JP6486200B2 - Mobile motion analysis apparatus, system, 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.

  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 acceleration, it is very important to recognize the timing (timing) of the foot landing in the acceleration data (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
A specifying means for specifying an extreme value that is one of a local maximum value and a local minimum value in a waveform representing acceleration in the vertical direction during movement of a person obtained by using an acceleration sensor;
Determining means for determining the threshold value so that the degree of variation of the time interval of the time corresponding to the extreme value exceeding a predetermined threshold value among the specified extreme values is minimized;
There is provided a mobile motion analysis apparatus comprising: a detecting unit that detects a time corresponding to an extreme value exceeding the determined threshold among the specified extreme values as a time when the person's foot has landed.

The invention of the second aspect is
A specifying means for specifying an extreme value that is one of a maximum value and a minimum value in a waveform representing an amount calculated based on an acceleration in a vertical direction during the movement of a person obtained by using an acceleration sensor;
Determining means for determining the threshold value so that the degree of variation of the time interval of the time corresponding to the extreme value exceeding a predetermined threshold value among the specified extreme values is minimized;
There is provided a mobile motion analysis apparatus comprising: a detecting unit that detects a time corresponding to an extreme value exceeding the determined threshold among the specified extreme values as a time when the person's foot has landed.

  Here, the “extreme value that is one of the local maximum value and the local minimum value” is the “local maximum value” when the foot landing appears as the local maximum value in the waveform, and the foot landing as the local minimum value in the waveform. When it appears, it is a “local minimum”. The “extreme value exceeding the threshold value” is “maximum value exceeding the threshold value” when the extreme value is a maximum value, and “minimum value below the threshold value” when the extreme value is a minimum value. is there.

  The “time when the foot has landed” is the time when at least a part of the foot, for example, the heel or the entire 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 a term of the vertical acceleration.

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 raising the term representing the second derivative of the vertical acceleration to the k-th power (k ≧ 1). Providing equipment.

The invention of the fifth aspect is
The moving motion analysis apparatus according to the fourth aspect, in which the k-th power is the third power.

The invention of the sixth aspect is
The detection means detects, as a time when the foot has landed, a time obtained by averaging times corresponding to the plurality of extreme values when there are a plurality of extreme values exceeding the threshold within a predetermined time width. A moving motion analysis apparatus according to any one of the first to fifth aspects is provided.

The invention of the seventh aspect
The mobile motion analysis apparatus according to the sixth aspect, wherein the predetermined time width is equal to or less than half of the average of the time interval.

The invention of the eighth aspect
The degree of variation provides the mobile motion analysis apparatus according to any one of the first to seventh aspects, which is a variance or standard deviation of a time interval of the time.

The invention of the ninth aspect is
The moving motion analysis apparatus according to any one of the first to eighth aspects is provided in which the waveform is subjected to a process for removing a high-frequency component.

The invention of the tenth aspect is
A mobile motion analysis apparatus according to any one of the first to ninth aspects, wherein the mobile motion is walking.

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

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

  According to the invention of the above aspect, the timing of the landing can be recognized with high accuracy in the data from the acceleration sensor attached to the person who moves.

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 the figure which overlapped and represented the walk period determined as an analysis object on the acceleration waveform produced | generated based on the sample acceleration data. It is a figure which shows the relationship between an up-down direction acceleration waveform 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 vertical acceleration waveform based on sample acceleration data. It is a figure which shows a vertical acceleration reflection waveform. It is a figure which shows the local maximum specified in the vertical acceleration reflection waveform. It is a figure which shows a vertical acceleration reflection waveform and the determined threshold value. It is a figure which shows the recognized landing. It is a figure which shows the example in case a some local maximum appears close in time in a vertical acceleration reflection waveform. 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 which shows an up-down direction acceleration waveform. It is a figure for demonstrating the 1st left-right step waveform extraction method. It is a figure for demonstrating the 2nd left-right step waveform extraction method. It is a figure which shows an example of the extracted some left-right step waveform. It is a figure which shows the example of a display of the screen on which the representative waveform of the left-right step waveform, the dispersion | variation bar of each walking phase, etc. were displayed.

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

  FIG. 1 is a diagram schematically showing the configuration of the gait analysis 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 a partial waveform corresponding to a forward movement of one step or a plurality of steps of the patient 10 from the acceleration waveform. Is assumed to be obtained, and a graph is obtained by obtaining a representative waveform and a degree of variation of the peak value in the partial waveform.

  It should be noted that in general, the forward movement operation step by step left and right includes one each operation of landing on the right foot, kicking the toe of the left foot, landing on the left foot, and kicking the toe of the right 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. And a step waveform graph generation unit 75.

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 step waveform graph generation unit 75 extracts a partial waveform corresponding to the forward movement of one step or a plurality of steps based on the detected step reference time, and the waveform at the same walking phase as the representative waveform of the partial waveform. The degree of variation of the high value is calculated, and these are graphed. Here, the walking phase refers to a phase when walking is considered as a periodic motion, and corresponds to, for example, the timing of right foot heel landing, left foot toe kick, left foot heel landing, right foot toe kick, etc. Phase is included.

  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 for displaying the representative waveform in the step waveform of the patient 10 and the degree of variation in the peak value. According to such a function of displaying the representative waveform in the step waveform and the degree of variation of the peak value, the waveform shape unique to the patient 10 can be easily recognized for the acceleration component during walking of the patient 10, In addition, it is possible to easily compare and observe the waveform variation in each walking phase such as landing on a foot and kicking a toe. As a result, the operator 11 grasps the characteristic movement of the weight balance of the patient 10, that is, the direction and strength of the movement from the stationary state, the timing of the foot landing of the patient 10 and the toe kick, the strength thereof, And its stability.

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 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 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 waveform having periodicity such as peak waveform or a specific waveform pattern in the vertical direction acceleration waveform W _z, detecting step reference time tb _j A way to do this is 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 by the acceleration threshold determination, the specific accuracy is limited. For example, since the magnitude of acceleration generated during operation varies depending on the patient's age, physique, the degree of gait disturbance, etc., the detection accuracy varies among patients. Further, for example, an operation different from the landing on the saddle is erroneously detected, or a detection failure of the true landing on the ground is caused.

  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 a vertical acceleration waveform reading unit 741, a vertical acceleration reflected waveform generation unit 742, a local maximum value specifying unit 743, a threshold value determination unit 744, and a landing landing recognition unit. 745.

  The local maximum value specifying unit 743, the threshold value determining unit 744, and the saddle landing recognizing unit 745 are examples of the specifying unit, the determining unit, and the detecting unit in the invention, respectively.

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

In step S71, the vertical acceleration waveform reading unit 741 reads the vertical acceleration waveform W _z from the storage unit 312. 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 foot landing on the other foot are alternately alternated at substantially constant time intervals for the forward movement of the patient 10 step by step. appear. Therefore, here, the vertical acceleration waveform _Wz is used to detect the step reference time. The vertical acceleration waveform reading unit 741 cuts out the waveform portion of the walking period to be analyzed from the read vertical acceleration waveform W _z as the vertical acceleration wave W _z ′.

FIG. 12 is an enlarged view of the vertical acceleration waveform W _z ′ based on the sample acceleration data. In this figure, the horizontal axis indicates the time t in the walking period determined as the analysis target, and the vertical axis indicates the vertical acceleration component _az .

The acceleration waveform used for detecting the step reference time is not limited to the vertical acceleration waveform W _z ′, but may be another acceleration waveform, a mixed waveform, or the like, but the vertical acceleration component a _z is mainly used. It is desirable that the waveform be included as a component.

Returning to FIG. 11, in step S <b> 72, the vertical acceleration reflected waveform generation unit 742 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. The second derivative at the extreme value is the curvature at that point, and represents how sharp the peak is.

Here, the vertical acceleration reflected waveform WJ _z ′ is calculated by taking the absolute value | a _z | of the vertical acceleration component a _{z and} the absolute value of the second-order time derivative of the acceleration component a _z | d ² a _z / dt ² | A waveform representing a time change of the vertical acceleration reflected value J _z represented by the product of the calculated value and the product. | D ² a _z / dt ² | is raised to the third power because | d ² a _z / dt ² | is weighted more than | a _z | and | d ² a _z / dt ² | This is to emphasize the differences.

The vertical acceleration reflected waveform WJ _z ′ is a waveform as shown in FIG. 13, for example. By doing so, a waveform in which a change in the vertical acceleration component a _z accompanying the movement of the patient's landing HC is emphasized, that is, a vertical acceleration reflected waveform WJ _z ′ representing a temporal change in the vertical acceleration reflected value J _z. Can be obtained. Accordingly, it is possible to detect the landing HC of the patient 10 by searching for the maximum value Ph in the vertical acceleration reflected waveform WJ _z ′.

In step S73, the local maximum value specifying unit 743 specifies the local maximum value Ph. First, as necessary, high frequency components are removed from the vertical acceleration reflected waveform WJ _z ′. Thereby, spike-like high frequency noise is removed. Next, the maximum value Ph is specified in the vertical acceleration reflected waveform WJ _z ′ from which the high frequency component has been removed. This local maximum value Ph is a candidate corresponding to the patient's 10 landing HC.

FIG. 14 is a diagram showing the maximum value Ph specified in the vertical acceleration reflected waveform WJ _z ′.

  In step S74, the threshold value determination unit 744 determines the threshold value Th. The threshold Th is determined so that the degree of variation in the time interval of the time corresponding to the maximum value Ph exceeding the predetermined threshold Th among the maximum value Ph specified in step S73 is minimized. The degree of variation is, for example, variance or standard deviation. The maximum value Ph corresponding to the true landing HC is expected to have a magnitude of a certain level or more. In addition, when the patient 10 normally moves forward, it is assumed that the saddle landing HC is ideally performed alternately at right and left intervals. Therefore, by determining the threshold value Th in this way and extracting only the maximum value Ph exceeding the determined threshold value Th, only the maximum value Ph corresponding to the true landing HC can be specified with a high probability.

  FIG. 15 is a diagram illustrating the vertical acceleration reflected waveform and the determined threshold value.

  In step S75, the saddle landing recognition unit 745 recognizes the saddle landing HC. That is, among the local maximum values Ph in the vertical acceleration reflected waveform, the local maximum value Ph exceeding the threshold Th determined in step S74 is recognized in association with the true landing HC. Thereby, the time corresponding to the maximum value PH exceeding the threshold Th can be considered as the time corresponding to the timing of the landing HC. This time can be used as the step reference time.

  FIG. 16 is a diagram showing the recognized landing HC. In this figure, the time corresponding to the landing HC, that is, the step reference time is indicated by a two-dot broken line.

In practice, as shown in FIG. 17, the local maximum value Ph in the vertical acceleration reflected waveform WJ _z ′ is not limited to the landing HC, but some operation performed at timing close to the landing HC, for example, Also, it may appear due to a toe kick TO with a foot opposite to the foot performing the landing HC. Further, when a plurality of local maximum values Ph appear close in time in this way, it is very difficult to distinguish which local maximum value Ph is the local maximum value Ph corresponding to the landing HC. Therefore, when there are a plurality of maximum values Ph exceeding the threshold Th in the vertical acceleration reflected waveform WJ _z ′, the times corresponding to the plurality of maximum values Ph are averaged. The time may be associated with the saddle landing HC. This predetermined time width Δt can be empirically set to, for example, half or less of the average of the time interval at the maximum value Ph exceeding the threshold Th, and more preferably about 1/2 to 1/3 of the average. can do.

  As described above, the extreme value that is one of the maximum value or the minimum value is specified in the acceleration reflected waveform, and the degree of time interval variation corresponding to the specified extreme value exceeding the threshold value is minimized. According to the method of determining the threshold value and detecting the time corresponding to the extreme value exceeding the determined threshold value among the specified extreme values as the landing time, ideally, the landing is almost constant. By optimizing the threshold value in consideration of the fact that it is performed periodically, detection omissions and false detections 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 S8, a graph is generated. The step waveform graph generation unit 75 generates a graph representing the degree of variation in the peak value in the same walking phase as the representative waveform of the step waveform. It should be noted that the processing for generating the graph representing the variation in the crest value at the same walking phase as the representative waveform of the step waveform may be performed separately for the forward movement of the right foot and the forward movement of the left foot. In this example, the left and right step waveforms corresponding to a series of operations including the forward movement step by step left and right are performed. That is, a plurality of left and right step waveforms are extracted from the acceleration waveform, and a representative waveform and a degree of variation in the peak value in the left and right step waveforms are obtained and graphed. If it does in this way, about the one step advance operation of patient 10, the waveform of the corresponding acceleration component can be observed simultaneously right and left, or it can compare right and left. In addition, the time required for one step forward operation can be confirmed and compared simultaneously on the left and right.

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

  FIG. 18 is a functional block diagram showing a functional configuration of the step waveform graph generation unit 75. As shown in FIG. 18, the step waveform graph generation unit 75 includes a step waveform extraction unit 751, a step waveform normalization unit 752, a representative waveform calculation unit 753, a peak value variation calculation unit 754, and a graph generation unit 755. have.

  FIG. 19 is a flowchart showing the flow of the step waveform graph generation process.

In step S81, the step waveform extraction unit 751 performs the determination based on the plurality of step reference times tb _j detected in the lateral acceleration waveform W _x ′, the longitudinal acceleration waveform W _y ′, and the vertical acceleration waveform W _z ′. A plurality of left and right step waveforms P _{x, j} , P _{y, j} , P _{z, j} are extracted.

Here, a case where a plurality of left and right step waveforms P _{z, j} are extracted from the vertical acceleration waveform W _z ′ will be described as an example.

Figure 20 is a diagram showing the vertical acceleration waveform W _z. As shown in FIG. 20, the vertical acceleration waveform W _z, heel landing of the right foot, kicks toe left foot, the heel landing left foot, and the walking phase corresponding to the operation of the kick toe of the right foot, the peak waveform appears.

  As a method of extracting the left and right step waveforms, for example, the following method can be considered.

  First, the first left and right step waveform extraction method will be described.

FIG. 21 is a diagram for explaining a first left / right step waveform extraction method. The first left and right step waveform extraction method focuses on even-numbered or odd-numbered step reference times tb _j , and steps left and right on partial waveforms corresponding to the time range tr _j having a specific positional relationship with the focused step reference times. This is a method of extracting as the waveform P _j . In the first left / right step waveform extraction method, first, the relative positional relationship J between the left / right step waveform and the step reference time is obtained from the results of experiments and simulations. Next, based on the time between peak waveforms in the vertical acceleration waveform W _{z and} the like, a period Δts in which the forward movement operation is repeated step by step left and right is obtained. Then, based on the positional relationship H and the period Δts, it is determined which time range is a time range corresponding to the forward movement step by step on the left and right with respect to the step reference time tb _j of interest. For example, the time range corresponding to the forward movement step by step on the left and right is obtained as the time range from the point of interest step reference time tb _j to the point of −Δt1 to the point of + (Δts−Δt1). The step waveform extraction unit 751 extracts the partial waveform in the time range thus obtained as the left and right step waveform P _j .

  Next, the second left / right step waveform extraction method will be described.

FIG. 22 is a diagram for explaining the second left / right step waveform extraction method. The second left and right step waveform extraction method focuses on the even or odd step reference time tb _j and extracts a partial waveform having a specific pattern close to the focused step reference time tb _j as the left and right step waveform P _j. It is a method to do. In a second lateral step waveform extraction method, in the vertical direction acceleration waveform W _z, the focused step reference time tb _j after appearing four Tsunoyama (wave height certain level or more peak waveform), namely, one of the heel of the foot A partial waveform including peaks corresponding to the landing, the toe kick of the other foot, the heel landing of the other foot, and the toe kick of the one foot is extracted as a left and right step waveform P _j .

Note that the step waveform extraction unit 751 may extract the left and right step waveforms P _j by pattern matching of the waveform shape, etc., regardless of the step reference time. Further, when extracting the left and right step waveform P _j , in the vertical acceleration waveform W _z ′, the time point when the peak value switches from negative to positive is the start time of the partial waveform to be extracted, and the peak value switches from positive to negative. The time point may be the end point of the partial waveform to be extracted.

FIG. 23 is a diagram illustrating an example of a plurality of extracted left and right step waveforms. In this example, a plurality of left and right step waveforms extracted from the vertical acceleration waveform W _z ′ are shown. As shown in FIG. 23, the extracted left and right step waveforms are partial waveforms that are similar to each other, but there are variations in the peak value for each walking phase.

Returning to FIG. 19, in step S _< b> 82, the step waveform normalization unit 752 normalizes the extracted left and right step waveforms P _j with respect to the time axis direction. For example, one for each cylinder step waveform P _j, so that the time between the time corresponding to the maximum value of the time and the last mountain corresponding to the maximum value of the first hill included in the left and right step waveform P _j is constant Next, adjust the scale and offset in the time axis direction. Note that this normalization is not an essential process and may be performed as necessary.

In step S83, the representative waveform calculation unit 753 obtains the average value of the peak values for each walking phase for the normalized left and right step waveforms P _j , plots them in the time axis direction, and displays the left and right step waveforms. The tentative average waveform V0 is calculated.

At step S84, the representative waveform calculation unit 753 calculates the amount of deviation from the provisional average waveform V0 for each right and left step waveform P _j, leaving only the left and right step waveform the shift amount is within a predetermined level for processing . For example, for each left and right step waveform P _j , a difference (absolute value) in peak value between the left and right step waveform and the provisional average waveform V0 is obtained for each walking phase, and a value obtained by integrating them is obtained. When the obtained value exceeds a predetermined threshold, the left and right step waveforms are excluded from the processing target. Thereby, an abnormal waveform corresponding to an accidental motion that is not a normal walking motion can be removed from the processing target.

  In step S85, the representative waveform calculation unit 753 calculates a representative waveform V1, which is a representative waveform for a plurality of left and right step waveforms from which abnormal waveforms have been excluded. In this example, an average waveform is calculated as the representative waveform V1.

  In step S86, the peak value variation calculation unit 754 calculates the degree of variation in the peak value of the left and right step waveforms for each walking phase with respect to the plurality of left and right step waveforms from which the abnormal waveforms are excluded. As the degree of variation, for example, dispersion or standard deviation can be used. In this example, the standard deviation is calculated as the degree of variation.

In step S87, the graph generation unit 755, the horizontal axis represents time or walking phase, the vertical axis in a coordinate system with its peak value, the representative waveform V1 of the left and right step waveform, the standard deviation of the peak value for each walking phase phi _k A left and right step waveform graph G in which a variation bar B _k having a width corresponding to σ _k is drawn is generated.

  FIG. 24 is a diagram illustrating an example of the generated left and right step waveform graph G.

In this example, as shown in FIG. 24, the graph generation unit 755 uses the peak waveform maximum value m _f for each peak waveform in the representative waveform V1 of the left and right step waveforms as an index value indicating the “strength” of walking. The standard deviation σ _f at the walking phase taking the maximum value m _f is used as an index value representing “variation” of walking, and the left and right step waveform graph G is generated so that these pieces of information are further included. In this way, the operator 11 refers to the left and right step waveform graph G, so that the vertical acceleration component in the walking phase φ _f corresponding to the movements of the left foot and the right foot of the patient 10 and the toe kick of the patient 10 is obtained. About _{az, f} , the magnitude _| size and stability can be grasped _| ascertained, and the gait evaluation of the patient 10 can be performed based on these information.

  Returning to FIG. 5, in step S <b> 9, 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 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 this embodiment, the representative waveform of the left and right step waveforms of the patient 10 can be obtained by the step waveform graph generation unit 75 and the step waveform graph generation process. The operator 11 can understand the characteristic motion unique to the patient 10 in the walking motion by referring to the representative waveform of the left and right step waveforms. Therefore, this embodiment is effective for objective evaluation of the walking motion of the patient 10.

In addition, according to the present embodiment, the variation bar B _k representing the degree of variation in the peak value is displayed for each walking phase of the plurality of extracted left and right step waveforms P _j , so that the representative of the left and right step waveforms P _j is displayed. The reliability of the waveform V1 and the stability for each walking phase φ can be recognized. Accordingly, the operator 11 can grasp the instability of the walking motion of the patient 10 and the timing at which an unstable motion is performed during the walking motion.

Further, according to the present embodiment, the maximum value m _f of the peak waveform in the representative waveform V1 of the left and right step waveform P _j and the degree of variation in the left and right step waveforms at the walking phase φ _f taking the maximum value are displayed. Therefore, the operator 11 is one of the main motions of the walking motion corresponding to these maximum values, ie, the foot landing on one foot, the toe kick on the other foot, the foot landing on the other foot, and the toe kick on one foot. It is possible to quantitatively grasp the strength and stability of the movement. The strength of each action is an index for measuring the muscle strength of the foot of the patient 10. The stability of each operation is an index of how much the patient 10 can support his / her weight with his / her foot. The operator 11 can objectively evaluate the walking motion of the patient 10 based on such information.

  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, when the left and right step waveforms are extracted, the vertical acceleration component a _z during the walking motion of the patient 10 is a processing target. However, the present invention is not limited to this. The component a _x and the longitudinal acceleration component a _y may be processed. That is, in the lateral direction acceleration waveform W _x and the longitudinal acceleration waveform W _y which represents the time variation of the longitudinal acceleration component a _y representing the time variation of the lateral direction acceleration component a _x, extracts the right and left step waveform, Ya representative waveform You may make it obtain | require the grade etc. of the dispersion | variation in a crest value. In the left-right acceleration waveform W _x and the front-rear acceleration waveform W _y , the characteristics of the left-right step waveform may not appear as clearly as the vertical acceleration waveform W _z . In this case, the time range tr corresponding to the left and right step waveforms is detected by detecting the reference time tb _{j of the} step based on the waveform shape of the vertical acceleration waveform W _z or performing pattern matching of the waveform shape. _{Specify j} . A partial waveform in the time range tr _{j in} the left-right acceleration waveform W _x and the front-rear acceleration waveform W _y may be extracted as the left-right step waveform P _j .

  In addition, for example, in this embodiment, when performing processing such as extraction of partial waveforms and calculation of the degree of variation of representative waveforms and peak values, the forward movement of each step on the left and right is handled as one unit. A forward movement of only one step on one foot or a forward movement of three or more consecutive steps may be treated as one unit.

  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 specifying unit 74 Step reference time detecting unit 741 Vertical acceleration waveform reading unit 742 Vertical acceleration reflected waveform generating unit 743 Maximum value specifying unit (specifying means)
744 Threshold value determination unit (determination means)
745 Landing recognition unit (association means)
75 step waveform graph generation unit 751 step waveform extraction unit 752 step waveform normalization unit 753 representative waveform calculation unit 754 peak value variation calculation unit 755 graph generation unit

Claims (12)

A specifying means for specifying an extreme value that is one of a local maximum value and a local minimum value in a waveform representing acceleration in the vertical direction during movement of a person obtained by using an acceleration sensor;
Determining means for determining the threshold value so that the degree of variation of the time interval of the time corresponding to the extreme value exceeding a predetermined threshold value among the specified extreme values is minimized;
A mobile motion analysis apparatus comprising: a detecting unit configured to detect a time corresponding to an extreme value exceeding the determined threshold among the specified extreme values as a time when the human foot has landed. A specifying means for specifying an extreme value that is one of a maximum value and a minimum value in a waveform representing an amount calculated based on an acceleration in a vertical direction during the movement of a person obtained by using an acceleration sensor;
Determining means for determining the threshold value so that the degree of variation of the time interval of the time corresponding to the extreme value exceeding a predetermined threshold value among the specified extreme values is minimized;
A mobile motion analysis apparatus comprising: a detecting unit configured to detect a time corresponding to an extreme value exceeding the determined threshold among the specified extreme values as a time when the human foot has landed.   The mobile motion analysis apparatus according to claim 2, wherein the calculated amount is calculated by an arithmetic expression including a term of the vertical acceleration.   The mobile motion analysis device according to claim 2, wherein the calculated amount is calculated by an arithmetic expression including a term obtained by raising a term representing the second derivative of the vertical acceleration to the k-th power (k ≧ 1). .   The mobile motion analysis apparatus according to claim 4, wherein the k-th power is the third power.   The detection means detects a time obtained by averaging the times corresponding to the plurality of extreme values as the time when the foot has landed when there are a plurality of extreme values exceeding the threshold within a predetermined time width. The moving motion analysis apparatus according to any one of claims 1 to 5.   The mobile motion analysis apparatus according to claim 6, wherein the predetermined time width is equal to or less than half of an average of the time intervals.   The mobile motion analysis apparatus according to claim 1, wherein the variation degree is a dispersion or a standard deviation of the time interval.   The mobile motion analysis apparatus according to any one of claims 1 to 8, wherein the waveform is subjected to processing for removing a high-frequency component.   The mobile motion analysis apparatus according to any one of claims 1 to 9, wherein the mobile motion is walking.   The mobile motion analysis system provided with the said acceleration sensor and the mobile motion analysis apparatus as described in any one of Claims 1-10. A program for causing a computer to function as the mobile motion analysis apparatus according to any one of claims 1 to 10.