Gait analysis system, gait analysis method, and gait analysis program

Abstract

To provide a walking analysis system capable of analyzing information on walking by a walker highly accurately in a short time without causing a portrait rights problem.SOLUTION: A walking analysis system can be provided, the walking analysis system being capable of acquiring information on walking by a walker without acquiring face information of the walker, only from pressure information derived from a sole of the walker, by classifying pressure information input to pressure information input means 22 to which pressure information is input, the pressure information being output from pressure information measurement means 12 that measures pressure information including information on a pressure value, a measurement time, and a measurement place derived from the sole of the walker, in pressure information classification processing (step S200) of classifying the pressure information into pressure information for each of any footprint, and classifying any footprint classified by the pressure information classification processing into a footprint for each of any person by footprint classification processing (step S300).SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a gait analysis system, a gait analysis method, and a gait analysis program. [Background technology]

[0002] Conventionally, methods for acquiring information on people's movement lines in public places, such as their direction, speed, and duration of stay, have been known for the purpose of optimizing product layout, predicting busy periods, etc. For example, Omron Corporation's HVC-C2W and other systems are known that recognize people based on image information captured by a camera and acquire information on the movement lines of each person.

[0003] Also, for example, Patent Document 1 discloses a behavior recognition device that improves the recognition accuracy of the behavior of an object based on the detection results of multiple detection means without user intervention. In this behavior recognition device, a motion detection unit analyzes an image captured by a camera (first sensor) to detect the movement of a person (object), and the motion detection unit analyzes the output of a sensor tag (second sensor) to detect the movement of the person. Then, a reliability calculation unit assigns a reliability to the detection result of the movement of the person based on the output of the sensor tag. The reliability calculation unit also assigns a reliability to the detection result of the movement of the person based on the image. Furthermore, the behavior recognition unit recognizes the behavior of the person without user intervention based on the detection result of the movement of the person detected by the motion detection unit and the reliability of the detection result. This allows the behavior of the object to be recognized with high accuracy. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-159726 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the behavior recognition device described in Patent Document 1 has a problem in that portrait rights may be violated if the captured image includes the face of a pedestrian. Also, since complex processing such as calculating reliability is required, there is a problem in that long processing times are required, particularly when recognizing the behaviors of a large number of people.

[0006] The present invention has been made in consideration of these problems, and its main object is to provide a gait analysis system that can analyze information about the gaits of an unspecified number of pedestrians with high accuracy and in a short time without causing problems with portrait rights. It is also an object of the present invention to provide an analysis method and a gait analysis program that can analyze information about the gaits of an unspecified number of pedestrians with high accuracy and in a short time. [Means for solving the problem]

[0007] In order to achieve at least one of the above-mentioned objects, the present invention employs the following means.

[0008] The gait analysis system of the present invention comprises: pressure information input means for inputting pressure information output from a pressure information measurement means for measuring pressure information including pressure values from the soles of the feet of a walker, and information regarding measurement times and measurement locations of the pressure values; a pressure information classification means for classifying the pressure information inputted to the pressure information input means into pressure information for each arbitrary foot shape; a foot shape classifying means for classifying the arbitrary foot shapes classified by the pressure information classifying means into foot shapes for arbitrary persons; characterized in that it comprises It is something.

[0009] This gait analysis system classifies pressure information input into a pressure information input means that receives pressure information output from a pressure information measurement means that measures pressure information including pressure values from the soles of the walker's feet, the measurement time and measurement location of the pressure values, using a pressure information classification means that classifies the pressure information into pressure information for each arbitrary foot shape, and then classifies the arbitrary foot shapes classified by the pressure information classification means into foot shapes for each arbitrary person using a foot shape classification means.By doing so, it is possible to obtain information that can analyze information about a walker's gait with high accuracy and in a short time from only the pressure information from the soles of the walker's feet, without causing any problems with the walker's portrait rights.

[0010] The gait analysis system of the present invention may further comprise a gait information analysis means for analyzing gait information for each person based on the foot shapes classified for each person by the foot shape classification means. This makes it possible to analyze information about a pedestrian's gait with high accuracy and in a short time from only pressure information derived from the soles of the feet, without causing any problems with the pedestrian's portrait rights.

[0011] In the gait analysis system of the present invention employing this aspect, the gait information analysis means may calculate at least one of a step length, a step width, a walking speed, and a walking cycle for each person from the foot shapes classified for each person by the foot shape classification means. In this way, information about the pedestrian's gait can be analyzed with high accuracy and in a short time from only pressure information derived from the soles of the pedestrian's feet, without causing any problems with the pedestrian's portrait rights, and at least one of a step length, a step width, a walking speed, and a walking cycle for each person can be calculated.

[0012] In the gait analysis system of the present invention, the pressure information classification means may classify the pressure information into pressure information for any given foot shape by clustering the pressure information derived from the same foot shape using a clustering algorithm that utilizes the K-means method. This allows for highly accurate and quick analysis of information about a pedestrian's gait using only the pressure information derived from the soles of the pedestrian's feet, without causing any issues with the pedestrian's portrait rights.

[0013] In the gait analysis system of the present invention, the foot shape classification means may classify the foot shapes of any individual based on a pressure distribution transition vector. This allows for accurate and rapid analysis of information about a pedestrian's gait, without violating the pedestrian's portrait rights, solely from pressure information derived from the soles of the feet. Here, the term "pressure distribution transition vector" refers to a vector (a vector in three-dimensional spatial coordinates consisting of X coordinates, Y coordinates, and time t) that is derived by calculating the pressure center point or area center point in the frames immediately before cluster formation and disappearance of the cluster of clustered pressure values, calculating the point on the coordinates immediately before the cluster formation and the point immediately before the cluster formation and the pressure value, and then calculating the end point from this start point to the end point. This pressure distribution transition vector is a three-dimensional vectorization of the spread of reaction sites from the formation to disappearance of the cluster, the trajectory of the foot pressure center, and the like.

[0014] In the gait analysis system of the present invention, the foot shape classification means may classify the foot shapes of any person based on a reference estimated existence range. This allows for highly accurate and quick analysis of information about a pedestrian's gait based solely on pressure information from the soles of the feet, without violating the pedestrian's portrait rights. Here, the "reference estimated existence range" refers to the range in which the next foot shape is likely to be located.

[0015] The gait analysis method of the present invention comprises: a pressure information input step of inputting pressure information output from a pressure information measurement means that measures pressure information including a pressure value from the sole of a walker's foot, and information regarding a measurement time and a measurement location of the pressure value; a pressure information classification step of classifying the pressure information inputted in the pressure information input step into pressure information for each arbitrary foot shape; a foot shape classification step of classifying the arbitrary foot shapes classified in the pressure information classification step into foot shapes for arbitrary persons; characterized in that it comprises It is something.

[0016] This gait analysis method involves inputting pressure information output from a pressure information measurement means that measures pressure information including pressure values from the soles of the walker's feet, the measurement time and measurement location of the pressure values, classifying the pressure information input in a pressure information classification step into pressure information for each arbitrary foot shape, and classifying the arbitrary foot shapes classified in the pressure information classification step into foot shapes for each arbitrary person in a foot shape classification step, thereby making it possible to obtain information that can be used to analyze information about the walker's gait with high accuracy and in a short time from only the pressure information from the soles of the walker's feet, without causing any problems with the walker's portrait rights.

[0017] The program of the present invention is a program for causing one or more computers to execute each step of a gait analysis method. This program may be recorded on a computer-readable storage medium (e.g., a hard disk, ROM, CD, DVD, flash memory, etc.), or may be transmitted from one computer to another via a transmission medium (a communication network such as the Internet or a wired / wireless LAN), or may be transmitted in any other form. Furthermore, even if the program is executed by a device that executes each step of the analysis method, the device on which the program is executed may be different from the device on which the processing is performed. In either case, by executing this program on a single computer or by having multiple computers share and execute each step, the same effects as the above-mentioned analysis method can be obtained. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 is a block diagram showing an example of electrical connections in a gait analysis system 20 of the present invention. [Figure 2] FIG. 2 is an explanatory diagram showing an example of the pressure information obtaining means 10 and pressure information. [Figure 3] FIG. 3 is a flowchart showing an example of a gait analysis processing routine of the present invention. [Figure 4] FIG. 4 is a flowchart showing an example of raw data processing in the gait analysis processing routine. [Figure 5] FIG. 5 is a flowchart showing an example of the foot shape classification process in the gait analysis process routine. [Figure 6] FIG. 6 is a schematic diagram showing clustered pressure information. [Figure 7] FIG. 7 is a flowchart showing an example of the person-by-person classification process in the gait analysis process routine. [Figure 8] FIG. 8 is a flowchart showing an example of the gait analysis process in the gait analysis process routine. DETAILED DESCRIPTION OF THE INVENTION

[0019] Next, a gait analysis system 20 will be described in detail as an example of an embodiment of the present invention. The embodiments and drawings described below are intended to exemplify a portion of the embodiments of the present invention and are not intended to limit the scope of the present invention. Appropriate modifications can be made without departing from the spirit of the present invention. Corresponding components in each drawing are denoted by the same or similar reference numerals. Furthermore, by showing an example of an analysis method using the gait analysis system 20, an example of a gait analysis method and gait analysis program of the present invention will be clarified, as well as an example of a method of using the gait analysis system 20.

[0020] 1, a gait analysis system 20, an example of an embodiment of the present invention, comprises: a pressure information input means 22 that inputs pressure information output from a pressure information measurement means 10 that measures pressure information including pressure values derived from the soles of a walker's feet and information about the measurement times and locations of the pressure values; a control means 30 (corresponding to the pressure information classification means and foot shape classification means of the present invention) that classifies the pressure information input to the pressure information input means 22 into pressure information for each arbitrary foot shape and classifies the classified arbitrary foot shapes into foot shapes for each arbitrary person; and an output means 24 that outputs the foot shape information for each arbitrary person classified by the control means 30, all of which are electrically connected. In this gait analysis system 20, the pressure information derived from the soles of a walker's feet output from the pressure information measurement means 10 is input from the pressure information input means 22, and the control means 30 classifies this pressure information into pressure information for each arbitrary foot shape and classifies it into foot shapes for each arbitrary person, and outputs it from the output means 24. This makes it possible to obtain information that can be used to analyze information about a walker's gait with high accuracy and in a short time, from only the pressure information derived from the soles of the walker's feet, without causing any issues with the walker's portrait rights.

[0021] The pressure information measurement means 10 is a known pressure sensor, and may be, for example, a resin sheet equipped with multiple pressure sensors at predetermined intervals. When a pedestrian walks on this resin sheet, the pressure sensors located on the soles of the pedestrian's feet detect the pedestrian's movements and output position information including information about the position of the detected pressure sensor, time information including information about the time of detection, and pressure information including the detected pressure value to the pressure information input means 22. In this manner, the control means 30 can acquire information about the pedestrian's walking based on this pressure information. Note that the multiple pressure sensors are arranged in a grid pattern as shown in FIG. 2(A), and the positions of the pressure sensors are represented by coordinate positions on an XY coordinate system. Information about the position information of the pressure sensors, the pressure values detected by the pressure sensors, and the time of detection is output to the pressure information input means 22 as pressure information in a table format in which coordinate positions correspond to pressure values for each time period, as shown in FIG. 2(B). Here, FIG. 2(A) is a schematic diagram illustrating an example of the pressure information measurement means 10, and FIG. 2(B) is an explanatory diagram illustrating an example of pressure information.

[0022] The pressure information input means 22 is a known input / output means that outputs a signal including the pressure information output from the pressure information measurement means 10 to the control means 30 .

[0023] The output means 24 is a known output means that outputs a signal including information about the walking of a pedestrian output from the control means 30. The output means 24 may also serve as a display means that outputs and displays the signal including information about the walking of a pedestrian on a monitor, a printer, or the like.

[0024] The control means 30 is configured as a microprocessor centered around a CPU 31, and is electrically connected via a bus 35 to a ROM 32 that stores various processing routines such as a gait analysis processing routine that classifies pressure information by arbitrary foot shape and by arbitrary person, a RAM 33 that temporarily stores pressure information input from the pressure information input means 22, and an interface 34 (hereinafter referred to as "I / F 34") that transmits and receives various signals between the pressure information input means 22, output means 24, etc. The control means 30 classifies pressure information input from the pressure information input means 22 by arbitrary person, and outputs information related to the pedestrian's gait from the output means 24.

[0025] Next, the operation of classifying pressure information input from the pressure information input means 22 will be described using a gait analysis processing routine executed by the control means 30 as an example. This gait analysis processing routine is executed repeatedly at predetermined times (for example, when a start signal is received or when a signal including pressure information is received). Note that FIG. 3 is a flowchart showing an example of the gait analysis processing routine.

[0026] When this gait analysis processing routine is executed by CPU 31, raw data processing (step S100) is executed to process the pressure information input to pressure information input means 22 into data suitable for subsequent processing, and the pressure information is converted into data in a format suitable for foot shape classification processing (step S200), which will be described later. In this raw data processing, the input pressure information (see FIG. 2(B)) is used to process the information required for foot shape classification processing (step S200).

[0027] Here, the raw data processing will be described in detail with reference to FIG. 4. Here, FIG. 4 is a flowchart showing an example of raw data processing. When raw data processing is executed by CPU 31, the value 0 is assigned to a variable t, which indicates the table number of the pressure information (step S110), and the pressure information included in the t-th table is temporarily stored in RAM 33 (step S120). Next, CPU 31 determines whether the pressure value stored in the pressure information is equal to or greater than a predetermined threshold (step S130), and if it determines that the pressure value is smaller than the predetermined threshold, assigns the value 0 to the pressure value (step S140). In this way, by setting values smaller than the predetermined threshold to 0, pressure values measured based on pressures not derived from the foot shape can be eliminated, thereby reducing the calculation effort and time compared to performing subsequent processing based on all pressure values.

[0028] Next, if the CPU 31 determines in step S130 that the pressure value is equal to or greater than the threshold value, or after step S140 is executed, it determines whether step S130 has been executed for all pressure values, and if it determines that step S130 has not been executed for all pressure values, it executes step S130 for the other pressure values. In this way, all pressure values lower than the threshold value can be replaced with a value of zero.

[0029] On the other hand, if the CPU 31 determines in step S150 that all pressure values have been processed, it determines whether all pressure values are zero (step S160), and if it determines that all pressure values are zero, it deletes the pressure table. Since it is clear that such a pressure table does not contain pressure values measured using pressure derived from the foot shape, deleting this pressure table reduces the effort required for the processing described below and shortens the calculation time.

[0030] Next, if CPU 31 determines in step S160 that any of the pressure values is not zero, or after step S170 has been executed, it adds the value 1 to variable t (step S180), determines whether the value of variable t is equal to or greater than the number of tables, and if it determines that the value is less than the number of tables, it executes step S130 again. By doing this, for all tables in which pressure values are stored for each time period, only tables for times in which pressure values equal to or greater than a threshold value are stored are extracted, and pressure values smaller than the threshold value are set to 0, thereby organizing the information into the necessary information, reducing the effort required for performing the processing described below, and shortening the calculation time.

[0031] As shown in Fig. 3, the CPU 31 executes a process for classifying by foot shape (step S200) following the raw data process (step S100). In this process for classifying by foot shape, a collection of pressure values is clustered for each pressure value derived from the same foot shape. In this way, the pressure values can be clustered as a collection of pressure values derived from the foot shape, and therefore the collection of clustered pressure values can be treated as the pressure value for one step in walking.

[0032] The foot shape classification process will now be described in detail with reference to FIG. 5. FIG. 5 is a flowchart showing an example of the foot shape classification process. When the foot shape classification process is executed by the CPU 31, a three-dimensional clustering process is executed (step S210), and the pressure information is clustered. Specifically, the clustering is performed using k-means clustering, a non-hierarchical clustering algorithm. At this time, the clustered data is represented in the position and time space of the foot shape by a set of pressure values, as shown in FIG. 6. Note that FIG. 6 is a schematic diagram showing the clustered pressure information, showing the change in position on the XY coordinate plane and the change in time in the height direction.

[0033] Next, the CPU 31 assigns consecutive cluster numbers starting from 1 to each cluster clustered in step S210 (step S220), and assigns the value 1 to variables n and m indicating the cluster numbers (step S230). Next, it determines whether the size of the nth cluster is equal to or greater than a pre-stored threshold (step S240). If it is determined that the size is equal to or greater than the threshold, it calculates a pressure distribution transition vector (step S250). Note that the "cluster size" here can refer to, for example, the area of the XY coordinate plane when represented in the schematic diagram shown in FIG. 6. Since the area of this XY coordinate plane represents the size of the footprint, by using the area of the XY coordinate plane as the "cluster size," it is possible to eliminate clusters resulting from small objects such as canes. Additionally, the height value when represented in the schematic diagram shown in FIG. 6 can also be used as the "cluster size." Since this height value represents the time the footprint remains on the ground, by using the height value as the "cluster size," it is possible to eliminate, for example, clusters resulting from objects that remain on the ground for a short period of time that would not occur during normal walking. Furthermore, the "pressure distribution transition vector" refers to a vector (a vector in three-dimensional spatial coordinates consisting of X coordinates, Y coordinates, and time t) from the start point to the end point, calculated by calculating the pressure center point or area center point in the frame when the cluster of clustered pressure value points occurs and immediately before the cluster disappears, and then calculating the point on the coordinates on the occurrence side as the start point, and calculating the end point from the point immediately before the disappearance and the pressure value. This pressure distribution transition vector is a three-dimensional vectorization using the spread of reaction points from the occurrence to the disappearance of the cluster, the trajectory of the foot pressure center, etc.

[0034] Next, CPU 31 stores the n-th cluster information and the n-th pressure distribution transition vector in the m-th structure array (step S260), and adds a value of 1 to variable n and a value of 1 to variable m (step S270). By doing so, the pressure distribution transition vector corresponding to the cluster information can be stored in association with the structure array.

[0035] On the other hand, if CPU 31 determines in step S240 that the size of the n-th cluster is less than the threshold, it adds a value of 1 to variable n and a value of 1 to variable m (step S270). If the size of the cluster is less than the threshold in this way, it is determined that the cluster is not a cluster of pressure values derived from footprints, and therefore there is no need to calculate the pressure distribution transition vector of such a cluster.

[0036] Next, CPU 31 determines whether the value of variable n is equal to or greater than the total number of clusters (step S280), and if it is determined that the value is less than the total number of clusters, executes step S240 again. By doing so, if the cluster size is equal to or greater than the threshold value for all clusters, it is possible to calculate the pressure distribution transition vector and store it in the structure array.

[0037] On the other hand, if it is determined in step S280 that the value of variable n is less than the total number of clusters, CPU 31 sorts the structure array in ascending order of variable t (step S290) and terminates the foot shape classification process. By doing so, it is possible to obtain chronological structure array data that stores clusters and pressure distribution transition vectors corresponding to the clusters.

[0038] As shown in Fig. 3, the CPU 31 executes the foot print classification process (step S200) followed by the person classification process (step S300). In this person classification process, the cluster representing one foot print obtained in the foot print classification process is classified by person. This allows the foot print to be classified for each pedestrian, and therefore multiple foot prints can be classified for any pedestrian, thereby obtaining information about the gait of each classified pedestrian.

[0039] The person-by-person classification process will now be described in detail with reference to Fig. 7. Fig. 7 is a flowchart showing an example of the person-by-person classification process. When the person-by-person classification process is executed by the CPU 31, the value 1 is assigned to each of variables j and k (step S310), and the j-th cluster is registered as a reference cluster (step S320) and temporarily stored in the RAM 33. Note that this variable j means a variable corresponding to each cluster number classified in the foot shape classification process.

[0040] Next, the CPU 31 determines whether the reference cluster temporarily stored in the RAM 33 in step S320 is in a stopped state (step S330). If it is determined that the reference cluster is in a stopped state, the CPU 31 registers the estimated presence range for the stopped state as the reference estimated presence range (step S340). If it is determined that the reference cluster is not in a stopped state, the CPU 31 registers the estimated presence range for the walking state as the reference estimated presence range (step S350). Specifically, the CPU 31 determines whether the value of the time component of the pressure distribution transition vector included in the reference cluster is equal to or greater than a predetermined threshold (e.g., 2000 ms or 1500 ms). If the value is equal to or greater than the threshold, the CPU 31 determines that the reference cluster is in a stopped state. If the value is less than the threshold, the CPU 31 determines that the reference cluster is not in a stopped state. Experiments conducted by the inventors have confirmed that in a normal walking state, the value of the time component of the pressure distribution transition vector is around 1000 ms at most. Therefore, by setting the threshold to a value greater than 1000 ms, the CPU 31 can determine whether the reference cluster is in a walking state or a stopped state. Here, "estimated presence range" means the range in which the next footprint is likely to be located, and by using different estimated presence ranges when the cluster derived from the current footprint is in a stationary state and when it is in a walking state, more accurate and faster processing can be performed when classifying clusters derived from the footprints of the same person.

[0041] Here, the "estimated presence range" will be explained in more detail. This estimated presence range refers to a range where the next footstep is likely to be located, and it means different ranges for the estimated presence range in the walking state and the estimated presence range in the stationary state. Specifically, the estimated presence range in the walking state refers to a predetermined range centered on a point a predetermined distance away from the center point of the pressure distribution transition vector in the direction of the pressure distribution transition vector, while the estimated presence range in the stationary state refers to a position of any predetermined shape (e.g., a rectangular shape or a sector shape with its center point in the direction of the center point of the pressure distribution transition vector) centered on the center point of the pressure distribution transition vector. Normally, when stationary, both feet are located close to each other, and when walking, both feet are located at positions spaced apart by a stride width. Therefore, by defining the estimated presence range in the stationary state and the estimated presence range in the walking state at different positions and using different estimated presence ranges for the stationary state and the walking state, when extracting a cluster derived from the footstep of the next step, the target cluster can be set to an appropriate range, thereby enabling more accurate and faster processing.

[0042] Next, the CPU 31 determines whether or not the cluster information classified in the foot shape classification process exists within the reference estimated existence range (step S360), and if it determines that the cluster information exists within the reference estimated existence range, it determines whether or not the cluster information determined to exist within the reference estimated existence range is estimated to be the same person as the reference cluster (step S370). In this way, by determining whether or not only the cluster information existing within the reference estimated existence range is estimated to be the same person, it is possible to perform processing faster than when making an estimation for all cluster information.

[0043] Here, a method for determining whether a reference cluster and a reference cluster are estimated to be the same person will be described in detail. For example, this estimation method may involve comparing the direction of the pressure distribution transition vector included in the reference cluster with the direction of the pressure distribution transition vector included in the cluster information to be compared. If one of the pressure distribution transition vectors points leftward and the other points rightward relative to the direction of travel, the clusters may be estimated to be the same person. Pressures derived from the footprints of the same person always appear in left-right order. Therefore, if the pressure distribution transition vectors are located within the estimated presence range and point leftward and rightward relative to the direction of travel, the clusters are likely to be derived from the footprints of the same person. Furthermore, if the difference between the pressure values included in the reference cluster information and the pressure values included in the cluster information to be compared is equal to or less than a threshold, the clusters may be estimated to be the same person. Because pressure values derived from footprints of the same person are similar, a small difference in pressure values may be likely to be derived from the footprints of the same person. Furthermore, if the j−1th cluster information is available, the clusters may be estimated to be the same person if the footprint shapes included in the j−1th cluster information and the j+1th cluster information are similar. This is because if the footprint shapes are similar, it is highly likely that the clusters originated from the footprints of the same person. Here, "similar" may be determined, for example, when the distribution of pressure values in the cluster (e.g., the distribution on the XY coordinate plane when represented in the schematic diagram shown in FIG. 6) is highly similar. Because the distribution on the XY coordinate plane is derived from the external shape of the derived footprint, a high similarity in the distribution of pressure values indicates a high probability that the clusters originated from the same person. Additionally, a high similarity in the distribution of high and low pressure values may be determined to be "similar." When walking, the location of high (or low) pressure values often depends on personal habits and habit. Therefore, a high similarity in the distribution of high and low pressure values indicates a high probability that the clusters originated from the same person. Alternatively, after determining which part of the foot (e.g., heel, toe, etc.) each pressure value corresponds to, the time change trend of the pressure values (e.g., the transition over time of the sum of the pressure value responses constituting the cluster from the time the foot touches the ground to the time the foot leaves the ground) may be used.Furthermore, two or more of these may be combined for judgment, or each may be used as a parameter, and judgment may be made by weighting and combining the parameters.

[0044] In step S370, if CPU 31 determines that the cluster information is estimated to be the same person as the reference cluster, it registers the cluster information as the kth person information and deletes the information from the cluster list (step S380). By doing so, it is possible to extract cluster information that is estimated to be the same person as the kth reference cluster and to exclude it from subsequent processing, thereby further speeding up the process.

[0045] After executing step S380, or when it is estimated in step S370 that the person is not the same as the reference cluster, the CPU 31 determines whether or not there is other cluster information within the reference estimated existence range (step S390), and when it is determined that there is other cluster information, it executes step S370 again for the other cluster information. By doing so, when multiple pieces of cluster information exist within the reference estimated existence range, it is possible to determine for each piece of cluster information whether or not it is estimated to be the same person as the reference cluster.

[0046] On the other hand, if CPU 31 determines in step S390 that no other cluster information exists, it adds a value of 1 to each of variables j and k (step S400), and determines whether variable j is equal to or greater than a constant J, which is the total number of clusters extracted in the foot shape classification process (step S410). If it is determined that variable j is less than constant J, it executes step S320 again. On the other hand, if it is determined in step S410 that variable k is equal to or greater than constant J, it ends this routine. In this way, all of the cluster information classified in the foot shape classification process can be classified by person.

[0047] 3, the CPU 31 executes a gait analysis process (step S500) following the person classification process (step S300). In this gait analysis process, the walking state of each person obtained in the person classification process is analyzed. In this way, information about the walking of each walker can be obtained from the multiple foot shapes classified for each person.

[0048] The gait analysis process will now be described in detail with reference to FIG. 8. FIG. 8 is a flowchart showing an example of the gait analysis process. When the gait analysis process is executed by the CPU 31, a value of 1 is assigned to a variable h (step S510), and cluster information of the h-th person classified in the person-by-person classification process is temporarily stored in the RAM 33 (step S520). Next, a stride length and a step width are calculated from the cluster information of the h-th person temporarily stored in the RAM 33 (step S530). The cluster information of the h-th person includes cluster information corresponding to the h-th person classified in the person-by-person classification process, among the foot shapes classified in the foot shape classification process, so that the stride length and the step width can be calculated based on the respective cluster information.

[0049] Next, CPU 31 calculates the walking speed and walking period (step S540). Since the cluster information of the h-th person includes the cluster information corresponding to the h-th person classified by the person classification process among the footprints classified by the foot shape classification process, the walking speed and walking period can be calculated based on the stride length and step width calculated in step S530 and the measurement interval derived from the cluster information.

[0050] Next, CPU 31 calculates the transition of the locus of the center of pressure (step S550). Since the cluster information of the h-th person includes the cluster information corresponding to the h-th person classified by the person classification process among the footprints classified by the foot shape classification process, the locus of the center of pressure can be calculated.

[0051] Next, the CPU 31 calculates the pressure transition for each step and each body part (step S560). Since the cluster information for the h-th person includes the cluster information corresponding to the h-th person classified by the person classification process among the footprints classified by the foot shape classification process, it is possible to calculate the pressure transition for each step and each body part.

[0052] Next, CPU 31 adds the value 1 to variable h (step S570), and determines whether variable h is equal to or greater than the person number calculated in the classification process by number of people (final value of variable k) (step S580). If it is determined that variable h is less than the person number, it executes step S530 again, and if it is determined that variable h is equal to or greater than the person number, it ends this routine. In this way, it is possible to calculate walking information for cluster information corresponding to all people classified in the classification process by person.

[0053] According to the gait analysis system 20 of the embodiment described above in detail, the pressure information input to the pressure information input means 22, which inputs pressure information output from the pressure information measurement means 10 that measures pressure information including information about the pressure value from the soles of the walker's feet, the measurement time, and the measurement location, is classified by a pressure information classification process (step S200) that classifies the pressure information into pressure information for each arbitrary foot shape, and the arbitrary foot shapes classified by the pressure information classification process are then classified into foot shapes for each arbitrary person by a foot shape classification process (step S300), thereby making it possible to obtain information about the walker's gait from only the pressure information from the soles of the walker's feet without causing any problems with the walker's portrait rights.

[0054] Furthermore, in step 500, the behavioral characteristics of any person, including the stride length, stride width, walking speed, or walking cycle, are estimated by gait analysis processing from the foot shapes classified for each person by the foot shape classification processing. This makes it possible to obtain information about the pedestrian's walking from only the pressure information derived from the soles of the feet, without causing any problems with the pedestrian's portrait rights, and by using this information about walking, the behavioral characteristics of any person can be estimated.

[0055] Furthermore, in step S200, by using a clustering algorithm that utilizes the K-means method to cluster pressure information derived from the same foot shape, the pressure information is classified into pressure information for each arbitrary foot shape, and information about the pedestrian's gait can be obtained with high accuracy and in a short time from only the pressure information derived from the soles of the pedestrian's feet, without causing any issues with the pedestrian's portrait rights.

[0056] Furthermore, in step S200, by classifying the foot shape of any person based on the pressure distribution transition vector, information about the pedestrian's gait can be obtained with high accuracy and in a short time from only the pressure information derived from the soles of the pedestrian's feet, without causing any problems with the pedestrian's portrait rights.

[0057] Then, in step S200, by classifying the foot shape of any person based on the reference estimated existence range, information about the pedestrian's gait can be obtained with high accuracy and in a short time from only the pressure information derived from the soles of the pedestrian's feet, without causing any problems with the pedestrian's portrait rights.

[0058] It goes without saying that the present invention is not limited to the above-described embodiment, and can be embodied in various forms as long as they fall within the technical scope of the present invention.

[0059] For example, in the above-described embodiment, step S100 is executed before step S200 is executed, but step S100 may not be executed. Even in this case, the same effects as those of the above-described embodiment can be obtained.

[0060] In the above-described embodiment, step S500 is executed after step S300, but step S500 may be omitted. By analyzing the data output in step S300 using a known information processing device and information processing method, information on pedestrian characteristics can be calculated. [Industrial Applicability]

[0061] As shown in the above-described embodiment, the present invention can be used in the field of information processing, particularly as a gait analysis system. [Explanation of symbols]

[0062] 10...pressure information measuring means, 20...gait analysis system, 22...pressure information input means, 24...output means, 30...control means, 31...CPU, 32...ROM, 33...RAM, 34...interface, 35...bus.

Claims

1. a pressure information input means for inputting pressure information output from a pressure information measurement means for measuring pressure information including a pressure value from the sole of a walker's foot, and information regarding a measurement time and a measurement location of the pressure value; a pressure information classification means for classifying the pressure information input to the pressure information input means into pressure information for each arbitrary foot shape; a foot shape classifying means for classifying the arbitrary foot shapes classified by the pressure information classifying means into arbitrary foot shapes for each person based on a pressure distribution transition vector; characterized in that it comprises Gait analysis system.

2. The gait analysis system according to claim 1 , a gait information analysis means for analyzing gait information of an arbitrary person from the foot shapes classified for each arbitrary person by the foot shape classification means; characterized in that it comprises Gait analysis system.

3. the walking information analysis means calculates at least one of a stride length, a step width, a walking speed, and a walking cycle for each of the arbitrary persons from the foot shapes classified for each of the arbitrary persons by the foot shape classification means, The directional analysis system of claim 2 .

4. The pressure information classification means classifies the pressure information into pressure information for each arbitrary foot shape by clustering the pressure information into pressure information derived from the same foot shape using a clustering algorithm that utilizes the K-means method. The gait analysis system according to any one of claims 1 to 3.

5. A pressure information input step of inputting pressure information output from a pressure information measuring means that measures pressure information including a pressure value from the sole of a walker's foot, and information regarding a measurement time and a measurement location of the pressure value; a pressure information classification step of classifying the pressure information inputted in the pressure information input step into pressure information for each arbitrary foot shape; a foot shape classification step of classifying the arbitrary foot shapes classified in the pressure information classification step into an arbitrary person's foot shape based on a pressure distribution transition vector; characterized in that it comprises Gait analysis methods.

6. A program for executing the gait analysis method according to claim 5 on one or more computers. Rum.

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