JP6367570B2 - Moving motion analysis apparatus and program - Google Patents

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 uses a patient's gait exercises to create a gait training program suitable for the patient and to understand the patient's recovery level. Is repeatedly evaluated.

  On the other hand, in recent years, an attempt has been made to measure acceleration generated during walking of a person using an acceleration sensor and to evaluate walking motion using the measurement result (see, for example, Patent Document 1, Abstract, etc.). ).

JP 2013-059489 A

  Generally, when an instructor such as a physical therapist evaluates a patient's walking motion, the instructor visually observes the patient's walking motion and evaluates it subjectively. For this reason, the evaluation results tend to vary depending on differences in the instructor's habits, proficiency level, evaluation timing, etc., making it difficult to objectively evaluate.

  Further, even in the existing walking evaluation technology using an acceleration sensor, a technology that enables objective evaluation of walking motion has not been established yet. In particular, it is considered useful to objectively evaluate walking motion, and it is useful to measure the time taken for a person's step-by-step forward movement, but a method for performing such measurement has not been established.

  Under such circumstances, there is a demand for a technique that enables an objective evaluation in a moving movement such as walking of a person.

The invention of the first aspect
Based on the acceleration data during the movement of the person obtained from an acceleration sensor attached to the person, the time required for one step for the one-step advance movement is specified for each one-step forward movement constituting the movement. Specific means to
There is provided a mobile motion analysis apparatus comprising: generation means for generating a graph representing a time-series change of the one-step required time based on a plurality of one-step required times specified by the specifying means.

The invention of the second aspect is
The moving motion analysis apparatus according to the first aspect is provided, wherein the generation means generates a graph representing a time-series change in the time required for one step by both feet of the person.

The invention of the third aspect is
The mobile motion analysis apparatus according to the second aspect, wherein the generation unit generates a graph in which the time required for one step by one foot of the person and the time required for one step by the other foot are alternately represented in the time axis direction. provide.

The invention of the fourth aspect is
From the first aspect, the one-step required time is a time from a reference time of one step forward movement by one person's foot to a reference time of one step forward movement by the other person's foot. A moving motion analysis apparatus according to any one of the three viewpoints is provided.

The invention of the fifth aspect is
The acceleration data includes information representing an acceleration component in the front-rear direction at each time of the person,
The mobile motion analysis apparatus according to the fourth aspect, wherein the specifying means detects a time corresponding to a peak waveform in a waveform representing a time change of the acceleration component in the front-rear direction as a reference time for the forward movement of one step of the person. I will provide a.

The invention of the sixth aspect is
The mobile motion analysis apparatus according to the fifth aspect, wherein the specifying unit detects a time corresponding to a rising position of the peak waveform as the reference time.

The invention of the seventh aspect
The mobile motion analysis apparatus according to any one of the first to third aspects is provided, wherein the one-step required time is a time corresponding to the person's single leg support period.

The invention of the eighth aspect
The moving motion analysis apparatus according to any one of the first to seventh aspects is provided, wherein the graph includes a bar graph.

The invention of the ninth aspect is
In the bar graph, the bar representing the time required for one step by one person's foot and the bar representing the time required for one step by the other person's foot are distinguished by a difference in color or pattern. A moving motion analysis apparatus according to eight aspects is provided.

The invention of the tenth aspect is
There is provided a mobile motion analysis apparatus according to any one of the first to ninth aspects, further comprising a calculation means for obtaining a feature amount related to the one-step required time.

The invention of the eleventh aspect is
The computing means has, as the feature amount, a first time representing a representative one-step required time of a plurality of one-step required times by the one foot and / or a representative of a plurality of one-step required times by the other foot. According to the tenth aspect of the present invention, there is provided a mobile motion analysis apparatus for obtaining a second time representing a typical one-step time.

The invention of the twelfth aspect is
The mobile motion analysis apparatus according to the eleventh aspect is provided, wherein the calculation means obtains a difference or ratio between the first time and the second time as the feature amount.

The invention of the thirteenth aspect is
The calculation means obtains, as the feature amount, a degree of variation of a plurality of one-step required times due to the one foot and / or a degree of variation of a plurality of one-step required times due to the other foot. A mobile motion analysis apparatus according to any one of the twelfth aspects from a viewpoint is provided.

The invention of the fourteenth aspect is
The computing means has, as the feature quantity, representative one-step required time in the time zone that is closer to the front during the moving motion and representative one step in the time zone that is later in the time than the moving motion. A moving motion analysis apparatus according to any one of the tenth to thirteenth aspects for obtaining a change amount with respect to a required time is provided.

The invention of the fifteenth aspect is
The amount of change is a typical one-step required time of one or more predetermined steps by one foot in the front time zone and the predetermined number of one foot by the one foot in the rear time zone. The mobile motion analysis apparatus according to the fourteenth aspect, which is the amount of change between the time required for one step and the time required for one step, is provided.

The invention of the sixteenth aspect is
The amount of change is a typical one-step required time of two or more predetermined steps required by both feet in the front time zone, and the predetermined number of both footes in the rear time zone. The mobile motion analysis apparatus according to the fourteenth aspect, which is the amount of change between the time required for one step and the time required for one step, is provided.

The invention of the seventeenth aspect is
A mobile motion analysis apparatus according to any one of the tenth to sixteenth aspects, comprising display control means for controlling display means to display the graph and the feature amount.

The invention of the eighteenth aspect is
An acceleration sensor attached to a person,
Specifying means for identifying a time required for one step of the forward movement of each step of the forward movement of each step constituting the movement based on acceleration data during the movement of the person obtained from the acceleration sensor; ,
There is provided a mobile motion analysis system comprising: generation means for generating a graph representing a time-series change in the time required for one step based on a plurality of time required for one step specified by the specifying means.

The invention of the nineteenth aspect is
Based on the acceleration data during the movement of the person obtained from an acceleration sensor attached to the person, the time required for one step for the one-step advance movement is specified for each one-step forward movement constituting the movement. And steps to
And generating a graph representing a time-series change of the one-step required time based on a plurality of one-step required times specified by the specifying means.

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

  According to the invention of the above aspect, it is possible to objectively evaluate a person's movement.

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. It is a figure which shows the waveform of the square sum of squares of the acceleration component of each direction from which gravity acceleration was removed obtained based on sample acceleration data. It is a functional block diagram which shows the functional structure of a walk period specific | specification part. It is a flowchart which shows the flow of a walk period specific process. It is a figure which shows the waveform showing the time change of the longitudinal / vertical acceleration feature-value calculated based on sample acceleration data. It is a figure which shows the waveform showing the time change of the binarization data D obtained based on sample acceleration data. It is an enlarged view of the waveform showing the time change of the binarized data D obtained based on sample acceleration data. It is a figure showing the waveform and threshold value TH which represent the time change of the binarized data variation | change_quantity (sigma) obtained 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 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 a _y based on sample acceleration data. It is a diagram showing a waveform representing the time variation of the variation .DELTA.a _y between adjacent time a _y. It is a diagram showing a waveform representing the time variation of the negative values deleted data values .delta.a _y 'of .delta.a _y. It is a diagram showing a waveform representing a time change of the smoothed-data-value S _y of .delta.a _y '. It is a graph showing a waveform representing the time variation of the squared value C _y of S _y. It is a diagram showing a waveform representing the time variation of the offset data OSD for C _y. Is a diagram showing a waveform representing the time variation of the C offset deleted data value C _y of _y '. It is a diagram showing a waveform representing a time change of the square value F _y of C _y '. It is a diagram showing a waveform representing the time variation of F normalized data values F _y of _y '. It is a diagram showing a waveform representing the time variation of 'variation [Delta] F _y between adjacent time' F _y. The detected step reference time is indicated by a black dot on the longitudinal acceleration waveform W _y ′. It is a figure which shows the relationship between the vertical acceleration waveform _Wz and a walk phase. It is a functional block diagram which shows the functional structure of a step waveform graph production | generation part. It is a flowchart which shows the flow of a step waveform graph production | generation process. It is a figure for demonstrating step time. It is a figure showing an example of the graph showing the time-sequential change of the step time by normal walking. It is a figure showing an example of the graph showing the time-sequential change of the step time by the walk which fixed the right knee with the metal stick.

  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. Here, the positive and negative acceleration components are positive on the right side, on the front side, and on the upper side.

  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 (time required for one step) and graphing the obtained step time.

  FIG. 4 is a functional block diagram showing a functional configuration of the acceleration data analysis unit 307. In order to realize the above function, the acceleration data analysis unit 307 includes an acceleration component specifying unit 71, an acceleration waveform generating unit 72, a walking period specifying unit 73, and a step reference time detecting unit 74, as shown in FIG. And a step time graph generation unit 75.

Based on the acquired acceleration data, the acceleration component specifying unit 71 specifies acceleration components a _x , a _y , and a _z in the horizontal direction, the front-rear direction, and the vertical direction of the patient 10 at each sampling time in the data acquisition period. . In this example, it is assumed that these acceleration components a _x , a _y , and a _z are calculated and specified 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.

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.

  Based on the generated acceleration waveform, 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 specified acceleration waveform during the walking period.

  Based on the detected step reference time, the step time graph generation unit 75 obtains a step time (time required for one step), which is a time required 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 75 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 the left and right (balance), and the like. 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 this example, the acceleration waveform generation unit 72 performs each time in a two-dimensional coordinate system K having two axes of an elapsed time (time) from the acceleration data acquisition start time and an acceleration component for each direction of the acceleration component. data points corresponding to the acceleration component a (i) at _{t i [a (i),} t i] to generate the acceleration waveform by plotting, respectively. The acceleration waveform is smoothed and smoothed as necessary to form 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 vertical acceleration waveform W _z , the peak waveform of the heel landing draws a relatively gentle mountain, while the peak waveform of the toe kick draws a relatively steep mountain.

  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.

  The first walking period specifying method is easy to use because the algorithm is very simple. However, it is necessary for the operator 11 to specify a period considered as a walking period.

  The second walking period specifying method is easy to use because the algorithm is simple and easy to understand. However, since the determination is made only by the acceleration threshold determination, the specific accuracy is limited.

FIG. 8 is a diagram showing a waveform representing a temporal change in the magnitude of acceleration obtained based on the sample acceleration data. The magnitude of the acceleration is obtained as a sum of squares of the acceleration components a _x , a _y , and a _z in each direction from which the gravitational acceleration g component is removed. For example, as can be seen from FIG. 8, even if the patient 10 is not walking (in this example, a period of time t = 20 to 23 seconds), if the patient 10 is moving, some acceleration occurs. It is conceivable that this period is erroneously detected as a walking period. In addition, for example, the detection timing changes depending on the threshold setting. In addition, for example, since the magnitude of acceleration generated during operation varies depending on the patient's age, physique, and the degree of gait disturbance, the detection accuracy varies among patients.

  Furthermore, during the walking period, not only the normal walking period in which normal walking is performed stably, but also the walking period at the turn-back point when walking back and forth on a straight road or the balance is lost during walking. Non-normal walking periods in which normal walking is not performed may also be included. However, if the target of the analysis process includes acceleration data for a non-normal walking period, a highly accurate analysis cannot be performed. In particular, when performing frequency analysis on acceleration data during walking of the patient 10, it is indispensable to accurately extract acceleration data during the normal walking period as a processing target.

  Therefore, in this example, the walking period specifying unit 73 specifies the walking period using a method devised so that only the normal walking period in which normal walking is stably performed can be specified with high accuracy. To do. Hereinafter, the functional configuration of the walking period specifying unit 73 and details of the walking period specifying process will be described.

  FIG. 9 is a functional block diagram showing a functional configuration of the walking period specifying unit 73. As illustrated in FIG. 9, the walking period specifying unit 73 includes a longitudinal / vertical acceleration feature amount calculation unit 731, a feature amount binarization processing unit 732, a binarized data variation amount calculation unit 733, and a variation amount threshold determination. A section 734 and a margin setting section 735.

  Hereinafter, the walking period specifying process by the walking period specifying unit 73 will be described.

  FIG. 10 is a flowchart showing the flow of the walking period specifying process.

In step S61, the longitudinal / vertical acceleration feature quantity calculation unit 731 calculates the longitudinal acceleration component a _y and the vertical acceleration component a _z for each sampling time as a feature quantity that gives important information as to whether or not walking. The longitudinal / vertical acceleration feature quantity Q _yz reflecting both components is calculated. In general, when walking is not performed, the vertical acceleration component _az tends to be significantly lower than the acceleration components in the other two directions, that is, the horizontal direction and the front-rear direction. Therefore, the vertical acceleration component a _z gives important information as to whether or not walking. However, when assuming walking of a patient with a bent waist, the longitudinal acceleration component a _y and the vertical acceleration component a _z tend to reverse when the sensor body is used as a reference. Further, the lateral acceleration component a _x often has a certain magnitude regardless of whether or not walking. Therefore, here, the longitudinal / vertical acceleration feature quantity Q _yz reflecting both the longitudinal acceleration component a _y and the vertical acceleration component a _z is calculated as information having a high ability to determine whether or not walking. . As the longitudinal / vertical acceleration feature quantity Q _yz , for example, the product or sum or weighted addition value of the longitudinal acceleration component a _y and the vertical acceleration component a _{z at} each sampling time can be considered. In this example, the product of the longitudinal acceleration component a _y and the vertical acceleration component a _{z at} each sampling time is calculated as the longitudinal / vertical acceleration feature quantity Q _yz .

FIG. 11 is a diagram illustrating a waveform representing a temporal change in the longitudinal / vertical acceleration feature quantity (a _y × a _z ) calculated based on the sample acceleration data.

When the patient 10 is performing normal walking, periodic strong vibrations are likely to occur in the body of the patient 10. Therefore, the longitudinal acceleration component a _y and the vertical acceleration component a _z when the patient 10 is walking normally are both crossed from positive (plus) to negative (minus) or negative. There is a strong tendency to vibrate greatly from to positive. On the other hand, when the patient 10 is not walking normally, random and weak movement is likely to occur in the body of the patient 10. Therefore, the longitudinal acceleration component a _y and the vertical acceleration component a _z when the patient 10 is not performing normal walking are less likely to cross 0 and have a strong tendency to continue with weak changes. Therefore, the product of the longitudinal acceleration component a _y and the vertical acceleration component a _z frequently repeats a relatively large value and 0 when the patient 10 is walking normally. When normal walking is not performed, values other than 0 change relatively slowly.

Returning to FIG. 10, in step S62, the feature quantity binarization processing unit 732 binarizes the longitudinal / vertical acceleration feature quantity Q _yz by threshold determination to obtain a binarized data value D. That is, when the longitudinal / vertical acceleration feature quantity Q _yz exceeds a predetermined threshold value at each sampling time, the data at that time is converted to 1, and the longitudinal / vertical acceleration feature quantity Q _yz is within the predetermined threshold value. Sometimes the data at that time is converted to zero.

  FIG. 12 is a diagram showing a time change of the binarized data value D obtained based on the sample acceleration data. FIG. 13 is a partially enlarged view when this is enlarged in the time axis direction.

In the time change of the binarized data value D obtained in this way, when the patient 10 is walking normally, switching between 0 and 1 frequently occurs, and the patient 10 is walking normally. When not, 0 is continuously generated. Although the above threshold can be set arbitrarily, in this example, when the acceleration data value is 128, the threshold is set to ± 1000 on a scale representing 1 g (gravity acceleration). That is, when the longitudinal / vertical acceleration feature quantity Q _yz exceeds +1000 and below −1000, 1 is taken, and when the longitudinal / vertical acceleration feature quantity Q _yz is −1000 or more and +1000 or less, 0 is taken. Try to take.

  Returning to FIG. 10, in step S <b> 63, the binarized data variation amount calculation unit 733 has a time range having a predetermined time width based on the time for each sampling time in the time change of the binarized data D. The binarized data variation amount σ representing the degree of variation of the binarized data D included in the data is calculated.

  Various definitions can be considered for the binarized data variation amount σ, and for example, variance or standard deviation can be used. In this example, a standard deviation is used as the binarized data variation amount σ.

  Further, the time range including the binarized data D used for calculating the binarized data variation amount σ can be arbitrarily set, but the binarized data variation amount depends on whether or not the patient 10 is performing normal walking. It sets so that change may appear easily in σ. For example, a standard time that is considered to include one step forward movement of the patient 10 with a time width as a center, for example, 1 to 5 seconds is set with the target time as the center. In this example, as a time range including the binarized data D used for calculation of the binarized data variation amount σ, a time range having a time width of 3 seconds with the target time as the center is set.

  Further, it is desirable to normalize the binarized data variation amount σ so that the maximum value becomes a predetermined value so as to have robustness. In this example, the binarized data variation amount σ is normalized so that the maximum value is 1.

  FIG. 14 is a diagram showing a waveform representing a time change of the binarized data variation amount σ obtained based on the sample acceleration data.

  As can be seen from FIG. 14, the binarized data variation amount σ increases when the patient 10 is walking normally, and the value when the patient 10 is not walking normally. Becomes smaller.

  Returning to FIG. 10, in step S <b> 64, the variation amount threshold determination unit 734 specifies a period in which the binarized data variation amount σ is equal to or greater than the predetermined threshold TH as a walking period. Although the threshold value TH can be set arbitrarily, empirically, when the binarized data variation amount σ is normalized so that the maximum value becomes 1, about 0.4 to 0.8 is appropriate. In this example, 0.6 is set as the threshold TH. When this is applied to the sample acceleration data, as shown in FIG. 14, the period in which the binarized data variation amount σ is equal to or greater than the threshold value TH is a period of time t = 10 to 21 seconds, and a period of time t = 24 to These are 30-second periods, and these periods are specified as walking periods.

  Returning to FIG. 10, in step S65, the margin setting unit 735 determines the remaining period after removing the end period from the walking period specified in step S64 as the walking period to be analyzed. The end of the specified walking period includes a time zone immediately after the start of walking and just before the end of walking. Therefore, more robust analysis can be performed by narrowing down the analysis target to data for a period during which walking is performed more stably. Although the time width of the edge part to remove can be set arbitrarily, empirically about 1 to 3 second is suitable. In this example, the end portions for the first and last two seconds in the specified walking period are removed. When this is applied to the sample acceleration data, the walking period to be analyzed is a period of time t = 12 to 19 seconds and a period of time t = 26 to 28 seconds.

FIG. 15 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. According to this figure, it can be seen that a period during which normal walking is stably performed is determined as an analysis target.

  In addition, the process which removes the edge part of the walk period specified previously and determines the walk period of analysis object is not an essential process, and should just be performed as needed.

  According to such a technique for identifying a normal walking period, a non-walking period during which no walking is performed or an unstable walking period during which walking is not performed stably can be removed from an analysis target, and an accurate and highly accurate walking is performed. Analysis of movement can be performed.

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.

Generally, as a method of detecting a step reference time, for example, the timing and the peak waveform appears in the vertical direction acceleration waveform W _z, mountain gentle of a peak waveform, waveform having periodicity such as a specific waveform pattern A method of detecting the step reference time tb _j based on the above feature is conceivable.

FIG. 29 is a diagram showing the relationship between the vertical acceleration waveform _Wz and the walking phase. The step reference time tb _j is detected as, for example, a time corresponding to the landing phase of the right foot or the left foot or the walking phase of toe kick. As a specific example, in the vertical acceleration waveform W _z, a peak waveform to draw relatively gentle mountain interval between the next peak waveform to detect a relatively short peak waveform. Then, the time corresponding to the maximum value of the peak waveform is regarded as the time corresponding to the landing position of a specific foot that is either the left or right foot in the walking motion, and this time is detected as the step reference time tb _j .

  This method is easy to use because the algorithm is simple and 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 forward motion, some acceleration may occur and erroneous detection may occur. In addition, for example, the detection timing changes depending on the threshold setting. In addition, for example, since the magnitude of acceleration generated during operation varies depending on the patient's age, physique, and the degree of gait disturbance, the detection accuracy varies among patients.

  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. 16 is a functional block diagram showing a functional configuration of the step reference time detection unit 74. As shown in FIG. 16, the step reference time detection unit 74 includes a longitudinal acceleration waveform reading unit 741, a rising-peak conversion unit 742, a conversion waveform shaping unit 743, a peak value normalization unit 744, and a peak detection unit 745. And have.

  FIG. 17 is a flowchart showing the flow of the step reference time detection process.

In step S 71, the longitudinal acceleration waveform reading unit 741 reads the longitudinal acceleration waveform W _y from the storage unit 312. Among the lateral acceleration waveform W _x , the longitudinal acceleration waveform W _y , and the vertical acceleration waveform W _z , the longitudinal acceleration waveform W _y represents the peak waveform with the highest SN ratio for one step forward movement of the patient 10. It has been confirmed that. In the vertical acceleration waveform W _z , the peak waveform corresponding to the foot landing on one foot and the peak waveform corresponding to the toe kick of the other foot appear at short time intervals for one step forward movement of the patient 10. It is complicated. Further, in the lateral acceleration waveform W _x , the SN ratio of the crest value is generally low, and it is difficult to detect a waveform correlated with the forward movement. Therefore, here, the longitudinal acceleration waveform W _y is used to detect the step reference time. The longitudinal acceleration waveform reading unit 741 cuts out the waveform portion of the walking period to be analyzed from the read longitudinal acceleration waveform W _{y as} the longitudinal acceleration wave W _y ′.

FIG. 18 is an enlarged view of the longitudinal acceleration waveform W _y ′ based on the sample acceleration data. The horizontal axis is the sampling number k, and the vertical axis is the longitudinal acceleration component _ay . In the present embodiment, the sampling number k is a number corresponding to each sampling time when sampling is performed at a frequency of 128 Hz, and can be considered as the time t ′ in the walking period determined as the analysis target.

The acceleration waveform used for detecting the step reference time is not limited to the longitudinal acceleration waveform _Wy, but may be other acceleration waveforms, mixed waveforms, or the like, but includes a longitudinal acceleration waveform as a main component. It is desirable that

Returning to FIG. 17, in step S <b> 72, the rise-peak conversion unit 742 causes the steep rising waveform in the longitudinal acceleration waveform W _y ′ to appear as a sharp peak waveform in the walking period determined as the analysis target. Predetermined data conversion is performed on the longitudinal acceleration component a _y . In general, the peak waveform corresponding to the one-step forward movement of the patient 10 in the longitudinal acceleration waveform W _y ′ has a strong tendency to branch at the peak tip, and the position of the peak tip is not stable. On the other hand, the steep rising waveform in the longitudinal acceleration waveform W _y ′ is less branched and the position is relatively stable. Therefore, here, by converting the steep rising waveform in the longitudinal acceleration waveform W _y ′ into a sharp peak waveform, the peak in the longitudinal acceleration waveform W _y ′ is emphasized, and the step reference time can be detected using that as a mark. Like that. Various methods are conceivable for converting such a steep rising waveform into a sharp peak waveform. In this example, the longitudinal acceleration at the sampling time (time specified by the sampling number k) is obtained at each sampling time. the component a _y (k), calculates the difference i.e. variation .DELTA.a _y obtained by subtracting from the next sampling time longitudinal acceleration component at (time specified by the sampling number _{k + 1) a y (k} + 1), the change the amount .DELTA.a _y and the data value at the sampling time.

FIG. 19 is an example of a graph showing a waveform after the rise-peak conversion based on the sample acceleration data. This waveform is a representation of the time variation of data values .DELTA.a _y representing the amount of change between adjacent time of the longitudinal acceleration component a _y.

Returning to Figure 17, at step S73, the conversion waveform shaping unit 743 shapes the waveform of the resulting data value .DELTA.a _y at step S72.

First, in the waveform of the data values .DELTA.a _y representing the amount of change between adjacent time of the longitudinal acceleration component a _y, since it suffices to pay attention only to the positive peak waveform, and converts the negative portion to the 0 value removing it gives a negative value deleted the data value of the data value Δa _y Δa _y 'to.

Figure 20 is a diagram showing a waveform representing the time variation of a _y negative values deleted data values .DELTA.a _y data values .DELTA.a _y representing the amount of change between adjacent time '.

Next, a smoothing process is performed on the waveform of the data value Δa _y ′ from which the negative value has been deleted to obtain a data value S _y that forms a smooth waveform.

FIG. 21 is a diagram illustrating a waveform representing a time change of the data value S _y obtained by performing the smoothing process on the waveform of the data value Δa _y ′ from which the negative value is deleted.

As a result, the detection accuracy of the step reference time is prevented from deteriorating due to the noise included in the waveform of the data value Δa _y ′ from which the negative value is deleted. Here, since we are only interested in the timing corresponding to the peak of the large peak waveform, strong smoothing processing may be performed. As the smoothing process, for example, a smoothing process or the like can be used.

Then, the smoothing process has been applied data value S _y, the peak value at each sampling time th power N (N ≧ 2) to obtain the data value C _y a peak is emphasized. In this example, N = 2.

Figure 22 is a diagram showing a waveform representing the time variation of the squared value C _y smoothing processing applied data value S _y.

Then calculated offset data OSD in the waveform data values C _y a peak is emphasized. Then, to remove the offset data OSD from the data values C _y.

Figure 23 is a view showing a waveform representing the time variation of the offset data OSD data values C _y a peak is emphasized, Figure 24 is offset deleted data value C _y data values C _y a peak is emphasized It is a figure which shows the waveform showing the time change of '.

The waveform of the data values C _y a peak is emphasized, and the like so far decorated with smoothing, zero point slightly offset. Therefore, for the subsequent step, to remove the offset data OSD content from the waveform peaks highlighted data values C _y. The calculation of the offset data OSD, for example, attenuates the peak the peak value of the waveform data values C _y a peak is emphasized by 1 / M-th power (M ≧ 2), subjected to a smoothing process, based on and ride M This is done by returning it. In this example, M = 4.

Next, in the waveform representing the time change of the offset deleted data value C _y ′, the peak value at each sampling time is raised to the Lth power (L ≧ 2) to obtain a data value F _y further emphasizing the peak. In this example, L = 2.

FIG. 25 is a diagram showing a waveform representing a time change of the square value F _y of the data C _y ′ with the peak emphasized.

Returning to Figure 17, in step S74, the peak value normalization unit 744, the waveform representing the time variation of the further enhancement data values F _y the peak, the maximum value of the wave height Normalization is performed so that 1, normal Obtain the digitized data value F _y ′.

Figure 26 is a diagram showing a waveform representing the time variation of the further enhancement data values F _y normalized data values F _y 'of the peak.

Returning to Figure 17, determined in step S75, the peak detector 745 detects the top of the peak waveforms in the waveform representing the time variation of the normalized data values F _y, a time corresponding to the vertex as a step reference time To do. A specific example of the determination method is shown below.

First, the waveform of the normalized data value F _y ′ is differentiated to generate a differentiated waveform. For example, in the waveform of the normalized data value F _y ′, for each sampling time, the peak value at the sampling time is subtracted from the peak value at the next sampling time, and the change amount ΔF _y ′ at the sampling time is calculated. Ask. Then, a differential waveform of the waveform of the normalized data value F _y ′ is obtained by sequentially plotting the amount of change at each sampling time in the time axis direction.

FIG. 27 is a diagram illustrating a waveform representing a time change of the change amount ΔF _y ′ between adjacent times of the normalized data value F _y ′.

  Next, an apex of a peak waveform whose peak value is equal to or greater than a predetermined threshold in the differential waveform is detected. For example, in this differential waveform, a waveform portion that is positive for two consecutive points and then negative for two consecutive points, and specifies a waveform portion whose positive peak value exceeds a predetermined threshold value. Then, the sampling time corresponding to the second positive point in the specified waveform portion is determined as the step reference time.

FIG. 28 shows the detected step reference time with rhombus dots on the longitudinal acceleration waveform W _y ′. It can be seen from FIG. 28 that the steep rise time in the longitudinal acceleration waveform W _y ′ is accurately captured as the step reference time.

  According to such a technique for detecting the step reference time, the waveform can be observed or the waveform can be analyzed for the acceleration component for each forward movement of one step of the patient 10. It is also possible to measure the time required for the forward movement for one step.

  Returning to FIG. 5, in step S <b> 8, the step time graph generation unit 75 generates a graph representing a time-series change in step time (time required for one step), which is the time taken for the step-by-step advancement of the patient 10. Several feature quantities related to the step time are calculated. The step time can be defined in various ways. For example, when the step time is regarded as a periodic motion, the time between a specific phase in one step forward movement, for example, a phase corresponding to landing, and the same specific phase in the next one step forward movement Can be defined as Further, for example, the step time can be a time corresponding to a so-called single leg support period. The single leg support period is a period in which the body is supported by only one leg and only one leg is landing. In other words, it is the period from one toe kicking to landing on the heel. Can do. In this example, the step time is defined using the identified step reference time, and the time between the step reference time corresponding to the one-step forward movement and the step reference time corresponding to the next one-step forward movement. I will ask for it.

  Hereinafter, the functional configuration of the step time graph generation unit 75 and the step time graph generation processing will be described.

  FIG. 30 is a functional block diagram showing a functional configuration of the step time graph generation unit 75. As illustrated in FIG. 30, the step time graph generation unit 75 includes a step time specification unit 751, a graph generation unit 752, and a feature amount calculation unit 753.

  The step time specifying unit 751, the graph generating unit 752, and the feature amount calculating unit 753 are examples of the specifying unit, the generating unit, and the calculating unit in the invention, respectively.

  FIG. 31 is a flowchart showing the flow of the step time graph generation process.

In step S81, the step time specifying unit 751 specifies a plurality of step times T _j , that is, a plurality of left step times T _Li and a right step time T _Ri , based on the detected plurality of step reference times tb _j .

Here, the step time is a time between the step reference time and the next step reference time as described above. The left step time T _Li is a step time corresponding to the one-step forward movement of the patient 10 by the left foot, and the right step time T _Ri is a step time corresponding to the one-step forward movement of the patient 10 by the right foot. is there.

  Hereinafter, a method for specifying the left step time and the right step time will be described.

  In general, in a walking motion, a one-step forward movement with one foot and a one-step forward movement with the other foot are alternately performed. Therefore, the step reference time based on one foot and the step reference time based on the other foot are alternately arranged in the time axis direction, and the left step time and the right step time are alternately switched.

Therefore, first, as shown in FIG. 32, for each combination of two step reference times adjacent to each other at a plurality of detected step reference times, the time between these step reference times is obtained as a step time T _j .

Next, provisionally specifying the left step time T _L and the right step time T _R is performed for the plurality of obtained step times T _j . In this example, when the odd-numbered step time T _2k-1 in-series order and left step time T _L, and even-numbered step time T _2k right step time T _R. Then, if necessary, the left step time T _L and the right step time T _R are exchanged later. For example, it is assumed that the operator 11 compares the graph displayed on the screen of the display unit 302 with the actual state of the walking disorder of the patient 10 as described later, and thinks that the left and right of the step time are reversed. In this case, the operator 11 performs an operation such as pressing a left-right change button displayed on the screen of the display unit 302, so that replacing a left step time T _L and the right step time T _R.

Alternatively, the left step time T _L and the right step time T _R are specified with reference to the left-right acceleration waveform W _x with respect to the plurality of obtained step times T _j . In general, in walking exercise, at the timing of landing on the left foot, the body of the patient 10 is tilted leftward and the acceleration component in the leftward direction is increased. On the other hand, at the timing of landing on the right foot, it is considered that the body of the patient 10 tilts to the right and the acceleration component to the right increases. Therefore, for example, in the left-right acceleration waveform W _x , when the waveform corresponding to the step reference time t _j to be noticed is a waveform that means that the left-right acceleration component a _x increases in the left direction, the attention is paid. step time T _j to step reference time tb _j start time to is determined that there is likely to be left step time T _L. Conversely, in the left-right acceleration waveform W _x , when the waveform corresponding to the step reference time tb _j to be noticed is a waveform that means that the left-right acceleration component a _x increases in the right direction, attention is paid. step time T _j to the step reference time tb _j and start time, it is determined that there is likely to be a right step time T _R. Then, it is determined whether the odd step time T _2k-1 in the chronological order is the left step time T _L or the right step time T _R , which corresponds to the above determination. However, try to adopt the more consistent one.

It should be noted that the step time T _{j is} not specified as left and right, and the odd-numbered step time T _2k-1 is set in the time-series order, the step time T _a by one foot, and the even-numbered step time T _2k in the other feet may be specified as a step time T _b by the.

The difference between the left step time T _L and the right step time T _R specified in this way reflects the difference between the one-step forward motion with the left foot and the one-step forward motion with the right foot. 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 S82, the graph generation unit 752 generates a graph representing temporal changes in 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.

  33 and 34 are diagrams illustrating an example of the generated graph G. FIG. FIG. 33 is a sample by normal walking, and FIG. 34 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. 33, 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 34, 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 indicated by the arrow Y1 in FIG. 34, 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.

Further, in FIG. 34, 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. 33, 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. 34, the step time T _j is longer in the second 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. 31, in step S83, the feature amount calculation unit 753 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.

As the first feature amount, for example, the difference or ratio between the left step time T _L and the right step time T _R can be considered.

More specifically, for example, the difference S ₁ or the ratio R ₁ between the average value of the left step time T _{L and} the average value of the right step time T _R in a plurality of steps within the entire walking period can be used.

Further, for example, the difference S ₂ or the ratio R ₂ between the average value of the left step time T _{L and} the average value of the right step time T _R in the left and right N steps in a partial period of the entire walking period. Can do. The constant N can be set to about 2 to 5, for example.

Further, for example, the difference S ₃ or the ratio R ₃ between the left step time T _L and the right step time T _R in a set of adjacent left and right steps can be used.

  As the first feature amount, for example, the difference or ratio between the M-th power value of the left step time and the M-th power value of the right step time can be considered.

More specifically, for example, the difference S ₄ or the ratio R ₄ between the M-th power value of the average value of the left step time T _L and the M-th power value of the average value of the right step time T _R in a plurality of steps within the entire walking period. It can be. The constant M can be 2 or 3, for example.

Further, for example, the difference S between the M-th power value of the average value of the left step time T _L and the M-th power value of the average value of the right step time T _R in the left and right N steps in a partial period of the entire walking period. it can be a ₅ or ratio R _5. The constant N can be set to about 2 to 5, for example. The constant M can be 2 or 3, for example.

Also, for example, the difference S ₆ or the ratio R ₆ between the M-th power value of the left step time T _{L and} the M-th power value of the right step time T _{R in} a pair of adjacent left and right steps can be used. The constant M can be 2 or 3, for example.

  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.

More specifically, as the second feature amount, for example, the standard deviation σ _L1 of the left step time T _L in the N _L steps by the left foot in the entire walking period and the right step in the N _R steps by the right foot The standard deviation σ _R1 of the time T _R can be set.

Further, for example, it is a standard deviation sigma _R2 of the standard deviation sigma _L2 and the right step time T _R of the left step time T _L over N steps of some within a period of the total walking period. The constant N can be set to about 2 to 5, for example.

  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.

  As the third feature amount, for example, the representative value of the step time in several steps within a partial period that is temporally forward in the entire walking period and the step in several steps within a partial period that is temporally behind The difference or ratio with the representative value of time can be considered.

More specifically, for example, the difference SF _L1 or the ratio between the average value of the left step times T _L of N steps immediately after the start of walking and the average value of the left step times T _L of N steps immediately before the end of walking. It can be RF _L1 . The constant N can be set to about 2 to 5, for example.

Further, for example, be a difference SF _R1 or ratio RF _R1 between the average value of the right step time T _R of N steps in the mean value and walking just before the end of the right step time T _R of N steps immediately after the start of walking Can do. The constant N can be set to about 2 to 5, for example.

Further, for example, walk-starting total N × 2 pieces of the left step time T _L and the right step time T _R average walking just before the end of the total N × 2 - step left step time T _L and the sum of the step immediately after it can be the difference SF _RL or ratio RF _RL and the average value of the sum of the right step time T _R. The constant N can be set to about 2 to 5, for example.

  The first feature amount and the second feature amount may be calculated independently, or the other feature amount is incidentally calculated when one feature amount exceeds a predetermined threshold. You may make it do. For example, when it is determined that the first feature amount exceeds a predetermined threshold and the left / right difference is equal to or greater than a certain level, the second feature amount is further calculated for each of the left foot and the right foot and presented to the operator 11. You may do it. In this way, it is possible to adopt a method that automatically performs further evaluation according to the situation.

  In addition, the second feature amount is calculated for each of the left foot and the right foot, the difference or ratio between the second feature amount of the left foot and the second feature amount of the right foot is obtained, and the left-right difference is evaluated using these values. It is also possible to strengthen.

  Returning to FIG. 5, in step S <b> 9, the display control unit 310 controls the display unit 302 to display the graph G and the first to third feature amounts on the screen. At this time, according to the operation of the operator 10, display / non-display of a bar representing the left step time, display / non-display of a bar representing the right step time, or display / non-display of the feature amount is switched. You may make it perform.

  As described above, according to the present embodiment, the normal walking period of the patient 10 can be specified within the acceleration data acquisition period by the configuration of the walking period specifying unit 73 and the walking period specifying process. As a result, the analysis target can be narrowed down to data obtained during a period in which the patient 10 stably performs normal walking, and high-precision analysis can be performed. Therefore, objective evaluation of the walking motion of the patient 10 becomes possible.

  According to the present embodiment, it is possible to detect a time that is a reference for the forward movement of each step of the patient 10 by the configuration of the step reference time detection unit 74 and the step reference time detection process. 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.

  According to the present embodiment, the left and right step times of the patient 10 can be obtained by the step time graph generation unit 75 and the step time graph generation process, and a graph representing the time change of the step time can be generated. The operator 11 can intuitively understand and evaluate the left-right difference, stability, fatigue level, and the like in the walking motion of the patient 10 by referring to the left and right step time graphs.

  Further, according to the present embodiment, various feature quantities related to the obtained step time can be calculated by the step time graph generation unit 75 and the step time graph generation process. By referring to these feature amounts, the operator 11 can quantitatively evaluate the left-right difference, stability, fatigue level, and the like in the walking motion of the patient 10.

  Thus, the operator 11 can intuitively or quantitatively evaluate the left-right difference and fatigue level of the forward movement of the patient 10 that has been subjectively evaluated by visual observation in the past.

  Further, according to the present embodiment, the left and right step times of the patient 10 can be automatically obtained. Since the operator 11 does not need to carry a stopwatch or the like for measuring the step time, the operator can easily obtain the step time. Moreover, the operator 11 can face the evaluation of the walking movement by hand, and can concentrate on the walking support of the patient 10.

  Based on these comprehensive evaluations, the operator 11 can grasp in detail the movement of the patient 10 during walking, and can create, for example, an effective walking training plan.

  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 this embodiment, a bar graph representing the time change of the step time is generated and displayed. However, the occupation ratio of the left step time and the right step time is obtained for the entire walking period or a step of a partial period, and the occupation A pie chart representing the rate may be generated and displayed.

  Further, for example, in the present embodiment, a graph representing a time series change of the step time, that is, the left step time and the right step time by both feet of the patient 10 is generated and displayed, but only by one of both feet. You may make it produce | generate and display the graph showing the time-sequential change of step time.

  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 731 Front / rear / vertical acceleration feature amount calculation unit 732 Feature amount binarization processing unit 733 Binary data variation amount calculation unit 734 Variation amount threshold determination unit 735 Margin setting unit 74 Step reference time detection unit 741 Front / rear acceleration waveform Reading unit 742 Rise-peak conversion unit 743 Conversion waveform shaping unit 744 Peak value normalization unit 745 Peak detection unit 75 Step time graph generation unit 751 Step time specification unit 752 Graph generation unit 753 Feature amount calculation unit

Claims (4)

Based on the acceleration data during the movement of the person obtained from an acceleration sensor attached to the person, the time required for one step for the one-step advance movement is specified for each one-step forward movement constituting the movement. Specific means to
Generating means for generating a graph representing a time-series change of the one-step required time based on a plurality of one-step required time specified by the specifying means;
A calculation unit for obtaining a feature amount related to the one-step required time;
The calculation means includes, as the feature amount, a typical one-step required time in a time zone that is closer to the front during the movement, and a representative step in a time zone that is closer to the time during the movement. Find the amount of change between the time required,
Mobile motion analysis device. The amount of change is a typical one-step required time of one or more predetermined steps by one leg in the front time zone and the predetermined number of one foot by the one foot in the rear time zone. The mobile motion analysis apparatus according to claim 1 , wherein the moving motion analysis apparatus is a change amount between a one-step required time and a representative one-step required time. The amount of change is a typical one-step required time of two or more predetermined steps required by both feet in the front time zone, and the predetermined number of both feet in the rear time zone. The mobile motion analysis apparatus according to claim 1 , wherein the moving motion analysis apparatus is a change amount between a one-step required time and a representative one-step required time. A program for causing a computer to function as the mobile motion analysis apparatus according to any one of claims 1 to 3 .