Interpolation device, gait measurement system, interpolation method, and program

Abstract

This interpolation device is provided with: a gait information processing unit that, in order to interpolate data in a missing section of data included in time-series data of sensor data on movements of the legs together with a characteristic of the missing section of the data, uses the time-series data of the sensor data to generate a walking waveform for each walking cycle and specifies the missing section in the time-series data; a missing information processing unit that calculates a walking phase for the specified missing section; and an interpolation unit that uses data on the walking phase in the missing section in the walking waveform of a different walking cycle from the walking cycle including the missing section to generate interpolation data for interpolation in the missing section, and uses the generated interpolation data to interpolate the missing section.

Description

[Technical field]

[0001] The present disclosure relates to an interpolation device that interpolates missing data in time series data. [Background technology]

[0002] Due to the growing interest in health care for managing physical condition, services that measure the characteristics included in walking patterns (also called gait) and provide users with information according to the gait are attracting attention. For example, devices that analyze users' gait by mounting a measuring device including an inertial sensor on footwear such as shoes have been developed. In such gait analysis, when data measured by a sensor (also called sensor data) is wirelessly transmitted from the measuring device, data loss may occur due to factors such as communication failure.

[0003] Patent Document 1 discloses a gait measurement system that aims to remove drift that shows different characteristics between the period when the foot is on the ground (stance phase) and the period when the foot is off the ground (swing phase). The system of Patent Document 1 detects at least one walking phase from acceleration data measured by an inertial measurement unit, and calculates velocity data by time-integrating the acceleration data. The system of Patent Document 1 calculates a correction amount corresponding to each walking phase based on the walking phase and the velocity data. The system of Patent Document 1 calculates corrected velocity data by subtracting the correction amount from the velocity data corresponding to each walking phase, and calculates trajectory data by time-integrating the calculated corrected velocity data.

[0004] Patent Document 2 discloses a signal interpolation method for interpolating missing portions of an input signal. In the method of Patent Document 2, the period of the signal before the input signal is missing is detected, and the detected input signal is stored. In the method of Patent Document 2, the stored signal is read out in response to the detection of the input signal, and the missing portion is interpolated. In the method of Patent Document 2, the polarity and level at the start of the input signal missing are identified, and accumulated waveform signals with polarity and level that maintain continuity with the input signal immediately before the missing are sequentially read out. In the method of Patent Document 2, the level of the accumulated waveform signal is gradually made to approximate the level of the input signal, thereby interpolating the missing portion of the input signal so that continuity is maintained.

[0005] Patent Document 3 discloses a data interpolation device that interpolates missing data measured by sensors or devices connected to a network. The device of Patent Document 3 stores a time series model of data and an interpolation method for interpolating missing data. The interpolation method corresponds to a phase specified as a range in which the time change of data is common in the time series model. The device of Patent Document 3 accumulates data output from an external device for each external device, and determines which time series model the time change pattern of the accumulated data corresponds to. The device of Patent Document 3 determines which phase of the determined time series model a missing part of the accumulated data is located in, and interpolates the missing part using an interpolation method according to the determined phase.

[0006] Patent Document 4 discloses a bioinformation measuring device that complements missing parts included in bioinformation. The device of Patent Document 4 complements missing parts included in time series data (biological information) measured by a pulse wave sensor and an electrocardiogram sensor. The device of Patent Document 4 complements missing parts of data measured by one of the two sensors based on data measured by the other sensor. The device of Patent Document 4 specifies a corresponding section of data before the start point of the missing part of the time series data measured by the other sensor, where an index indicating a correlation between the missing part and time series data of a section at the same time satisfies a predetermined condition. The device of Patent Document 4 complements the missing part using time series data of a section at the same time as the corresponding section of data measured by one sensor. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Publication No. 2020 / 105115 [Patent Document 2] Japanese Patent Publication No. 61-227432 [Patent Document 3] JP 2014-102779 A [Patent Document 4] JP 2010-264168 A Summary of the Invention [Problem to be solved by the invention]

[0008] According to the method of Patent Document 1, it is possible to remove drifts that show clearly different characteristics in different gait periods, such as the stance phase and swing phase. However, the method of Patent Document 1 cannot interpolate missing parts that occur locally in individual gait periods, such as the stance phase and swing phase. Therefore, the method of Patent Document 1 cannot restore the features included in the missing parts.

[0009] In the method of Patent Document 2, when there is a gap in the input signal, the accumulated signal is repeatedly read out to interpolate the gap so as to obtain continuity from the start of the gap to the end of the gap. According to the method of Patent Document 2, when the periodicity and shape of the input signal are simple, the gap can be interpolated. However, in the method of Patent Document 2, when the regularity and waveform are complex as in sensor data, it is difficult to interpolate the gap so as to obtain continuity from the start of the gap to the end of the gap. Therefore, in the method of Patent Document 2, there is a possibility that the features included in the gap may be lost.

[0010] In the method of Patent Document 3, it is necessary to store a time series model and an interpolation method in advance. Therefore, in the method of Patent Document 3, it is not possible to interpolate missing data for which a time series model or an interpolation method cannot be specified.

[0011] In the method of Patent Document 4, two mutually related time series data measured at the same time are matched, and missing parts of one time series data are complemented based on the other data. Therefore, the method of Patent Document 4 cannot complement missing parts unless there is time series data measured at the same time.

[0012] An object of the present disclosure is to provide an interpolation device and the like capable of interpolating data of a missing section including characteristics of the missing section of data contained in time-series data of sensor data. [Means for solving the problem]

[0013] An interpolation device of one aspect of the present disclosure includes a gait information processing unit that generates a walking waveform for each walking cycle using time-series data of sensor data related to foot movement and identifies missing sections of data in the time-series data, a missing information processing unit that calculates the walking phase of the identified missing section, and an interpolation unit that generates interpolation data that interpolates the missing section using walking phase data of the missing section in a walking waveform of a walking cycle different from the walking cycle that includes the missing section, and interpolates the generated interpolation data into the missing section.

[0014] In an interpolation method according to one aspect of the present disclosure, a computer generates a walking waveform for each walking cycle using time-series data of sensor data related to foot movement, identifies missing sections of data in the time-series data, calculates the walking phase of the identified missing section, generates interpolation data that interpolates the missing section using data on the walking phase of the missing section in a walking waveform of a walking cycle different from the walking cycle that includes the missing section, and interpolates the generated interpolation data into the missing section.

[0015] A program according to one aspect of the present disclosure causes a computer to execute the following processes: generating a gait waveform for each gait cycle using time series data of sensor data related to foot movement; identifying missing sections of data in the time series data; calculating the gait phase of the identified missing section; generating interpolation data that interpolates the missing section using data on the gait phase of the missing section in a gait waveform of a gait cycle different from the gait cycle that includes the missing section; and interpolating the generated interpolation data into the missing section. Effect of the Invention

[0016] According to the present disclosure, it is possible to provide an interpolation device and the like capable of interpolating data of a missing section including the characteristics of the missing section of data contained in time-series data of sensor data. [Brief description of the drawings]

[0017] [Figure 1] 1 is a block diagram showing an example of the configuration of a gait measurement system according to a first embodiment. [Diagram 2] FIG. 1 is a conceptual diagram showing an example of the arrangement of measurement devices in a gait measurement system according to a first embodiment. [Diagram 3] FIG. 2 is a conceptual diagram for explaining an example of a coordinate system set in a measurement device of the gait measurement system according to the first embodiment. [Figure 4] FIG. 1 is a conceptual diagram for explaining a walking cycle. [Diagram 5] FIG. 11 is a conceptual diagram for explaining defects that occur in time-series data of sensor data. [Figure 6] FIG. 11 is a conceptual diagram for explaining an example of interpolating missing data that occurs in time-series data of sensor data using an analytical method. [Figure 7] FIG. 2 is a conceptual diagram for explaining interpolation of a missing section by the interpolation device of the gait measurement system according to the first embodiment. [Figure 8] 1 is a block diagram showing an example of the configuration of a measurement device of a gait measurement system according to a first embodiment. FIG. [Figure 9] 1 is a block diagram showing an example of a configuration of an interpolation device of the gait measurement system according to the first embodiment. FIG. [Figure 10] FIG. 2 is a conceptual diagram showing an example of interpolation of a defect by the interpolation device of the gait measurement system according to the first embodiment. [Figure 11] FIG. 2 is a conceptual diagram for explaining interpolation of a defect by an interpolation device of the gait measurement system according to the first embodiment. [Figure 12] 1 is a block diagram showing an example of a configuration of a gait measurement device of a gait measurement system according to a first embodiment. FIG. [Figure 13] 5 is a flowchart for illustrating an example of the operation of a gait information processing unit included in the interpolation device of the gait measurement system according to the first embodiment. [Figure 14] 5 is a flowchart for illustrating an example of the operation of a missing information processing unit included in the interpolation device of the gait measurement system according to the first embodiment. [Figure 15] 5 is a flowchart for illustrating an example of the operation of an interpolation unit included in the interpolation device of the gait measurement system according to the first embodiment. [Figure 16] 13 is a flowchart for illustrating another example of the operation of the interpolation unit included in the interpolation device of the gait measurement system according to the first embodiment. [Figure 17] FIG. 11 is a block diagram showing an example of the configuration of a gait measurement system according to a second embodiment. [Figure 18]FIG. 11 is a block diagram showing an example of the configuration of an interpolation device of a gait measurement system according to a second embodiment. [Figure 19] 1 is an example of time-series data of sensor data spanning a plurality of walking cycles. [Figure 20] 13 is an example of a gait waveform obtained by normalizing time series data for one walking cycle, extracted from time series data of sensor data spanning a plurality of walking cycles. [Figure 21] This is a frequency distribution of data values ​​in walking phase P1 and walking phase P2, with walking waveforms of multiple walking cycles as a sample group (whole). [Figure 22] 13 is a graph showing the correlation of standard deviations in the overall distribution of data values ​​of data N and data N+1 of consecutive walking phases. [Diagram 23] 13 is a graph showing the correlation coefficient of the deviation value according to the distance from a target walking phase. [Figure 24] 10 is a graph for explaining the interpolation of a missing section by an interpolation device according to a second embodiment. [Diagram 25] 10 is a flowchart illustrating an operation of the interpolation device according to the second embodiment. [Figure 26] FIG. 13 is a block diagram showing an example of a configuration of an interpolation device according to a third embodiment. [Figure 27] FIG. 2 is a block diagram showing an example of a hardware configuration for executing control and processing according to each embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Hereinafter, the embodiments for carrying out the present invention will be described with reference to the drawings. However, the embodiments described below are limited in a manner that is technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following. In addition, in all the drawings used to explain the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. In addition, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0019] (First embodiment) First, a gait measurement system according to a first embodiment will be described with reference to the drawings. The gait measurement system of this embodiment measures physical quantities (sensor data) related to foot movement by a measurement device installed in footwear worn by a user. The measurement device includes an acceleration sensor and an angular velocity sensor. For example, the physical quantities related to foot movement include acceleration in three axial directions (also called spatial acceleration) measured by an acceleration sensor, and angular velocity around three axes (also called spatial angular velocity) measured by an angular velocity sensor. The gait measurement system of this embodiment interpolates missing parts of data that occur during communication of measured sensor data, etc.

[0020] (composition) 1 is a block diagram showing the configuration of a gait measurement system 1 of this embodiment. Gait measurement system 1 includes a measurement device 11, an interpolation device 12, and a gait measurement device 13. Interpolation device 12 may be connected to measurement device 11 and gait measurement device 13 by wire or wirelessly. Furthermore, measurement device 11, interpolation device 12, and gait measurement device 13 may be configured as a single device. Furthermore, gait measurement system 1 may be configured from interpolation device 12 and gait measurement device 13, excluding measurement device 11.

[0021] The measuring device 11 is installed on the foot. For example, the measuring device 11 is installed on footwear such as shoes. For example, the measuring device 11 is placed at a position on the back side of the arch of the foot. The measuring device 11 includes an acceleration sensor and an angular velocity sensor. The measuring device 11 measures acceleration (also called spatial acceleration) measured by an acceleration sensor and angular velocity (also called spatial angular velocity) measured by an angular velocity sensor as physical quantities related to the movement of the foot of a user wearing the footwear. The physical quantities related to the movement of the foot measured by the measuring device 11 also include velocity, angle, and position (trajectory) calculated by integrating the acceleration and angular velocity. The measuring device 11 converts the measured physical quantities into digital data (also called sensor data). The measuring device 11 transmits the converted sensor data to the interpolation device 12. The sensor data includes a timestamp corresponding to the time when the sensor data was acquired. The timestamp is a time series number assigned to the sensor data. For example, the measuring device 11 is connected to the interpolation device 12 via a mobile terminal (not shown) carried by the user.

[0022] The mobile terminal (not shown) is a communication device that can be carried by a user. For example, the mobile terminal is a mobile communication device having a communication function such as a smartphone, a smart watch, or a mobile phone. The mobile terminal receives sensor data related to the movement of the user's feet from the measurement device 11. The mobile terminal transmits the received sensor data to a server or cloud in which the interpolation device 12 is implemented. Note that the functions of the interpolation device 12 and the gait measurement device 13 may be realized by application software or the like installed on the mobile terminal. In this case, the mobile terminal processes the received sensor data by the application software or the like installed on the mobile terminal.

[0023] The measurement device 11 is realized by, for example, an inertial measurement device including an acceleration sensor and an angular velocity sensor. An example of an inertial measurement device is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor that measures acceleration in three axial directions and an angular velocity sensor that measures angular velocity around three axes. The measurement device 11 may also be realized by an inertial measurement device such as a VG (Vertical Gyro) or an AHRS (Attitude Heading). The measurement device 11 may also be realized by a GPS / INS (Global Positioning System / Inertial Navigation System).

[0024] FIG. 2 is a conceptual diagram showing an example of the arrangement of the measuring device 11 in the shoe 100. In the example of FIG. 2, the measuring device 11 is arranged at a position that hits the back side of the arch of the foot. For example, the measuring device 11 is arranged in an insole inserted into the shoe 100. For example, the measuring device 11 is arranged on the bottom surface of the shoe 100. For example, the measuring device 11 may be embedded in the body of the shoe 100. The measuring device 11 may be detachable from the shoe 100, or may not be detachable from the shoe 100. Note that the measuring device 11 may be arranged at a position other than the back side of the arch of the foot, as long as it can acquire sensor data related to the movement of the foot. The measuring device 11 may also be installed in socks worn by the user or in an accessory such as an anklet worn by the user. The measuring device 11 may also be directly attached to the foot or embedded in the foot. FIG. 2 shows an example in which the measuring device 11 is arranged in the shoe 100 on the right foot side, but the measuring device 11 may also be arranged in the shoes 100 for both feet. If the measuring device 11 is placed in the shoes 100 for both feet, the gait can be measured based on the movements of both feet.

[0025] 3 is a conceptual diagram for explaining a local coordinate system (x-axis, y-axis, z-axis) set in the measuring device 11 and a world coordinate system (X-axis, Y-axis, Z-axis) set with respect to the ground when the measuring device 11 is installed on the back side of the arch of the foot. In the world coordinate system (X-axis, Y-axis, Z-axis), when the user is standing upright, the horizontal direction of the user is set as the X-axis direction (rightward is positive), the direction in front of the user (direction of movement) is set as the Y-axis direction (forward is positive), and the direction of gravity is set as the Z-axis direction (vertically upward is positive). In this embodiment, a local coordinate system consisting of the x-direction, y-direction, and z-direction is set with the measuring device 11 as the reference.

[0026] FIG. 4 is a conceptual diagram for explaining a step cycle based on the right foot. FIG. 4 shows one walking cycle of the right foot, starting from the time when the heel of the right foot lands on the ground and ending from the time when the heel of the right foot lands on the ground. FIG. 4 shows a walking cycle normalized by setting one walking cycle of the right foot as 100%. One walking cycle of one foot is roughly divided into a stance phase in which at least a part of the sole of the foot is in contact with the ground, and a swing phase in which the sole of the foot is off the ground. In this embodiment, the walking cycle is normalized so that the stance phase occupies 60% and the swing phase occupies 40%. The stance phase is further divided into an early stance phase T1, a mid stance phase T2, a final stance phase T3, and a pre-swing phase T4. The swing phase is further divided into an early swing phase T5, a mid swing phase T6, and a final swing phase T7. The walking waveform for one step cycle does not have to start from the time when the heel hits the ground. For example, the walking waveform for one step cycle may start and end from the time when the heel lifts off the ground.

[0027] Figure 4(a) shows the event where the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). Figure 4(b) shows the event where the toe of the left foot leaves the ground (opposite toe off) (OTO: Opposite Toe Off) while the sole of the right foot is in contact with the ground (HR: Heel Rise). Figure 4(d) shows the event where the heel of the left foot touches the ground (opposite heel strike) (OHS: Opposite Heel Strike). Figure 4(e) shows the event where the toe of the right foot leaves the ground (toe off) while the sole of the left foot is in contact with the ground (TO: Toe Off). Figure 4(f) shows the event where the left and right feet cross (foot crossing) with the sole of the left foot touching the ground (FA: Foot Adjacent). Figure 4(g) shows the event where the tibia of the right foot is almost perpendicular to the ground (tibia vertical) with the sole of the left foot touching the ground (TV: Tibia Vertical). Figure 4(h) shows the event where the heel of the right foot touches the ground (heel strike) (HS: Heel Strike). Figure 4(h) corresponds to the end point of the walking cycle that begins with Figure 4(a) and also corresponds to the starting point of the next walking cycle.

[0028] The initial stance phase T1 is the period from heel-on-ground HS to toe-off OTO. The mid-stance phase T2 is the period from toe-off OTO to heel-lift HR. The end-stance phase T3 is the period from heel-lift HR to heel-on-ground OHS. The early swing phase T4 is the period from heel-on-ground OHS to toe-off TO. The initial swing phase T5 is the period from toe-off TO to foot crossing FA. The mid-swing phase T6 is the period from foot crossing FA to tibia vertical TV. The end-swing phase T7 is the period from tibia vertical TV to heel-on-ground HS. The timing at which walking events occur varies depending on the person, physical condition, and walking state, and does not necessarily match the expected walking cycle.

[0029] In walking, periodic movements are repeated as shown in FIG. 4. Therefore, the skeleton and muscles used at the same timing (also called walking phase) in different walking cycles are almost the same. Therefore, the waveforms of the time-series data of the sensor data measured during walking are similar in each walking phase. In this embodiment, missing sections of the time-series data of the walking cycle to be interpolated are interpolated using data without missing parts included in the time-series data of a walking cycle different from the walking cycle to be interpolated.

[0030] The interpolation device 12 receives sensor data from the measurement device 11. The interpolation device 12 generates time series data of the received sensor data. The interpolation device 12 generates the time series data based on the timestamp included in the sensor data. The interpolation device 12 extracts a waveform for one walking cycle from the generated time series data. For example, the interpolation device 12 extracts a waveform for one walking cycle starting from the timing of heel strike or heel lift. The interpolation device 12 normalizes the extracted waveform for one walking cycle to generate a waveform for one walking cycle (also called a walking waveform).

[0031] The interpolation device 12 identifies a portion where a time stamp number is missing in the time series data of the sensor data as a missing section. The interpolation device 12 generates interpolation data for interpolating data of the missing section using a walking waveform in another walking cycle that is free of missing data in the same walking phase as the missing section. The interpolation device 12 uses the generated interpolation data to interpolate the missing section.

[0032] Figure 5 is a conceptual diagram for comparing a normal waveform with a waveform that includes missing parts (also called a missing waveform). Figure 5 shows a walking waveform of forward acceleration (Y-direction acceleration) in a step cycle starting from heel lift. The missing waveform in Figure 5 includes two missing sections. As shown in Figure 5, the data in the missing sections may include gait characteristics.

[0033] FIG. 6 is a conceptual diagram for explaining an example of interpolation of missing intervals of time series data in the related art. FIG. 6 shows an example of interpolating data of missing intervals by an analytical method with reference to time stamps of sensor data included in the time series data. FIG. 6 shows linear interpolation (dashed line) that connects the start point and end point of the missing interval with a line segment (linear function) and polynomial interpolation (dash-dotted line) that connects the start point and end point of the missing interval with a curve. In linear interpolation, the start point and end point of the missing interval are connected with a line segment. In linear interpolation, the unevenness of the amplitude appearing in the time series data cannot be restored, and therefore the characteristics of the interval including the unevenness cannot be restored. In polynomial interpolation, the start point and end point of the missing interval are connected with a curve. In polynomial interpolation, the unevenness of the time series data can be roughly restored, but the fine features included in the unevenness cannot be restored.

[0034] Fig. 7 is a conceptual diagram for explaining an example of restoration of missing sections of time-series data in this embodiment. In the example of Fig. 7, data of missing sections S1 to S5 included in each of the walking waveforms of walking cycles i to i+3 are mutually interpolated (i is a natural number). The walking waveform of walking cycle i includes missing sections S1 and S5. The walking waveform of walking cycle i+1 includes missing sections S2 and S3. The walking waveform of walking cycle i+2 includes missing section S4. The walking waveform of walking cycle i+3 does not include a missing section.

[0035] The interpolation device 12 interpolates the missing section S1 included in the walking waveform of the walking cycle i using data of the walking phase corresponding to the missing section S1 included in the walking waveform of the walking cycle i+1 to 3. The interpolation device 12 interpolates the missing section S5 included in the walking waveform of the walking cycle i using data of the walking phase corresponding to the missing section S5 included in the walking waveform of the walking cycle i+1 to 3. The interpolation device 12 interpolates the missing section S2 included in the walking waveform of the walking cycle i+1 with data of the walking phase corresponding to the missing section S2 included in the walking waveform of the walking cycle i, i+2 to 3. The interpolation device 12 interpolates the missing section S3 included in the walking waveform of the walking cycle i+1 with data of the walking phase corresponding to the missing section S3 included in the walking waveform of the walking cycle i, i+2 to 3. The interpolation device 12 interpolates the missing section S4 included in the walking waveform of the walking cycle i+2 with data of the walking phase corresponding to the missing section S4 included in the walking waveform of the walking cycle i to i+1 and i+3.

[0036] For example, for a missing section of time-series data that occurs in a certain piece of time-series data, the interpolation device 12 selects at least one piece of data of a walking phase corresponding to the missing section from at least one walking waveform that does not have missing data for the missing section. For example, the interpolation device 12 uses the selected data to generate interpolation data for interpolating the missing section.

[0037] For example, the interpolation device 12 selects a plurality of data of a walking phase corresponding to a missing section of time-series data from a plurality of walking waveforms in which data of the missing section is not missing. The interpolation device 12 calculates an average value such as an arithmetic average or a weighted average for the plurality of selected data, and generates interpolation data for interpolating the missing section using the calculated average value. For example, the interpolation device 12 generates the interpolation data using the data of the plurality of selected data that has the closest values ​​of the start point and the end point of the missing section.

[0038] For example, the interpolation device 12 may select a method for interpolating data of a missing section depending on the walking period of the missing section. directionThe waveform of the acceleration (Y-direction acceleration) fluctuates little during the stance phase and fluctuates greatly during the swing phase. Therefore, the interpolation device 12 may be configured to interpolate data of a missing section occurring during the stance phase using an analytical method, and to interpolate data of a missing section occurring during the swing phase based on gait waveforms of other gait cycles.

[0039] For example, the interpolation device 12 is implemented in a server (not shown), etc. For example, the interpolation device 12 may be realized by an application server, etc. For example, the interpolation device 12 may be realized by application software installed in a mobile terminal (not shown), etc.

[0040] The gait measurement device 13 acquires a gait waveform that does not include a missing section from the interpolation device 12. The gait measurement device 13 performs measurement related to gait using the acquired gait waveform. For example, the gait measurement device 13 detects a gait event from the gait waveform, and measures the user's gait based on the detected gait event. For example, the gait measurement device 13 measures gait such as stride length, step length, step width, foot angle, and walking speed based on the detected gait event. For example, the gait measurement device 13 may estimate physical conditions such as the degree of pronation / supination, the progress of hallux valgus, body symmetry, and body flexibility based on the measured gait. For example, the gait measurement device 13 may measure physical parameters such as the length of the upper limbs, lower limbs, upper arms, forearms, upper legs, and lower legs based on the measured gait. There is no particular limitation on the items to be measured by the gait measurement device 13.

[0041] Gait measurement device 13 outputs measurement results related to gait (also referred to as gait information). For example, gait measurement device 13 outputs gait information to a display device (not shown) or a mobile terminal (not shown). The gait information output to the display device is displayed on the screen of the display device or the mobile terminal. For example, gait measurement device 13 outputs gait information to an external system (not shown). The gait information output from gait measurement device 13 can be used for any purpose. There are no particular limitations on the communication function through which gait measurement device 13 outputs gait information.

[0042] For example, gait measurement device 13 is implemented in a server or the like (not shown). For example, gait measurement device 13 may be realized by an application server. For example, gait measurement device 13 may be realized by application software or the like installed on a mobile terminal (not shown).

[0043] [Measuring equipment] Next, a detailed configuration of the measuring device 11 will be described with reference to the drawings. Fig. 8 is a block diagram showing an example of a detailed configuration of the measuring device 11. The measuring device 11 has an acceleration sensor 111, an angular velocity sensor 112, a control unit 113, and a transmission unit 115. The measuring device 11 includes a power supply (not shown).

[0044] Acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). Acceleration sensor 111 outputs the measured acceleration to control unit 113. For example, a piezoelectric type, a piezoresistance type, a capacitance type, or other type of sensor can be used for acceleration sensor 111. Note that the measurement type of the sensor used for acceleration sensor 111 is not limited as long as it can measure acceleration.

[0045] Angular velocity sensor 112 is a sensor that measures angular velocity (also called spatial angular velocity) in three axial directions. Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a vibration type, a capacitance type, or other type of sensor can be used for angular velocity sensor 112. Note that the measurement method of the sensor used for angular velocity sensor 112 is not limited as long as it can measure angular velocity.

[0046] The control unit 113 acquires the acceleration in three axial directions and the angular velocity around the three axes from each of the acceleration sensor 111 and the angular velocity sensor 112. The control unit 113 converts the acquired acceleration and angular velocity into digital data, and outputs the converted digital data (also called sensor data) to the transmission unit 115. The sensor data includes at least the acceleration data converted into digital data and the angular velocity data converted into digital data. The acceleration data includes an acceleration vector in the three axial directions. The angular velocity data includes an angular velocity vector around the three axes. The acceleration data and the angular velocity data are associated with the acquisition time of the data. The control unit 113 may be configured to output sensor data obtained by applying corrections such as mounting error, temperature correction, and linearity correction to the acquired acceleration data and angular velocity data. The control unit 113 may also generate angle data around the three axes using the acquired acceleration data and angular velocity data.

[0047] For example, the control unit 113 is a microcomputer or a microcontroller that performs overall control of the measuring device 11 and data processing. For example, the control unit 113 has a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, and the like. The control unit 113 controls the acceleration sensor 111 and the angular velocity sensor 112 to measure angular velocity and acceleration. For example, the control unit 113 performs AD conversion (Analog-to-Digital Conversion) of physical quantities (analog data) such as the measured angular velocity and acceleration. The control unit 113 stores the converted digital data in a storage unit (not shown) such as a flash memory. Note that the physical quantities (analog data) measured by the acceleration sensor 111 and the angular velocity sensor 112 may be converted into digital data in each of the acceleration sensor 111 and the angular velocity sensor 112. The digital data stored in the storage unit is output to the transmission unit 115 at a predetermined timing.

[0048] The transmitting unit 115 acquires sensor data from the control unit 113. The transmitting unit 115 transmits the acquired sensor data to the interpolation device 12. For example, the transmitting unit 115 transmits the sensor data to the interpolation device 12 via a wired connection such as a cable. For example, the transmitting unit 115 transmits the sensor data to the interpolation device 12 via wireless communication. For example, the transmitting unit 115 is configured to transmit the sensor data to the interpolation device 12 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the transmitting unit 115 may be conforming to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0049] [Interpolation device] Next, a detailed configuration of the interpolation device 12 will be described with reference to the drawings. Fig. 9 is a block diagram for explaining the detailed configuration of the interpolation device 12. The interpolation device 12 has a receiving unit 121, a gait information processing unit 122, a missing information processing unit 123, a storage unit 125, an interpolation unit 126, and a transmitting unit 128.

[0050] The receiving unit 121 receives sensor data from the measurement device 11. The receiving unit 121 outputs the received sensor data to the gait information processing unit 122. For example, the receiving unit 121 receives the sensor data from the measurement device 11 via a wired connection such as a cable. For example, the receiving unit 121 receives the sensor data from the measurement device 11 via wireless communication. For example, the receiving unit 121 is configured to receive the sensor data from the measurement device 11 via a wireless communication function (not shown) conforming to a standard such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the receiving unit 121 may conform to a standard other than Bluetooth (registered trademark) or WiFi (registered trademark).

[0051] The gait information processing unit 122 acquires sensor data measured in association with the walking of a user wearing the shoe 100 on which the measuring device 11 is installed from the receiving unit 121. The gait information processing unit 122 generates time-series data of the acquired sensor data based on a timestamp included in the sensor data. For example, the gait information processing unit 122 converts the coordinate system of the acquired sensor data from the local coordinate system to the world coordinate system.

[0052] The gait information processing unit 122 generates time series data of physical quantities related to foot movement using the sensor data. For example, the gait information processing unit 122 generates time series data such as spatial acceleration and spatial angular velocity. Furthermore, the gait information processing unit 122 integrates the spatial acceleration and spatial angular velocity to generate time series data such as spatial velocity, spatial angle (sole angle), and spatial trajectory. These time series data correspond to a gait waveform. The gait information processing unit 122 generates time series data at a predetermined timing or time interval set in accordance with a general gait cycle or a gait cycle specific to a user. The timing at which the gait information processing unit 122 generates the time series data can be set arbitrarily. For example, the gait information processing unit 122 is configured to continue generating time series data during a period during which the user continues walking. Furthermore, the gait information processing unit 122 may be configured to generate time series data at a specific timing.

[0053] The gait information processing unit 122 extracts a waveform for one gait cycle from the generated time-series data. For example, the gait information processing unit 122 extracts a waveform for one gait cycle starting from heel strike. For example, the gait information processing unit 122 detects the timing of heel strike in a walking waveform of the travel direction acceleration (Y-direction acceleration) for one gait cycle. In a walking waveform of the travel direction acceleration for one gait cycle, the timing of heel strike is the midpoint between the timing at which a minimum peak is detected and the timing at which a maximum peak that appears after the minimum peak is detected. For example, the gait information processing unit 122 extracts time-series data starting from the timing of the preceding heel strike and ending from the timing of the succeeding heel strike as a waveform for one gait cycle starting from heel strike.

[0054] For example, the gait information processor 122 detects the timing of toe-off in the walking waveform of the travel direction acceleration for one walking cycle. In the walking waveform of the travel direction acceleration for one walking cycle, the timing of toe-off is the timing of a valley that appears between two peaks included in the maximum peak. For example, the gait information processor 122 detects the timing of toe-off as the timing of a valley that appears between two peaks included in the maximum peak in the walking waveform of the travel direction acceleration for one walking cycle.

[0055] For example, the gait information processor 122 may extract a waveform for one step cycle starting from a heel lift. In the gait waveform of the plantar angle, a state in which the toe is located above the heel (dorsiflexion) is defined as negative, and a state in which the toe is located below the heel (plantar flexion) is defined as positive. The time at which the gait waveform of the plantar angle becomes minimum corresponds to the timing of the start of the stance phase. The time at which the gait waveform of the plantar angle becomes maximum corresponds to the timing of the start of the swing phase. The time at the midpoint between the start of the stance phase and the start of the swing phase corresponds to the timing of the center of the stance phase. The timing of the center of the stance phase corresponds to the timing of the heel lift. For example, the gait information processor 122 extracts time series data having the earlier timing as the start point and the later timing as the end point of two heel lift timings included in the time series data for two steps. The extracted time series data is a gait waveform for one stride cycle starting from heel lift.

[0056] The gait information processing unit 122 normalizes the waveform of the extracted step cycle to generate a waveform of the step cycle (also called a walking waveform). The gait information processing unit 122 normalizes the time axis of the waveform of the step cycle to a walking cycle whose starting point timing is 0 and whose ending point timing is 100. The walking cycle of the normalized waveform is divided into 100 and expressed as a percentage. Each percentage of the walking cycle is called a walking phase. For example, the gait information processing unit 122 normalizes the walking cycle of the walking waveform so that the ratio of the stance phase to the swing phase is 60:40. For example, the gait information processing unit 122 may normalize the walking waveform based on a walking event that appears in the walking waveform. For example, the gait information processing unit 122 normalizes a walking waveform whose starting point is the timing of heel lift so that the timing of toe off is 30% and the timing of heel contact is 70%. Note that the timing at which a walking event occurs varies depending on the person, their physical condition, and their walking state, and therefore does not necessarily match perfectly with the expected walking phase.

[0057] Furthermore, the gait information processing unit 122 may normalize the amplitude (values ​​of acceleration and angular velocity) of the walking waveform for one step gait cycle. For example, the gait information processing unit 122 normalizes the amplitude of the walking waveform for one step gait cycle so that the amplitude fluctuation width is within a certain range. For example, the gait information processing unit 122 normalizes the amplitude of the walking waveform for one step gait cycle so that the maximum value of the amplitude is 1 and the maximum value of the amplitude is -1. If the amplitude of the walking waveform is normalized, the amplitudes of the walking waveform for a plurality of gait cycles become uniform, making it easier to interpolate data in a missing section. Furthermore, the gait information processing unit 122 may normalize the baseline of the walking waveform for one step gait cycle. For example, the gait information processing unit 122 normalizes the walking waveform for one step gait cycle so that the slope of the baseline becomes 0. If the baseline of the walking waveform is normalized, the slope of the baseline of the walking waveform for a plurality of gait cycles becomes uniform. Therefore, if the baseline of the walking waveform is normalized, it becomes easier to interpolate data in a missing section.

[0058] The gait information processing unit 122 stores the normalized walking waveform in the storage unit 125. For example, the gait information processing unit 122 stores only the walking waveform without a missing section in the storage unit 125. In this case, the missing information processing unit 123 may identify the missing section and then store the walking waveform with a missing section (also called a missing waveform) in the storage unit 125. For example, all walking waveforms including the missing waveform may be stored in the storage unit 125. In this case, the missing information processing unit 123 may acquire the missing waveform stored in the storage unit 125 and identify the missing section included in the missing waveform. Data of the missing section of the walking waveform stored in the storage unit 125 (also called normal data) is used to interpolate the missing section included in the missing waveform.

[0059] The missing information processing unit 123 acquires a missing waveform. The missing information processing unit 123 may acquire the missing waveform from the gait information processing unit 122, or may acquire the missing waveform from the storage unit 125. The missing information processing unit 123 identifies a portion in the acquired missing waveform where a timestamp number is missing as a missing section. For example, the missing information processing unit 123 determines which data is missing from the starting point of the missing section in the walking waveform of a step gait cycle, and calculates the walking phase that is missing. For example, the missing information processing unit 123 may identify the walking period of the missing section based on the timestamp number.

[0060] The missing information processing unit 123 stores information (also called missing information) about the missing section included in the missing waveform in the storage unit 125 in association with the missing waveform. For example, the missing information processing unit 123 stores, as missing information, the walking phases at the start and end of the missing section in the storage unit 125 in association with the missing waveform. For example, the missing information processing unit 123 may store, as missing information, the walking phases at the start and end of the missing section and the amplitude value in the walking phase in association with the missing waveform in the storage unit 125. The missing information processing unit 123 may also be configured to output the missing information to the interpolation unit 126.

[0061] The memory unit 125 stores a walking waveform. The memory unit 125 stores a walking waveform that does not include a missing section (also called a normal waveform). The memory unit 125 also stores a walking waveform that includes a missing section (also called a missing waveform) and a walking waveform in which the missing section has been restored (also called a restored waveform). The missing waveform is a target for interpolation of the missing section. The missing waveform is associated with loss information of the missing waveform. The normal waveform and the restored waveform are used for gait measurement by the gait measurement device 13. The walking waveform stored in the memory unit 125 is acquired by the transmission unit 128 at a predetermined timing and transmitted from the transmission unit 128 to the gait measurement device 13.

[0062] The interpolation unit 126 acquires a missing waveform. The interpolation unit 126 acquires a missing waveform from the storage unit 125 or the missing information processing unit 123. The missing waveform acquired by the interpolation unit 126 includes information (missing information) on the missing section specified by the missing information processing unit 123. The interpolation unit 126 uses data of a walking waveform with a walking cycle different from the acquired missing waveform to interpolate the missing section included in the missing waveform. The interpolation unit 126 extracts normal data in a walking phase corresponding to the missing section from the walking waveform with a walking cycle different from the missing waveform. The interpolation unit 126 generates interpolation data for interpolating the missing section using the extracted interpolation data.

[0063] For example, the missing waveform includes a missing section in a section where the walking phase is 10 to 20%. In this case, the interpolation unit 126 selects at least one walking waveform that does not include a missing section in a section where the walking phase is 10 to 20% from among the walking waveforms stored in the storage unit 125. The interpolation unit 126 extracts normal data for a section where the walking phase is 10 to 20% from the at least one selected walking waveform. For example, with respect to a missing section included in a certain missing waveform, the interpolation unit 126 selects at least one normal data for the walking phase corresponding to the missing section from at least one walking waveform in which data for the missing section is not missing. The interpolation unit 126 generates interpolation data for interpolating the missing section using the selected normal data.

[0064] For example, the interpolation unit 126 selects a plurality of data of a walking phase corresponding to a missing section included in a certain missing waveform from a plurality of walking waveforms in which data of the missing section is not missing. For example, the interpolation unit 126 calculates interpolation data for each individual walking phase included in the missing section. The interpolation unit 126 calculates an average value such as an arithmetic average or a weighted average for the plurality of selected data, and generates interpolation data for interpolating the missing section using the calculated average value. For example, the interpolation unit 126 generates interpolation data using data of which the values ​​of the start point and the end point of the missing section are closest among the plurality of selected data.

[0065] For example, the interpolation unit 126 may select a method for interpolating data of a missing section depending on the walking period of the missing section. direction The waveform of the acceleration (Y-direction acceleration) of the leg has small fluctuations during the stance phase and large fluctuations during the swing phase. That is, the swing phase (also called the first walking period) has more drastic data fluctuations than the stance phase (also called the second walking period), making it difficult to analytically interpolate data. In other words, the stance phase (second walking period) has less data fluctuations than the swing phase (first walking period), making it possible to analytically interpolate data. Therefore, the interpolation unit 126 may use an analytical method to interpolate missing sections included in the stance phase (second walking period). Then, the swing phase (first walking period) may be interpolated based on the walking waveforms of other walking cycles. Note that the interpolation unit 126 may also use an analytical method to interpolate missing sections included in the swing phase (first walking period) if the section has small fluctuations.

[0066] FIG. 10 is a conceptual diagram for explaining the interpolation of missing sections in analytically interpolated sections. In the example of FIG. 10, the amplitude fluctuation is small in the section where the walking phase is 0 to 20% and the section where the walking phase is 80% to 100%. In contrast, the amplitude fluctuation is large in the section where the walking phase is 20 to 80%. In such a case, the interpolation unit 126 may perform analytical interpolation for the section where the walking phase is 0 to 20% and the section where the walking phase is 80% to 100%. Since the amplitude fluctuation is large in the section where the walking phase is 20 to 80%, it is better for the interpolation unit 126 to generate interpolated data using data of another walking waveform.

[0067] The interpolation unit 126 uses the generated interpolation data to interpolate the missing interval included in the missing waveform. For example, the interpolation unit 126 interpolates the missing waveform by inserting the interpolation data into the missing interval. For example, the interpolation unit 126 sequentially interpolates the missing interval with normal data included in the interpolation data from the start point to the end point of the missing interval. For example, the interpolation unit 126 may sequentially interpolate the missing interval with normal data included in the interpolation data from the end point to the start point of the missing interval. For example, the interpolation unit 126 may sequentially interpolate the missing interval with normal data included in the interpolation data from the end point and the start point of the missing interval.

[0068] For example, the interpolation unit 126 inserts a waveform formed by the interpolation data into the missing section. For example, the interpolation unit 126 inserts a waveform formed by the interpolation data so that the start point and the end point of the interpolation data match the start point and the end point of the missing section. For example, it is assumed that the start point and the end point of the interpolation data do not match the start point and the end point of the missing section. In such a case, the interpolation unit 126 rotates the baseline of the interpolation data so that the start point and the end point of the interpolation data match the start point and the end point of the missing section, and inserts the interpolation data into the missing section. For example, the interpolation unit 126 calculates the amplitude of the start point and the end point of the missing section based on the normal waveform of another walking cycle. For example, the interpolation unit 126 adjusts the baseline according to the difference between the calculated amplitude of the start point and the end point of the missing section and the actual amplitude of the start point and the end point of the missing section.

[0069] 11 is a conceptual diagram showing an example of inserting a waveform formed by interpolation data into a missing section. (1) The interpolation unit 126 generates interpolation data for the missing section included in the missing waveform. (2) The interpolation unit 126 inserts the generated interpolation data into the missing section of the missing waveform. (3) The interpolation unit 126 sets a baseline for the interpolation data inserted into the missing section of the missing waveform. (4) The interpolation unit 126 rotates the baseline of the interpolation data inserted into the missing section of the missing waveform, and performs start-end point correction so that the start point and end point of the missing section match the start point and end point of the interpolated data, respectively.

[0070] The interpolation unit 126 stores a walking waveform (restored waveform) in which the missing section has been interpolated in the storage unit 125. For example, the interpolation unit 126 may label a walking phase included in a missing section in the restored waveform so that the restored missing section can be identified. For example, the interpolation unit 126 may store a restored waveform (also called a provisional restored waveform) in which provisional interpolation data has been inserted in the missing section in the storage unit 125. For example, the gait measurement device 13 may correct the interpolation data inserted in the missing section included in the provisional restored waveform. The restored waveform stored in the storage unit 125 is used for gait measurement by the gait measurement device 13 in the same manner as other walking waveforms that do not include a loss.

[0071] The transmitting unit 128 acquires a gait waveform from the storage unit 125. The transmitting unit 128 acquires a gait waveform including a normal waveform and a restored waveform from the storage unit 125. The transmitting unit 128 transmits the acquired gait waveform to the gait measurement device 13. For example, the transmitting unit 128 transmits the gait waveform to the gait measurement device 13 via a wire such as a cable. For example, the transmitting unit 128 transmits the gait waveform to the gait measurement device 13 via wireless communication. The communication method between the transmitting unit 128 and the gait measurement device 13 is not particularly limited.

[0072] [Gait measurement device] Next, a detailed configuration of gait measurement device 13 provided in gait measurement system 1 will be described with reference to the drawings. Fig. 12 is a block diagram showing an example of a detailed configuration of gait measurement device 13. Gait measurement device 13 has an acquisition unit 131, a detection unit 132, and a gait measurement unit 133. In reality, an output unit (communication interface) that outputs the estimation result by gait measurement unit 133 is provided, but in Fig. 12, the function of the output unit is included in gait measurement unit 133.

[0073] The acquisition unit 131 acquires a walking waveform from the interpolation device 12. The acquisition unit 131 acquires a walking waveform including a normal waveform and a restored waveform from the interpolation device 12. The acquisition unit 131 outputs the acquired walking waveform to the detection unit 132. For example, the acquisition unit 131 receives the walking waveform from the interpolation device 12 via a wire such as a cable. For example, the acquisition unit 131 receives the walking waveform from the interpolation device 12 via wireless communication. There is no particular limitation regarding the communication method between the interpolation device 12 and the acquisition unit 131.

[0074] The detection unit 132 acquires a walking waveform from the acquisition unit 131. The detection unit 132 detects walking periods and walking events from the acquired walking waveform. For example, the detection unit 132 detects walking periods such as a stance phase and a swing phase from the walking waveform. For example, the detection unit 132 detects walking events such as a heel strike, opposite foot toe off, heel lift, opposite foot heel strike, toe off, foot crossing, and tibia vertical from the walking waveform. For example, the detection unit 132 detects walking periods such as an early stance phase, a mid stance phase, a late stance phase, an early swing phase, an early swing phase, a mid swing phase, and a late swing phase from the walking waveform based on the detected walking events.

[0075] For example, the detection unit 132 detects walking periods and walking events from the walking waveform based on the percentage of the walking phase. If the same person walks under the same conditions, the timing at which the walking periods and walking events are detected is almost constant. Even if the person is different, the timing at which the walking periods and walking events are detected shows the same tendency. Therefore, the walking periods and walking events can be identified based on the percentage of the walking phase.

[0076] For example, the detection unit 132 may detect a walking period or a walking event based on features extracted from the walking waveform. For example, the detection unit 132 detects the timing at which the walking waveform of the plantar angle becomes minimum as the timing of the start of the stance phase. For example, the detection unit 132 detects the timing at which the walking waveform of the plantar angle becomes maximum as the timing of the start of the swing phase. For example, the detection unit 132 detects the timing of the midpoint between the start of the stance phase and the start of the swing phase as the timing of heel lift.

[0077] For example, the detection unit 132 detects a walking event based on a walking waveform starting from the timing of heel lift. The detection unit 132 detects the timing at which a valley appears between two peaks included in the maximum peak in the walking waveform of the forward acceleration (Y-direction acceleration) as the timing of toe-off. For example, the detection unit 132 detects the minimum peak at about 60% of the walking cycle in the walking waveform of the forward acceleration (Y-direction acceleration) as the timing of sudden deceleration of the foot at the end of the swing phase. For example, the detection unit 132 detects the maximum peak at about 70% of the walking cycle in the walking waveform of the forward acceleration (Y-direction acceleration) as the timing of heel rocker. For example, the detection unit 132 detects the midpoint between the minimum peak and the maximum peak in the walking waveform of the forward acceleration (Y-direction acceleration) as the timing of heel contact. For example, the detection unit 132 detects the timing of the maximum peak between the toe-off and the heel-landing in the walking waveform of the vertical acceleration (Z-direction acceleration) as the timing of the tibia vertical. For example, the detection unit 132 detects the timing of a gentle maximum peak between the toe-off and the tibia vertical in the walking waveform of the forward acceleration (Y-direction acceleration) as the timing of the foot crossing. For example, the detection unit 132 detects the timing of the opposite foot heel-landing and the opposite foot toe-off from the walking waveform of the roll angular velocity. For example, the detection unit 132 detects the timing of the acceleration inflection point in the curve from the starting point to the toe-off timing in the walking waveform of the roll angular velocity as the timing of the opposite foot heel-landing. For example, the detection unit 132 detects the timing of the deceleration inflection point in the curve from the heel-landing timing to the end point in the walking waveform of the roll angular velocity as the timing of the opposite foot toe-off.

[0078] The gait measurement unit 133 acquires information on the walking period and walking events detected by the detection unit 132. The gait measurement unit 133 performs measurements related to the gait using the information acquired from the detection unit 132. For example, the gait measurement unit 133 measures the user's gait based on the walking events detected from the walking waveform. For example, the gait measurement unit 133 measures gait such as stride length, step length, stride width, foot angle, and walking speed based on the detected walking events. For example, the gait measurement unit 133 may estimate physical conditions such as the degree of pronation / supination, the progress of hallux valgus, body symmetry, and body flexibility based on the measured gait. For example, the gait measurement unit 133 may estimate physical conditions such as muscle weakness, bone density, and basal metabolism based on the measured gait. For example, the gait measurement unit 133 may measure body parameters such as the lengths of the upper limbs, lower limbs, upper arms, forearms, upper legs, and lower legs based on the measured gait. There is no particular limitation on the items to be measured by the gait measurement unit 133.

[0079] Gait measurement device 13 outputs the results (also referred to as gait information) measured by gait measurement unit 133. For example, gait measurement device 13 outputs the gait information to a display device (not shown) or a mobile terminal (not shown). The gait information output to the display device is displayed on the screen of the display device or the mobile terminal. For example, gait measurement device 13 outputs the gait information to an external system (not shown). The gait information output from gait measurement device 13 can be used for any purpose. There are no particular limitations on the communication function through which gait measurement device 13 outputs the gait information.

[0080] (operation) Next, the operation of the interpolation device 12 of the gait measurement system 1 will be described with reference to the drawings. Descriptions of the operations of the measurement device 11 and the gait measurement device 13 will be omitted. Below, the processes performed by the gait information processing unit 122, the missing information processing unit 123, and the interpolation unit 126 included in the interpolation device 12 will be described individually. The following operation of the interpolation device 12 may include processes and sequences different from those of the interpolation device 12 described above.

[0081] [Gait Information Processing] First, gait information processing by the gait information processing unit 122 will be described with reference to a flowchart. Fig. 13 is a flowchart for explaining gait information processing by the gait information processing unit 122. In explaining the processing following the flowchart of Fig. 13, the gait information processing unit 122 will be described as the subject of action.

[0082] In FIG. 13, first, the gait information processing unit 122 acquires time-series data of the sensor data (step S111).

[0083] If there is a missing part in the time-series data (Yes in step S112), the gait information processing unit 122 outputs the time-series data including the missing part to the missing part information processing unit 123 (step S113). The gait information processing unit 122 may store the time-series data including the missing part in the storage unit 125. After step S113, the process proceeds to step S118.

[0084] On the other hand, if there is no loss in the time-series data (No in step S112), the gait information processing unit 122 detects walking events from the acquired time-series data (step S114). For example, the gait information processing unit 122 detects walking events such as heel contact, toe lift, and heel lift from the walking waveform.

[0085] After step S114, the gait information processing unit 122 extracts a gait waveform for one step cycle based on the detected walking event (step S115). For example, the gait information processing unit 122 extracts a gait waveform for one step cycle starting from heel contact. For example, the gait information processing unit 122 extracts a gait waveform for one step cycle starting from toe lift.

[0086] Next, the gait information processing unit 122 normalizes the extracted walking waveform for one cycle (step S116). For example, the gait information processing unit 122 normalizes the walking phase and amplitude of the walking waveform.

[0087] Next, the gait information processing unit 122 stores the normalized walking waveform in the storage unit 125 (step S117). The walking waveform stored in the storage unit 125 is used for interpolation of missing sections included in the missing waveform and for gait measurement by the gait measurement device 13.

[0088] If the process is to be continued (Yes in step S118), the process returns to step S111. If the process is not to be continued (No in step S118), the process according to the flowchart in Fig. 13 ends. The condition for determining whether to continue the process in step S118 may be set in advance.

[0089] [Missing information processing] Next, the missing information processing by missing information processing unit 123 will be described with reference to a flowchart. Fig. 14 is a flowchart for explaining the missing information processing by missing information processing unit 123. In explaining the processing according to the flowchart of Fig. 14, the missing information processing unit 123 will be described as the subject of operation.

[0090] 14, first, the missing information processing unit 123 acquires time-series data including missing parts (missing waveform) from the gait information processing unit 122 (step S121). The missing information processing unit 123 may acquire the missing waveform from the storage unit 125.

[0091] Next, the loss information processing unit 123 identifies a loss section included in the acquired loss waveform based on the time stamp (step S122).

[0092] Next, the missing information processing unit 123 calculates the walking phase of the identified missing section (step S123).

[0093] Next, the missing information processing unit 123 stores information (missing information) about the walking phase of the missing section included in the missing waveform in the storage unit 125 (step S124). The missing information stored in the storage unit 125 is used by the interpolation unit 126 to interpolate the missing section.

[0094] [Interpolation Processing] Next, the interpolation process by the interpolation unit 126 will be described with reference to a flowchart. Here, an example in which interpolation data is generated in all sections (FIG. 15) and an example in which interpolation is performed in some sections using an analytical method (FIG. 16) will be described.

[0095] Fig. 15 is a flowchart for explaining the interpolation process of the interpolation unit 126 when generating interpolated data for all sections. In the explanation following the flowchart of Fig. 15, the interpolation unit 126 will be described as the subject of operation.

[0096] 15, first, the interpolation unit 126 acquires time-series data including a missing section (missing waveform) from the storage unit 125 (step S131). The interpolation unit 126 may acquire the missing waveform from the gait information processing unit 122. The missing waveform is associated with missing information indicating the walking phase of the missing section.

[0097] Next, the interpolation unit 126 acquires from the storage unit 125 a walking waveform of another walking cycle in which the walking phase of the missing section is not missing (step S132).

[0098] Next, the interpolation unit 126 generates interpolation data for the missing section using the acquired walking waveform data (step S133).

[0099] Next, the interpolation unit 126 corrects the start point and the end point of the interpolated data, and inserts the interpolated data into the missing section (step S134).

[0100] Next, the interpolation unit 126 resamples the corrected waveform to restore it to time-series data (step S135). Step S135 is performed because the original time-series data is required when calculating the walking speed, stride length, etc. If the original time-series data is not required, step S135 may be omitted.

[0101] Fig. 16 is a flowchart for explaining the interpolation process of the interpolation unit 126 when an analytical method is used to perform interpolation in a part of a section. In the explanation based on the flowchart of Fig. 16, the interpolation unit 126 will be described as the subject of operation.

[0102] 16, first, the interpolation unit 126 acquires time-series data including a missing section (missing waveform) from the storage unit 125 (step S141). The interpolation unit 126 may acquire the missing waveform from the gait information processing unit 122. The missing waveform is associated with missing information indicating the walking phase of the missing section.

[0103] If the missing section is one for which the analytical method can be used (Yes in step S142), the interpolation unit 126 interpolates data of the missing section using the analytical method (step S143). After step S143, the process proceeds to step S147.

[0104] On the other hand, if the missing section is not one for which an analytical method can be used (No in step S142), the interpolation unit 126 acquires a walking waveform of another walking cycle in which the walking phase of the missing section is not missing from the storage unit 125 (step S144).

[0105] Next, the interpolation unit 126 generates interpolation data for the missing section using the acquired walking waveform data (step S145).

[0106] Next, the interpolation unit 126 corrects the start point and the end point of the interpolated data, and inserts the interpolated data into the missing section (step S146).

[0107] Next, the interpolation unit 126 resamples the corrected waveform to restore it to time-series data (step S146). Step S146 is performed because the original time-series data is required when calculating the walking speed, stride length, etc. If the original time-series data is not required, step S146 may be omitted.

[0108] As described above, the gait measurement system of this embodiment includes a measurement device, an interpolation device, and a gait measurement device. The measurement device is placed on a user's footwear. The measurement device measures spatial acceleration and spatial angular velocity according to the user's gait. The measurement device generates sensor data based on the measured spatial acceleration and spatial angular velocity. The measurement device outputs the generated sensor data to the interpolation device. The interpolation device has a receiving unit, a gait information processing unit, a missing information processing unit, a storage unit, an interpolation unit, and a transmission unit. The receiving unit receives the sensor data transmitted from the measurement device. The gait information processing unit generates time-series data of the sensor data. The gait information processing unit generates a gait waveform for each gait cycle using the time-series data of the sensor data related to the movement of the feet, and identifies a missing section of data in the time-series data. The gait information processing unit stores the generated gait waveform and information on the identified missing section in the storage unit. The storage unit stores the gait waveform, information on the missing section, and a repair waveform in which the missing section is interpolated. The missing information processing unit calculates the gait phase of the identified missing section. The interpolation unit generates interpolation data for interpolating the missing section by using data of a walking phase of the missing section in a walking waveform having a walking cycle different from the walking cycle including the missing section. The interpolation unit interpolates the missing section with the generated interpolation data. The gait measurement device acquires from the interpolation device a gait waveform that does not include a missing section that is generated by the interpolation device using time-series data of the sensor data, and a gait waveform in which the interpolation device interpolates data of the missing section. The gait measurement device measures the user's gait based on walking events detected from the acquired gait waveform. The gait measurement device outputs information related to the measured gait of the user.

[0109] In the method of the present embodiment, by focusing on the periodicity of walking, interpolation data is generated to fill in missing sections of data that occur in the time series data of sensor data measured according to the user's walking. In the method of the present embodiment, the interpolation data is generated using data of the same walking phase of walking waveforms with different walking cycles. Therefore, according to the method of the present embodiment, since the interpolation data generated by focusing on the periodicity of walking is used, data of the missing sections can be interpolated including the characteristics of the missing sections of data included in the time series data of the sensor data.

[0110] When communicating sensor data measured by a wearable sensor having a wireless communication function, data loss may occur due to communication failure. Data loss that may occur due to communication failure is often continuous rather than discrete. For example, important data used for feature extraction may be lost entirely from time-series data of sensor data. According to the method of this embodiment, the similarity and periodicity of walking motion are used to interpolate data loss sections, so that data loss sections of a long loss section spanning several percent to several tens of percent of the walking phase can be interpolated. In addition, the method of this embodiment can also be used to remove outliers measured due to circuitry-related factors and to level abnormal values ​​measured due to unknown factors.

[0111] In one aspect of this embodiment, the gait information processor extracts a waveform for one walking cycle from the time-series data. The gait information processor normalizes the walking phase included in the extracted waveform to generate a walking waveform. According to this aspect, the walking phases of multiple walking waveforms generated from the time-series data of the sensor data are unified, making it easier to mutually interpolate data between the multiple walking waveforms.

[0112] In one aspect of this embodiment, the interpolation unit selects at least one piece of data of a walking phase included in a missing section from at least one walking waveform in which data of the walking phase included in the missing section is not missing for the missing section. The interpolation unit generates interpolated data using the data of the selected walking phase. According to this aspect, the missing section can be interpolated using data of another walking cycle.

[0113] In one aspect of this embodiment, the interpolation unit calculates an average value of data of a walking phase selected from the walking waveforms of a plurality of walking cycles. The interpolation unit generates the interpolated data using the calculated average value of the data of the walking phase. According to this aspect, data of a missing section is interpolated based on the average value of data of a plurality of walking cycles, so that the interpolated data that better reflects the characteristics of walking can be generated.

[0114] In one aspect of this embodiment, the interpolation unit generates the interpolation data by an analytical method for a walking period in which the data fluctuation is small. The interpolation unit generates the interpolation data by using data of a walking cycle different from the walking cycle including the missing section for a walking period in which the data fluctuation is large. According to this aspect, the interpolation data is generated by an analytical method for a walking period in which the data fluctuation is small, so that the calculation time and amount can be reduced.

[0115] Second embodiment Next, a gait measurement system according to a second embodiment will be described with reference to the drawings. The gait measurement system of this embodiment differs from the first embodiment in that it generates interpolation data for missing sections based on the deviation value in the overall distribution of data values ​​(amplitude values) for each walking phase regarding the walking waveform of a plurality of walking cycles.

[0116] (composition) 17 is a block diagram showing the configuration of a gait measurement system 2 of this embodiment. The gait measurement system 2 includes a measuring device 21, an interpolation device 22, and a gait measurement device 23. The interpolation device 22 may be connected to the measuring device 21 and the gait measurement device 23 by wire or wirelessly. The measuring device 21, the interpolation device 22, and the gait measurement device 23 may be configured as a single device. The gait measurement system 2 may be configured from the interpolation device 22 and the gait measurement device 23, excluding the measuring device 21.

[0117] The measuring device 21 has the same configuration as the measuring device 11 of the first embodiment. The measuring device 21 is installed on the foot. The measuring device 21 measures acceleration (also called spatial acceleration) measured by an acceleration sensor and angular velocity (also called spatial angular velocity) measured by an angular velocity sensor as physical quantities related to the movement of the foot of a user wearing the footwear. The physical quantities related to the movement of the foot measured by the measuring device 21 include velocity, angle, and position (trajectory) calculated by integrating the acceleration and angular velocity. The measuring device 21 converts the measured physical quantities into digital data (also called sensor data). The measuring device 21 transmits the converted sensor data to the interpolation device 22.

[0118] The interpolation device 22 receives sensor data from the measurement device 21. The interpolation device 22 generates time series data of the received sensor data. The interpolation device 22 generates the time series data based on the timestamp included in the sensor data. The interpolation device 22 extracts a waveform for one step walking cycle from the generated time series data. For example, the interpolation device 22 extracts a waveform for one step walking cycle starting from the timing of heel strike or heel lift. The interpolation device 22 normalizes the waveform for the extracted one step walking cycle to generate a waveform for one step walking cycle (also called a walking waveform).

[0119] The interpolation device 22 specifies a portion where a time stamp number is missing as a missing section in the time series data of the sensor data. The interpolation device 22 generates interpolation data for interpolating data of the missing section based on the overall distribution of data values ​​of the walking phase included in the missing section. The overall distribution is a distribution of data values ​​for each walking phase included in the missing section, with the walking waveforms of multiple walking cycles used for interpolating the missing section as the whole. In other words, the interpolation device 22 calculates statistics in the overall distribution of data values ​​of the walking phase corresponding to the missing section using data values ​​(amplitude values) of the walking waveforms of multiple walking cycles that have no missing data in the walking phase of the missing section. The interpolation device 22 generates interpolation data for the missing section based on statistics in the overall distribution of data values ​​for each walking phase included in the missing section. In particular, the interpolation device 22 generates interpolation data for the missing section based on a representative value in the overall distribution of data values ​​for each walking phase included in the missing section. For example, the interpolation device 22 generates interpolation data for the missing section based on a deviation value in the overall distribution of data values ​​(amplitude values) for each walking phase included in the missing section. For example, the interpolation device 22 may generate the interpolation data for the missing section based on the standard deviation, variance, deviation, etc. instead of the deviation value. The interpolation device 22 uses the generated interpolation data to interpolate the missing section.

[0120] Gait measurement device 23 has the same configuration as gait measurement device 13 of the first embodiment. Gait measurement device 23 acquires a gait waveform that does not include a missing section from interpolation device 22. Gait measurement device 23 performs measurement related to gait using the acquired gait waveform. Gait measurement device 23 outputs measurement results related to gait (also referred to as gait information).

[0121] For example, the interpolation device 22 and the gait measurement device 23 are implemented in a server or the like (not shown). For example, the interpolation device 22 and the gait measurement device 23 may be realized by an application server. For example, the interpolation device 22 and the gait measurement device 23 may be realized by application software or the like installed on a mobile terminal (not shown).

[0122] [Interpolation device] Next, a detailed configuration of the interpolation device 22 will be described with reference to the drawings. Fig. 18 is a block diagram showing an example of a detailed configuration of the interpolation device 22. The interpolation device 22 includes a receiving unit 221, a gait information processing unit 222, a missing information processing unit 223, a storage unit 225, an interpolation unit 226, and a transmitting unit 228.

[0123] The receiving unit 221 has the same configuration as the receiving unit 121 of the first embodiment. The receiving unit 221 receives sensor data from a measurement device (not shown). The receiving unit 221 outputs the received sensor data to the gait information processing unit 222.

[0124] The gait information processing unit 222 has the same configuration as the gait information processing unit 122 of the first embodiment. The gait information processing unit 222 acquires sensor data from the receiving unit 221. The gait information processing unit 222 generates time series data of the acquired sensor data. The gait information processing unit 222 generates the time series data based on a timestamp included in the sensor data. The gait information processing unit 222 extracts a waveform for one step gait cycle from the generated time series data. The gait information processing unit 222 normalizes the waveform for the extracted one step gait cycle to generate a gait waveform for one step gait cycle. The gait information processing unit 222 stores the normalized gait waveform in the storage unit 225.

[0125] The missing information processing unit 223 has the same configuration as the missing information processing unit 123 in the first embodiment. The missing information processing unit 223 acquires a missing waveform. The missing information processing unit 223 stores information about a missing section included in the missing waveform (also called missing information) in association with the missing waveform in the storage unit 225.

[0126] The storage unit 225 has the same configuration as the storage unit 125 of the first embodiment. A walking waveform is stored in the storage unit 225. The walking waveforms stored in the storage unit 225 include a walking waveform not including a missing section (also called a normal waveform), a walking waveform including a missing section (also called a missing waveform), and a walking waveform in which the missing section has been restored (also called a restored waveform). The missing waveform is associated with loss information of the missing waveform. The walking waveform stored in the storage unit 225 is acquired by the transmission unit 228 at a predetermined timing, and is transmitted from the transmission unit 228 to a gait measurement device (not shown).

[0127] The interpolation unit 226 acquires from the storage unit 225 a plurality of walking waveforms having no loss in the walking phase of the missing section included in the missing waveform to be interpolated. The interpolation unit 226 calculates a statistic in the overall distribution of the data values ​​of the walking phase corresponding to the missing section in the data group of the plurality of walking waveforms, using the data values ​​(amplitude values) of the acquired plurality of walking waveforms. In particular, the interpolation unit 226 calculates a representative value such as a deviation value, a standard deviation, a variance, or a deviation as the statistic. For example, the interpolation unit 226 calculates a deviation value of the data values ​​of the walking phase included in the missing section in the overall distribution of the data group of the plurality of walking waveforms. The interpolation unit 226 generates the interpolation data based on the calculated statistic. For example, the interpolation unit 226 generates the interpolation data based on the deviation value. For example, the interpolation unit 226 may generate the interpolation data of the missing section based on the standard deviation, the variance, the deviation, or the like instead of the deviation value. The interpolation unit 226 uses the generated interpolation data to interpolate the missing section.

[0128] Here, a method for generating interpolation data based on a deviation value, which is one of statistics (representative values), will be described with reference to several drawings (FIGS. 19 to 23). FIG. 19 shows time-series data of forward acceleration (Y-direction acceleration) measured as the user walks. The time-series data in FIG. 19 includes waveforms of multiple walking cycles during a measurement period of 20 seconds. The waveforms of each walking cycle show a similar pattern, although the data values ​​(amplitude values) differ.

[0129] FIG. 20 shows a waveform (walking waveform) obtained by extracting one of the waveforms of multiple walking cycles included in the time-series data of FIG. 19 and normalizing the extracted waveform. A pattern including walking characteristics can be seen in the walking waveform of FIG. 20. FIG. 20 shows walking phases (P1, P2) used in the following description. In the following, walking phase P1 and walking phase P2 are applied not only to the walking cycle of FIG. 20 but also to multiple walking cycles.

[0130] FIG. 21 shows the frequency distribution of data values ​​(amplitude values) in walking phases P1 and P2, with the walking waveforms of multiple walking cycles extracted from the time-series data of FIG. 19 as the sample group (whole). For walking phase P1, the frequency distribution of data values ​​shows a distribution close to a normal distribution. For walking phase P2, the frequency distribution of data values ​​shows a distribution close to a normal distribution. For all other walking phases, the frequency distribution of data values ​​for each 1% was normalized and discriminated by the Kolmogorov-Smirnov test, and a positive result was obtained. That is, the frequency distribution of data values ​​in each walking phase of the walking waveforms of multiple walking cycles shows a distribution close to a normal distribution regardless of the walking cycle. This shows that it is possible to estimate the interpolated data for the walking waveforms of multiple walking cycles based on the overall distribution of data values ​​for each walking phase. In other words, it is possible to estimate the interpolated data for the walking waveforms of multiple walking cycles based on the statistics in the overall distribution of data values ​​for each walking phase.

[0131] Fig. 22 is a graph showing the correlation of the standard deviation in the overall distribution of data values ​​of data N and data N+1 in consecutive walking phases in the time-series data of the sensor data in Fig. 19 (N is a natural number). As shown in Fig. 22, the standard deviation of consecutive data N and data N+1 shows an almost proportional relationship. In other words, the standard deviation in the overall distribution of data values ​​in walking phase P2 shows similar values ​​for consecutive data N and data N+1.

[0132] FIG. 23 is a graph showing the correlation coefficient of the deviation value according to the distance from the target walking phase (walking phase P2). The solid line in FIG. 23 shows the correlation coefficient between the deviation value of the data value of the walking phase P2 and the data value of a walking phase different from the walking phase P2, for the sample group of walking waveforms of a plurality of walking cycles cut out from the time series data in FIG. 19. The dashed line in FIG. 23 is a curve obtained by fitting the correlation coefficient (solid line) to a polynomial. As shown in FIG. 23, it can be seen that the closer the walking phase is from the target walking phase, the larger the correlation coefficient of the deviation value is, and the deviation value shows a similar value. In other words, the closer the walking phase is from the target walking phase, the larger the correlation coefficient is, and the closer the deviation value is. That is, the interpolation data of the target walking phase can be generated by using the data value of a walking phase with a deviation value close to the deviation value of the target walking phase.

[0133] For example, the interpolation unit 226 calculates the interpolation data by performing a reverse calculation from the deviation value of the calculated walking phase, and generates the interpolation data for interpolating the missing section. For example, the interpolation unit 226 may generate the interpolation data based on a representative value such as a standard deviation, a variance, or a deviation instead of the deviation value. For example, the interpolation unit 226 reverse-calculates the following formula 1 to obtain the value d of the interpolation data in the walking phase j. j The following formula 1 is the deviation value T of the data value of the walking phase j included in the missing section. j This is the calculation formula. TIFF0007626221000001.tif11150

[0134] In the above formula 1, μ is the average value of the data values ​​of the walking phase j in the multiple walking waveforms included in the target data group. σ is the standard deviation of the data values ​​of the walking phase j in the multiple walking waveforms included in the target data group. When generating the interpolated data based on a representative value of the standard deviation, variance, deviation, etc. instead of the deviation value, the value of the interpolated data can be calculated by inversely calculating the formula for the standard deviation, variance, deviation, etc.

[0135] For example, the interpolation unit 226 may estimate the interpolation data of the missing section based on the deviation value of the data value of the start point and the end point of the missing section included in the missing waveform to be interpolated. FIG. 24 is a graph for explaining an example of the interpolation of the missing section based on the deviation value of the start point (walking phase M1) and the end point (walking phase M2) of the missing section included in the missing waveform to be interpolated. For example, the interpolation unit 226 calculates the average value of the deviation value in the entire distribution of the data value of the walking phase M1 and the deviation value in the entire distribution of the data value of the walking phase M2 for the missing section. The interpolation unit 226 estimates the interpolation data from the entire distribution of the data value using the average value of the deviation values ​​of the data values ​​of the walking phase M1 and the walking phase M2. In the method of FIG. 24, the closer the distance between the walking phase M1 and the walking phase M2 of the missing section is, the higher the estimation accuracy of the interpolation data is.

[0136] For example, the method of generating interpolated data by the interpolation unit 226 of this embodiment may be combined with the method of generating interpolated data by the interpolation unit 126 of the first embodiment. For example, the interpolated data may be generated by the method of the first embodiment, and the start point and end point of the generated interpolated data may be corrected by the method of this embodiment.

[0137] The interpolation unit 226 stores the walking waveform (restored waveform) in which the missing sections have been interpolated in the storage unit 225. For example, the interpolation unit 226 may label the missing sections in the restored waveform so that the restored missing sections can be identified. For example, the interpolation unit 226 may store the restored waveform (also called the provisional restored waveform) in which provisional interpolation data has been inserted in the missing sections in the storage unit 225. The restored waveform stored in the storage unit 225 is used for gait measurement by a gait measurement device (not shown) in the same way as other walking waveforms.

[0138] The transmission unit 228 acquires a gait waveform from the storage unit 225. The transmission unit 228 acquires a gait waveform including a normal waveform and a restored waveform from the storage unit 225. The transmission unit 228 transmits the acquired gait waveform to a gait measurement device (not shown). The transmission unit 228 may transmit the gait waveform to the gait measurement device via a wired connection such as a cable, or may transmit the gait waveform to the gait measurement device via wireless communication. There are no particular limitations on the communication method between the transmission unit 228 and the gait measurement device.

[0139] (operation) Next, the operation of the interpolation device 22 will be described with reference to the drawings. In the following, an example will be described in which the interpolation unit 226 included in the interpolation device 22 performs interpolation of a missing section based on the deviation values ​​of the start point and end point of the missing section. The following operation of the interpolation device 22 may include processing and an order different from those of the above-mentioned interpolation unit 226. The processing of the gait information processing unit 222 and the missing information processing unit 223 is omitted since it is the same as in the first embodiment.

[0140] [Interpolation Processing] The interpolation processing by the interpolation unit 226 will be described with reference to a flowchart. Fig. 25 is a flowchart for explaining the interpolation processing by the interpolation unit 226. In the explanation following the flowchart of Fig. 25, the interpolation unit 226 will be described as the subject of operation.

[0141] 25, first, the interpolation unit 226 acquires time-series data including a missing section (missing waveform) from the storage unit 225 (step S231). The interpolation unit 226 may acquire the missing waveform from the gait information processing unit 222. The missing waveform is associated with missing information indicating the walking phase of the missing section.

[0142] Next, the interpolation unit 226 acquires from the storage unit 225 walking waveforms of a plurality of walking cycles in which the walking phase of the missing section included in the missing waveform is free of any missing portion (step S232).

[0143] Next, the interpolation unit 226 uses the acquired data of the plurality of walking waveforms to derive the overall distribution of data of the walking phase corresponding to the missing section (step S233).

[0144] Next, the interpolation unit 226 calculates the deviation values ​​of the walking phases at the start and end points of the missing section in the entire distribution of data values ​​of the walking phases included in the missing section (step S234).

[0145] Next, the interpolation unit 226 generates interpolation data for the missing section using the calculated deviation value (step S235).

[0146] Next, the interpolation unit 226 inserts the generated interpolation data into the missing section (step S236). For example, the interpolation unit 226 corrects the start point and the end point of the interpolation data, and inserts the interpolation data into the missing section.

[0147] Next, the interpolation unit 226 resamples the corrected waveform to restore it to time-series data (step S237). Step S237 is performed because the original time-series data is required when calculating the walking speed, stride length, etc. If the original time-series data is not required, step S237 may be omitted.

[0148] As described above, the gait measurement system of this embodiment includes a measurement device, an interpolation device, and a gait measurement device. The measurement device is placed on a user's footwear. The measurement device measures spatial acceleration and spatial angular velocity according to the user's gait. The measurement device generates sensor data based on the measured spatial acceleration and spatial angular velocity. The measurement device outputs the generated sensor data to the interpolation device. The interpolation device has a receiving unit, a gait information processing unit, a missing information processing unit, a storage unit, an interpolation unit, and a transmission unit. The receiving unit receives the sensor data transmitted from the measurement device. The gait information processing unit generates time-series data of the sensor data. The gait information processing unit generates a gait waveform for each gait cycle using the time-series data of the sensor data related to the movement of the feet, and identifies a missing section of data in the time-series data. The gait information processing unit stores the generated gait waveform and information on the identified missing section in the storage unit. The storage unit stores the gait waveform, information on the missing section, and a repair waveform in which the missing section is interpolated. The missing information processing unit calculates the gait phase of the identified missing section. The interpolation unit generates interpolation data for the missing section based on a representative value of data for each walking phase included in the missing section in a data group of walking waveforms of multiple walking cycles used for interpolating the missing section. For example, the interpolation unit generates the interpolation data for the missing section using a deviation value of data for each walking phase included in the missing section. The interpolation unit interpolates the generated interpolation data into the missing section. The gait measurement device acquires from the interpolation device a gait waveform that does not include the missing section generated by the interpolation device using time-series data of the sensor data, and a gait waveform in which the interpolation device has interpolated data for the missing section. The gait measurement device measures the user's gait based on walking events detected from the acquired gait waveform. The gait measurement device outputs information related to the measured gait of the user.

[0149] In the method of this embodiment, attention is focused on the periodicity of walking, and interpolation data is generated to fill missing sections of data that occur in time-series data of sensor data measured according to the user's walking. In the method of this embodiment, the interpolation data is generated based on a representative value of data for each walking phase included in the missing section. In the method of this embodiment, the interpolation data is generated based on the distribution of walking waveform data of multiple walking cycles generated from the time-series data. According to the method of this embodiment, by generating the interpolation data based on the overall distribution, the walking waveform data including sudden abnormalities is smoothed, so that the data of the missing section can be restored with higher accuracy.

[0150] (Third embodiment) Next, an interpolation device according to a third embodiment will be described with reference to the drawings. The interpolation device of this embodiment has a simplified configuration of the interpolation devices of the first and second embodiments. FIG. 26 is a block diagram showing an example of the configuration of an interpolation device 32 of this embodiment. The interpolation device 32 includes a gait information processing unit 322, a missing information processing unit 323, and an interpolation unit 326.

[0151] The gait information processing unit 322 generates a walking waveform for each walking cycle using time-series data of the sensor data related to the movement of the feet, and identifies a missing section of data in the time-series data. The missing information processing unit 323 calculates the walking phase of the identified missing section. The interpolation unit 326 generates interpolation data for interpolating the missing section using data of the walking phase of the missing section in the walking waveform of a walking cycle different from the walking cycle including the missing section. The interpolation unit 326 interpolates the generated interpolation data into the missing section.

[0152] According to this embodiment, since the interpolation data generated by focusing on the periodicity of walking is used, it is possible to interpolate data of missing sections including the characteristics of the missing sections of data contained in the time series data of the sensor data.

[0153] (Hardware) Here, a hardware configuration for executing the control and processing according to each embodiment of the present disclosure will be described using an information processing device 90 in Fig. 27 as an example. Note that the information processing device 90 in Fig. 27 is an example of a configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present disclosure.

[0154] As shown in Fig. 27, an information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input / output interface 95, and a communication interface 96. In Fig. 27, the interface is abbreviated as I / F (Interface). The processor 91, the main storage device 92, the auxiliary storage device 93, the input / output interface 95, and the communication interface 96 are connected to each other via a bus 98 so as to be able to communicate data with each other. In addition, the processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are connected to a network such as the Internet or an intranet via the communication interface 96.

[0155] The processor 91 loads a program stored in the auxiliary storage device 93 or the like into the main storage device 92. The processor 91 executes the program loaded into the main storage device 92. In this embodiment, a software program installed in the information processing device 90 may be used. The processor 91 executes the control and processing according to this embodiment.

[0156] The main memory device 92 has an area in which a program is loaded. The processor 91 loads a program stored in the auxiliary memory device 93 or the like in the main memory device 92. The main memory device 92 is realized by a volatile memory such as a dynamic random access memory (DRAM). Furthermore, a non-volatile memory such as a magnetoresistive random access memory (MRAM) may be configured / added to the main memory device 92.

[0157] The auxiliary storage device 93 stores various data such as programs. The auxiliary storage device 93 is realized by a local disk such as a hard disk or a flash memory. Note that it is also possible to configure the main storage device 92 to store various data and omit the auxiliary storage device 93.

[0158] The input / output interface 95 is an interface for connecting the information processing device 90 to peripheral devices based on standards and specifications. The communication interface 96 is an interface for connecting to external systems and devices through a network such as the Internet or an intranet based on standards and specifications. The input / output interface 95 and the communication interface 96 may be a common interface for connecting to external devices.

[0159] Input devices such as a keyboard, a mouse, and a touch panel may be connected to the information processing device 90 as necessary. These input devices are used to input information and settings. When a touch panel is used as an input device, the display screen of the display device may also serve as an interface for the input device. Data communication between the processor 91 and the input devices may be mediated by an input / output interface 95.

[0160] The information processing device 90 may be equipped with a display device for displaying information. When the display device is equipped, the information processing device 90 is preferably equipped with a display control device (not shown) for controlling the display of the display device. The display device may be connected to the information processing device 90 via the input / output interface 95.

[0161] The information processing device 90 may also be provided with a drive device. The drive device mediates between the processor 91 and a recording medium (program recording medium) for reading data and programs from the recording medium, writing the processing results of the information processing device 90 to the recording medium, and the like. The drive device may be connected to the information processing device 90 via an input / output interface 95.

[0162] The above is an example of a hardware configuration for enabling the control and processing according to each embodiment of the present invention. The hardware configuration in FIG. 27 is an example of a hardware configuration for executing the control and processing according to each embodiment, and does not limit the scope of the present invention. In addition, a program for causing a computer to execute the control and processing according to each embodiment is also included in the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also included in the scope of the present invention. The recording medium can be realized, for example, by an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may be realized by a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. In addition, the recording medium may be realized by a magnetic recording medium such as a flexible disk or other recording medium. When a program executed by a processor is recorded on a recording medium, the recording medium corresponds to a program recording medium.

[0163] The components of each embodiment may be combined in any manner. Furthermore, the components of each embodiment may be realized by software or by a circuit.

[0164] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Explanation of symbols]

[0165] 1, 2 Gait measurement system 11, 21 Measurement equipment 12, 22, 32 Interpolator 13, 23 Gait measurement device 111 Acceleration Sensor 112 Angular Rate Sensor 113 Control Unit 115 Transmitter 121, 221 Receiver 122, 222, 322 Gait information processing unit 123, 223, 323 Missing information processing unit 125, 225 Storage section 126, 226, 326 Interpolation 128, 228 Transmitter 131 Acquisition Department 132 Detection unit 133 Gait Measurement Department

Claims

1. a gait information processing means for generating a gait waveform for each gait cycle using time series data of the sensor data relating to foot movement and for identifying a data missing section in the time series data; A missing information processing means for calculating a walking phase of the identified missing section; an interpolation means for generating interpolation data for interpolating the missing section using data of the walking phase of the missing section in a walking waveform having a walking cycle different from a walking cycle including the missing section, and for interpolating the generated interpolation data into the missing section; The interpolation means With respect to the missing section included in the time series data, at least one piece of data of the walking phase included in the missing section is selected from at least one of the walking waveforms in which data of the walking phase included in the missing section is not missing; An interpolation device that generates the interpolated data using data of the selected walking phase.

2. The walking information processing means includes: A waveform for one walking cycle is extracted from the time series data, The interpolation device according to claim 1 , wherein the walking waveform is generated by normalizing the walking phase included in the extracted waveform.

3. The interpolation means Calculating an average value of data of the walking phase selected from the walking waveforms of the plurality of walking cycles; 3. The interpolation device according to claim 1, wherein the interpolated data is generated using an average value of the calculated data of the walking phase.

4. The interpolation means For a walking period included in the stance phase in which data fluctuation is smaller than that of the swing phase, the interpolated data is generated using an analytical method; 4. The interpolation device according to claim 1, wherein, for a walking period included in the swing phase in which data fluctuation is larger than that of the stance phase, the interpolated data is generated using data of a walking cycle different from the walking cycle including the missing section.

5. The interpolation means 5. The interpolation device according to claim 1, wherein the interpolation data for the missing section is generated based on a representative value of data for each of the walking phases included in the missing section in a data group of the walking waveform for a plurality of the walking cycles used for interpolating the missing section.

6. The interpolation means The interpolation device according to claim 5 , wherein the interpolation data for the missing section is generated using a deviation value of data for each of the walking phases included in the missing section.

7. An interpolation device according to any one of claims 1 to 6, a measurement device that is arranged in a user's footwear, measures spatial acceleration and spatial angular velocity in response to the user's walking, generates sensor data based on the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data to the interpolation device; a gait measurement device that acquires, from the interpolation device, a gait waveform that does not include missing sections that is generated by the interpolation device using time-series data of the sensor data and a gait waveform in which the interpolation device interpolates data of the missing sections, measures a gait of the user based on walking events detected from the acquired gait waveform, and outputs information regarding the measured gait of the user.

8. The computer A walking waveform is generated for each walking cycle using time series data of the sensor data regarding the movement of the feet. Identifying a data missing section in the time series data; Calculate the walking phase of the identified missing section; generating interpolation data for interpolating the missing section using data of the walking phase of the missing section in a walking waveform having a walking cycle different from the walking cycle including the missing section; The generated interpolation data is interpolated into the missing section; In the interpolation, With respect to the missing section included in the time series data, at least one piece of data of the walking phase included in the missing section is selected from at least one of the walking waveforms in which data of the walking phase included in the missing section is not missing; An interpolation method for generating the interpolated data using data of a selected one of the walking phases.

9. A process of generating a walking waveform for each walking cycle using time series data of the sensor data related to foot movement; A process of identifying a data missing section in the time series data; A process of calculating a walking phase of the identified missing section; A process of generating interpolation data for interpolating the missing section using data of the walking phase of the missing section in a walking waveform having a walking cycle different from a walking cycle including the missing section; A process of interpolating the generated interpolation data into the missing section; In the interpolation process, selecting at least one piece of data of the walking phase included in the missing section from at least one of the walking waveforms having no missing data of the walking phase included in the missing section, for the missing section included in the time series data; and generating the interpolated data using data of the selected walking phase.

Images