therefore, in the waveform WL _x ′ at the position in the left-right direction, the saddle landing HC that corresponds temporally to the waveform portion that protrudes upward is determined as the right foot saddle landing. Further, in the waveform at the position in the left-right direction, the saddle landing HC that corresponds temporally to the waveform portion that protrudes downward is determined as the left foot saddle landing.

Various discriminating algorithms can be considered. For example, the following algorithm can be considered.
Step T1: The one-step section K including the time of the landing HC to be determined is determined as a section [t ₀ , t ₁ ] for determining unevenness in the waveform WL _x ′ at the position in the left-right direction.
Step T2: A function f (t) representing a straight line connecting the coordinates (t ₀ , L _x ′ (t ₀ )) and the coordinates (t ₁ , L _x ′ (t ₁ )) is determined.
Step T3: Integrate f (t) −L _x ′ (t) in the interval [t ₀ , t ₁ ] to determine its positive / negative.
Step T4: If the integral value is positive, it is determined that the waveform portion in the one-step section K of the waveform WL _x ′ in the left-right direction is convex downward, and the heel landing HC is the landing of the left foot. If the integral value is negative, it is determined that the waveform portion in the one-step section K of the waveform WL _x ′ in the left-right direction is convex upward, and that the heel landing HC is the landing of the right foot.

For example, the following algorithm can also be considered.
Step U1: The waveform WC of the low frequency component (component close to a straight line) of the waveform WL _x ′ in the left-right direction position is obtained by moving average processing or the like. The window width in the moving average process is set to about several times the one-step cycle.
Step U2: It is determined whether the coordinate L _x ′ (t _HC ) of the horizontal position at the time t _HC of the anchor landing HC to be determined is on the positive side or the negative side of the waveform WC.
Step U3: If the coordinate L _x ′ (t _HC ) is on the negative side of the waveform WC, the waveform portion of the waveform WL _x ′ in the left-right direction is convex downward with respect to the landing HC, and the landing HC is Judged to be landing. If the coordinate L _x ′ (t _HC ) is on the positive side of the waveform WC, the waveform portion corresponding to the saddle landing HC of the waveform WL _x ′ in the left-right direction is convex upward, and the saddle landing HC is the landing of the right foot It is determined that

  Accordingly, the plurality of step reference times specified in step S7 can be specified separately for the left foot step reference time and the right foot step reference time.

  In this way, the time interval corresponding to one step of the left foot and the time interval corresponding to one step of the right foot are identified based on the waveform characteristics in the waveform of the position in the left and right direction, and the time of the detected landing on the foot According to the method of discriminating the left and right sides of the landing depending on which section is left or right, it has been confirmed that the waveform shape corresponding to one step of the left foot and the waveform shape corresponding to one step of the right foot appear stably. The section corresponding to one step of the left foot and the section corresponding to one step of the right foot can be obtained with high accuracy using the existing waveform, and the right and left of the landing can be distinguished with high accuracy. As a result, it is possible to improve the accuracy in quantitatively and objectively evaluating the difference and balance between the left and right foot movements in the walking movement of the patient.

  Returning to FIG. 5, in step S <b> 9, the step time graph generation unit 76 generates a graph representing a time-series change of the step time, which is the time taken for the step-by-step advance operation of the patient 10, and relates to the step time. Several feature quantities are calculated. The step time can be defined in various ways. In this example, the step time is defined using the step reference time, which corresponds to the step reference time corresponding to one step forward operation and the next step forward operation. It is determined as the time between the step reference time.

  Hereinafter, the functional configuration of the step time graph generation unit 76 and the step time graph generation processing will be described.

  FIG. 24 is a functional block diagram showing a functional configuration of the step time graph generation unit 76. As shown in FIG. 24, the step time graph generating unit 76 includes a step time specifying unit 761, a graph generating unit 762, and a feature amount calculating unit 763.

  FIG. 25 is a flowchart showing the flow of the step time graph generation process.

In step S91, as shown in FIG. 26, the step time specifying unit 761 determines the left step time T _Li and the right step time T _Ri based on the detected step reference time tb _j and the left / right discrimination result of the foot. And specify. The left step time T _Li is a step time corresponding to one step forward movement by the left foot of the patient 10, and is a time from the step reference time corresponding to the left foot landing HC to the step reference time corresponding to the right foot landing HC. It is. The right step time T _Ri is a step time corresponding to one step forward movement by the right foot of the patient 10, from the step reference time corresponding to the right saddle landing HC to the step reference time corresponding to the left foot saddle landing HC. Is the time.

The difference between the identified left step time T _L and the right step time T _R is the difference between one step of advance movement by one step of the forward movement and the right foot by the left foot is reflected. Therefore, the left-right difference of the forward movement of the foot of the patient 10, it is possible to evaluate and quantify these left step time T _L and the right step time T _R.

In step S92, the graph generation unit 762 generates a graph representing time changes of the obtained plurality of step times _Tj .

In this example, a bar graph is generated with the step number j on the horizontal axis and the step time (seconds) on the vertical axis. The step number j is a number representing the time series order of the step time T. In this example, the left step time T _L and the right step time T _R are changed in color or pattern (pattern, pattern) so that the left step time T _L and the right step time T _R can be easily distinguished from each other. Change and draw.

  27 and 28 are diagrams illustrating an example of the generated graph G. FIG. FIG. 27 is a sample by normal walking, and FIG. 28 is a sample by walking with the right knee fixed by a metal rod. The graph G generated here is not limited to a bar graph as in this example, but may be a line graph, a spline curve graph using spline interpolation, or the like. Further, a line graph or a splice curve graph may be superimposed on the bar graph.

In normal walking, as can be seen from FIG. 27, there is no significant difference between the left step time T _L and the right step time T _R. However, in the walking fixing the right knee, as can be seen from Figure 28, seen that the right step time T _R is longer than the left step time T _L. This means that the forward movement by the right foot is slowed by fixing the right knee with the metal rod, and as shown by the arrow Y1 in FIG. 28, the left step time _TL and the right step time. and a relatively large difference exists between the T _R, the degree of the difference can be confirmed to have been quantified.

In FIG. 28, the difference between the left step time T _L and the right step time T _R is much larger than the degree of variation in the step time due to the same foot, or due to a large step time that occurred by chance. It can be confirmed at a glance that there is reproducibility.

Further, in normal walking, as can be seen from FIG. 27, the step time T _j hardly fluctuates. However, in walking with the right knee fixed with a metal rod, as can be seen from FIG. 28, the step time T _j is longer in the latter half compared to the first half in the walking period (arrow Y2). This indicates that the forward movement is slowed while walking, and represents fatigue due to the right knee being fixed and difficult to walk.

Thus, in the graph G representing the time change of the step time as described above, the left step time T _L and the right step time T _R that are alternately arranged in time series order are visually represented. Therefore, according to such a graph G, the left-right difference of the step time T _j can be intuitively grasped in the entire walking period or in a partial local period. You can easily understand the balance of the forward movement of your feet. Further, according to such a graph G, it is possible to intuitively grasp the variation of the step time T _j , that is, the stability, both as a whole and as a left and right separately, and whether the stable walking as a whole can be performed. It can be easily understood whether or not an unstable forward movement is included in either leg. In addition, according to such a graph G, the temporal change in the step time T _j can be intuitively grasped as a whole or left and right as a whole. It is possible to easily understand how a simple walking, the balance of the forward movement of the left and right feet, and the step time T _j are not extended due to fatigue. Then, the operator 11 can explain the evaluation result of the walking motion while showing the graph G to the patient 10 so that the patient 10 can easily and in detail understand the evaluation result of his / her walking motion.

  Returning to FIG. 25, in step S <b> 93, the feature amount calculation unit 763 calculates various feature amounts related to the step time for evaluation of the walking motion of the patient 10.

In this example, a feature quantity that reflects the degree of difference between the left step time T _L and the right step time T _R is calculated as the first feature quantity. In general, the greater the degree of difference between the left step time T _L and the right step time T _R, often balance the forward operation by the forward movement and the right foot by the left foot of the patient 10 are more collapsed. Conversely, as the degree of difference between the left step time T _L and the right step time T _R is small, it is often balanced the forward operation by the forward movement and the right foot by the left foot of the patient 10. Therefore, according to such a first feature amount, it is possible to quantitatively evaluate the balance between the forward motion by the left foot and the forward motion by the right foot of the patient 10 based on the size. For example, when the first feature value takes a larger value as the degree of difference between the left step time T _L and the right step time T _R is larger, the smaller the first feature value is, the more the patient 10 moves forward. It can be evaluated that the left and right balance is good.

  In this example, a feature amount that reflects the degree of variation in step time is calculated as the second feature amount. In general, the greater the degree of variation in the step time, the worse the stability of the forward motion of the patient 10 is. Conversely, the smaller the degree of variation in the step time, the better the stability of the forward movement of the patient 10 is. Therefore, according to the second feature amount, the stability of the forward movement of the patient 10 can be evaluated based on the size. For example, when the second feature amount takes a larger value as the degree of variation in the step time is larger, it can be evaluated that the stability of the forward movement of the patient 10 is higher as the second feature amount is smaller.

  In this example, as the third feature amount, a feature amount that reflects the degree of difference between the temporally forward step time and the temporally backward step time of the entire walking period is calculated. In general, the larger the degree of difference between the step time closer to the front and the step time closer to the rear, the longer the step time becomes because the patient 10 gets tired during the walking exercise. Conversely, the smaller the degree of difference between the time step forward and the time step backward, the more often the patient 10 does not get tired during the walking exercise and the step time is not prolonged. Therefore, according to the third feature amount, the ease of fatigue of the patient 10 can be quantitatively evaluated based on the size. For example, when the third feature amount takes a larger value as the degree of difference between the time step closer to the front and the step time closer to the time is larger, the smaller the third feature amount, the more the patient 10 has. Can be evaluated as less fatigued.

  Returning to FIG. 5, in step S <b> 10, the display control unit 310 controls the display unit 302 to display the graph G on the screen.

  As described above, according to the present embodiment, the operator 11 can grasp in detail the movement of the patient 10 while walking based on the analysis result of the acceleration data, and can create, for example, an effective walking training plan.

  Further, according to the present embodiment, in particular, the configuration of the step reference time detection unit 74 and the step reference time detection process recognize the timing of the landing of the patient 10 with high accuracy, and the reference of the forward movement operation for each step. Can be detected. By using such a reference time, it is possible to extract acceleration data when one step or a plurality of steps of the patient 10 is performed, and to measure the time required for the operation. It is possible to perform an analysis focusing on the operation of each step. Therefore, this embodiment is effective for objective evaluation of the walking motion of the patient 10.

  In addition, according to the present embodiment, in particular, the configuration of the saddle landing left / right discriminating unit 75 and the saddle landing left / right discriminating process make it highly accurate whether each of the recognized saddle landings of the patient 10 is due to the left or right foot. Can be determined. By using such a discrimination result, the left and right balance in the walking motion of the patient 10 can be quantitatively and objectively evaluated. As a result, appropriate advice for improving the left and right balance is given to the patient 10. It can also be provided.

  The invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the invention.

  For example, in the present embodiment, the saddle landing time is detected as the step reference time. However, the present invention is not limited to this, and the foot landing time or the toe kicking time may be detected.

  In addition, for example, the present embodiment is an example in which the invention is applied to a person's walking motion, but the invention can also be applied to another movement motion of a person, for example, a running motion of a person.

  Further, for example, the present embodiment is a mobile motion analysis device that analyzes acceleration data obtained from an acceleration sensor attached to a person as described above, but a program for causing a computer to function as such a device is also used. It is one of the embodiments of the invention.

1 Gait Analysis System 10 Patient 11 Operator 2 Acceleration Sensor Module 21 Processor 22 Acceleration Sensor 23 Memory 24 Communication I / F
25 Battery 201 Acceleration sensor unit 202 Sampling unit 203 Transmitting unit 3 Walking analysis device 31 Processor 32 Display 33 Operation unit 34 Memory 35 Communication I / F
36 battery 301 operation unit 302 display unit 303 patient information reception unit 304 reception unit 305 acceleration data acquisition control unit 307 acceleration data analysis unit 310 display control unit 312 storage unit 41 database 42 storage medium 71 acceleration component calculation unit 72 acceleration waveform generation unit 73 Walking period identification unit 74 Step reference time detection unit 741 Acceleration waveform reading unit 742 Walking synchronization waveform generation unit 743 One step segment identification unit 744 Vertical acceleration reflected waveform generation unit 745 Segment maximum value identification unit 746 踵 Landing recognition unit 75 踵 Landing left / right discrimination unit 751 Acceleration waveform reading unit 752 Left / right position waveform generation unit 753 Left / right determination unit 76 Step time graph generation unit 761 Step time specification unit 762 Graph generation unit 763 Feature amount calculation unit

Claims (11)

In the first waveform generated based on the data representing the acceleration during the moving motion of the person obtained using the acceleration sensor, a specifying means for specifying a time interval corresponding to one step;
In the second waveform representing the vertical acceleration during the movement of the person obtained based on the data in the same time as the first waveform, either the maximum value or the minimum value of each of the sections And a detecting means for detecting the time corresponding to the time when the foot has landed. In the first waveform generated based on the data representing the acceleration during the moving motion of the person obtained using the acceleration sensor, a specifying means for specifying a time interval corresponding to one step;
In the second waveform representing the amount calculated based on the vertical acceleration during the movement movement of the person based on the data in the same time as the first waveform, the maximum value of each of the sections And a detecting means for detecting a time corresponding to one of the minimum values as a time when the foot has landed.   The mobile motion analysis device according to claim 2, wherein the calculated amount is calculated by an arithmetic expression including a term of the vertical acceleration.   4. The mobile motion analysis device according to claim 2, wherein the calculated amount is calculated by an arithmetic expression including a term obtained by second-order differentiation of the vertical acceleration to a power of k (k ≧ 1). 5. .   The mobile motion analysis apparatus according to any one of claims 1 to 4, further comprising means for removing a high-frequency component from the second waveform.   6. The specifying means includes means for specifying the section based on a third waveform representing a linear combination of vertical acceleration and longitudinal acceleration obtained from the data. The movement analysis apparatus according to any one of the above.   The mobile motion analysis apparatus according to claim 6, wherein the specifying unit includes a unit that specifies, as the section, a time period corresponding to one of a maximum value and a minimum value in the third waveform.   The specifying means subtracts a waveform obtained by moving and averaging the waveform of the position from the waveform of the position obtained by second-order integration of the acceleration in the vertical direction, the longitudinal direction, or the horizontal direction obtained from the data. The mobile motion analysis apparatus according to any one of claims 1 to 5, further comprising means for specifying the section based on the waveform.   The mobile motion analysis apparatus according to any one of claims 1 to 8, wherein the mobile motion is walking.   A mobile motion analysis system comprising the acceleration sensor and the mobile motion analysis device according to any one of claims 1 to 9. A program for causing a computer to function as the mobile motion analysis apparatus according to any one of claims 1 to 9.