Detection device, detection system, gait measurement system, detection method, and program

Abstract

The detection device comprises: an acquisition unit that acquires data including a dorsiflexion peak time, a plantarflexion peak time, and travel direction acceleration, obtained from sensor data about the movement of the feet in order to detect heel grounding in the walking of a user by using data measured by a sensor installed on a foot portion of the user; a candidate detection unit that detects, as a candidate time for the heel grounding, a time of a feature signal point extracted from time-series data of the travel direction acceleration in an investigation time period starting from an acceleration peak time detected from the travel direction acceleration on the basis of the dorsiflexion peak time; and an output unit that outputs the detected candidate time as a heel grounding time.

Description

[Technical Field]

[0001] The present disclosure relates to a detection device and the like that detects gait events in response to a user's walking. [Background technology]

[0002] With growing interest in healthcare, services that provide information based on the characteristics contained in walking patterns (also called gait) are attracting attention. For example, technology has been developed to analyze gait based on sensor data measured by sensors mounted on footwear such as shoes. The characteristics of gait events (also called walking events) related to physical conditions appear in the time-series data of the sensor data. If the timing of walking events can be detected accurately, physical conditions can be estimated with higher accuracy.

[0003] Patent Document 1 discloses a foot and gait evaluation device. The device in Patent Document 1 acquires plantar pressure data when a user is walking or standing still from a pressure sensor installed in the shoes worn by the user. The device in Patent Document 1 analyzes the acquired plantar pressure data to acquire various parameters.

[0004] A gait evaluation system is disclosed in Patent Document 2. The system in Patent Document 2 calculates a gait evaluation value of a subject using acceleration data in three axes measured by an acceleration sensor attached to the ankle of the subject.

[0005] Patent Document 3 discloses a motion analysis device that identifies the timing of a person's walking motion using three-axis acceleration data acquired by a smartphone attached to the person's trunk and motion signals acquired by pressure sensors attached to the soles of the feet.

[0006] Non-Patent Document 1 discloses a method for calculating gait parameters using sensor data from an inertial sensor including an acceleration sensor and an angular velocity sensor. The method in Non-Patent Document 1 calculates the timing of a subject's walking events and parameters related to walking using three-axis acceleration data and three-axis angular velocity data measured by an inertial sensor attached to the side of a shoe. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Publication No. 2018 / 164157 [Patent Document 2] Japanese Patent Application Publication No. 2019-150329 [Patent Document 3] Japanese Patent Application Laid-Open No. 2015-83085 [Non-patent literature]

[0008] [Non-Patent Document 1] A. Rampp, et.al., “Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients”, IEEE Transactions on Biomedical Engineering, Vol. 62, No. 4, April, 2015, pp.1089-97. Summary of the Invention [Problem to be solved by the invention]

[0009] The method of Patent Document 1 analyzes the walking state of a user using data measured by a pressure sensor installed in the shoe. However, the method of Patent Document 1 cannot analyze the walking state of a user if the shoe does not have a pressure sensor installed in the shoe.

[0010] The method of Patent Document 2 analyzes the walking state of a subject using data measured by an acceleration sensor attached to the ankle. However, the method of Patent Document 2 cannot analyze the walking state of a subject if the acceleration sensor is not attached to the ankle.

[0011] The method of Patent Document 3 analyzes a person's walking state using data measured by an acceleration sensor attached to the trunk and a pressure sensor attached to the soles of the feet. However, the method of Patent Document 3 cannot analyze a person's walking state if a smartphone is not attached to the trunk or if a pressure sensor is not attached to the soles of the feet.

[0012] The method in Non-Patent Document 1 can detect a pedestrian's heel-strike event if the operating frequency of the acceleration sensor is 100 Hertz (Hz) or higher. However, if the operating frequency of the acceleration sensor is less than 100 Hz, it is difficult for a steep minimum peak to occur, and therefore the method in Non-Patent Document 1 cannot be applied.

[0013] An object of the present disclosure is to provide a detection device etc. that can detect heel strike during walking of a user using data measured by a sensor placed on the user's foot. [Means for solving the problem]

[0014] A detection device according to one aspect of the present disclosure includes an acquisition unit that acquires data including dorsiflexion peak times, plantar flexion peak times, and forward acceleration obtained from sensor data relating to foot movement; a candidate detection unit that detects, as candidate heel strike times, the times of feature signal points extracted from the time series data of forward acceleration within a search time period starting from the acceleration peak time detected from the forward acceleration, with the dorsiflexion peak time as the reference point; and an output unit that outputs the detected candidate times as heel strike times.

[0015] In a detection method according to one aspect of the present disclosure, data including dorsiflexion peak time, plantar flexion peak time, and forward acceleration is acquired from sensor data relating to foot movement, and the times of feature signal points extracted from the time series data of forward acceleration within a search time period starting from the acceleration peak time detected from the forward acceleration, with the dorsiflexion peak time as the reference point, are detected as candidate heel strike times, and the detected candidate times are output as heel strike times.

[0016] A program according to one aspect of the present disclosure causes a computer to perform the following processes: acquiring data including dorsiflexion peak time, plantar flexion peak time, and forward acceleration obtained from sensor data relating to foot movement; detecting, as candidate heel strike times, the times of feature signal points extracted from the time series data of forward acceleration within a search time period starting from the acceleration peak time detected from the forward acceleration, based on the dorsiflexion peak time; and outputting the detected candidate times as heel strike times. [Effects of the Invention]

[0017] According to the present disclosure, it is possible to provide a detection device or the like that can detect heel strike during walking of a user using data measured by a sensor placed on the user's foot. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a block diagram showing an example of the configuration of a detection system according to a first embodiment. [Figure 2] 2 is a conceptual diagram for explaining an example of the arrangement of a measuring device included in the detection system according to the first embodiment. FIG. [Figure 3] FIG. 2 is a conceptual diagram for explaining a coordinate system set in a measurement device included in the detection system according to the first embodiment. [Figure 4] 3 is a conceptual diagram for explaining a foot tilt angle measured by a measurement device included in the detection system according to the first embodiment. FIG. [Figure 5]1 is a conceptual diagram for explaining a human body surface used in the description of the detection system according to the first embodiment. FIG. [Figure 6] FIG. 1 is a conceptual diagram for explaining a walking cycle used in explaining the gait measurement device according to the first embodiment. [Figure 7] 1 is a block diagram showing an example of the configuration of a measurement device included in a detection system according to a first embodiment. [Figure 8] 4 is a graph for explaining a dorsiflexion peak and a plantar flexion peak detected by a measurement device included in the detection system according to the first embodiment. [Figure 9] 1 is a block diagram showing an example of the configuration of a detection device included in a detection system according to a first embodiment. [Figure 10] 4 is a graph for explaining an acceleration peak detected by a detection device included in the detection system according to the first embodiment. [Figure 11] 10 is a graph illustrating a mid-stance time calculated by a detection device included in the detection system according to the first embodiment. [Figure 12] 6 is a graph for explaining an example of detection of a first candidate time by a detection device included in the detection system according to the first embodiment. [Figure 13] 6 is a graph for explaining an example of detection of a first candidate time by a detection device included in the detection system according to the first embodiment. [Figure 14] 4 is a flowchart for explaining the operation of a detection device included in the detection system according to the first embodiment. [Figure 15] FIG. 10 is a block diagram showing an example of the configuration of a detection device according to a second embodiment. [Figure 16] 10 is a graph for explaining an example of detection of a second candidate time by the detection device according to the second embodiment. [Figure 17] 10 is a flowchart illustrating the operation of the detection device according to the second embodiment. [Figure 18]FIG. 10 is a block diagram showing an example of the configuration of a detection device according to a third embodiment. [Figure 19] 10 is a graph for explaining an example of detection of a third candidate time by the detection device according to the third embodiment. [Figure 20] 10 is a flowchart illustrating the operation of the detection device according to the third embodiment. [Figure 21] FIG. 10 is a block diagram showing an example of the configuration of a detection device according to a fourth embodiment. [Figure 22] 10 is a graph for explaining an example of determining the heel contact time by the detection device according to the fourth embodiment. [Figure 23] 10 is a flowchart illustrating the operation of the detection device according to the fourth embodiment. [Figure 24] FIG. 10 is a block diagram showing an example of the configuration of a gait measurement system according to a fifth embodiment. [Figure 25] FIG. 10 is a conceptual diagram for explaining an application example of the gait measurement system according to the fifth embodiment. [Figure 26] FIG. 13 is a block diagram showing an example of the configuration of a detection device according to a sixth embodiment. [Figure 27] FIG. 2 is a block diagram showing an example of a hardware configuration for realizing processing according to each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, 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 all drawings used to describe the following embodiments, the same reference numerals are used for similar parts unless otherwise specified. Furthermore, in the following embodiments, repeated explanations of similar configurations and operations may be omitted.

[0020] (First embodiment) First, a detection system according to a first embodiment will be described with reference to the drawings. The detection system according to this embodiment measures sensor data relating to foot movement measured in response to a user's walking. The detection system according to this embodiment detects the timing of heel strike, which is one of the events associated with walking (also called walking events), from the measured sensor data. The heel strike timing (also called heel strike time) detected by the detection system according to this embodiment is used to detect other walking events, calculate gait parameters, and so on. For example, the gait parameters are used to estimate the user's physical state.

[0021] (composition) 1 is a block diagram showing an example of the configuration of a detection system 1 according to this embodiment. The detection system 1 includes a measurement device 10 and a detection device 13. The measurement device 10 and the detection device 13 may be connected by wire or wirelessly. Furthermore, the measurement device 10 and the detection device 13 may be configured as a single device.

[0022] In this embodiment, an example will be described in which the measurement device 10 and the detection device 13 are configured as separate pieces of hardware. For example, the measurement device 10 is attached to footwear or the like of a user whose physical state is to be estimated. For example, the functions of the detection device 13 are installed in a mobile terminal carried by the user. The measurement device 10 and the detection device 13 may be configured as the same piece of hardware. For example, the measurement device 10 and the detection device 13 are configured as the same piece of hardware and attached to footwear or the like of a user. Below, the configurations of the measurement device 10 and the detection device 13 will be described individually.

[0023] [Measuring equipment] The measurement device 10 is attached to the user's foot. For example, the measurement device 10 is attached to the user's footwear. The measurement device 10 measures sensor data related to foot movement. The measurement device 10 includes sensors such as an acceleration sensor and an angular velocity sensor. The measurement device 10 generates sensor data using measurement values ​​measured by the sensors in response to foot movement.

[0024] FIG. 2 is a conceptual diagram showing an example of a measurement device 10 placed inside a shoe 100 for a right foot. In the example of FIG. 2, the measurement device 10 is placed at a position corresponding to the back of the arch of the foot. For example, the measurement device 10 is placed in an insole inserted into the shoe 100. For example, the measurement device 10 may be placed on the side or bottom of the shoe 100. For example, the measurement device 10 may be embedded in the body of the shoe 100. The measurement device 10 may be detachable from the shoe 100 or may not be detachable from the shoe 100. The measurement device 10 may be placed at a position other than the back of the arch of the foot as long as it can measure sensor data related to foot movement. The measurement device 10 may also be placed in socks worn by the user or in an accessory such as an anklet worn by the user. The measurement device 10 may also be attached directly to the foot or embedded in the foot. Figure 2 1 shows an example in which the measurement device 10 is installed in the shoe 100 of the right foot. The measurement device 10 may also be installed in the shoe 100 of the left foot. Furthermore, the measurement device 10 may also be installed in the shoes 100 of both feet.

[0025] In the example of FIG. 2, a local coordinate system is set with the measuring device 10 as the reference, and includes an x-axis in the left-right (lateral) direction, a y-axis in the front-back (direction of travel), and a z-axis in the up-down (vertical) direction. The x-axis is set to be positive to the left. The y-axis is set to be positive to the rear. The z-axis is set to be positive to the up. The directions of the axes set in the measuring device 10 may be the same for the left and right feet, or may be different for the left and right feet. For example, when measuring devices 10 manufactured with the same specifications are placed in left and right shoes 100, the up-down directions (directions of the Z-axis) of the sensors 11 placed in the left and right shoes 100 are the same. In this case, the three axes of the local coordinate system set for the sensor data derived from the left foot and the three axes of the local coordinate system set for the sensor data derived from the right foot are the same for the left and right feet.

[0026] FIG. 3 is a conceptual diagram illustrating a local coordinate system (x-axis, y-axis, z-axis) set in the measurement device 10 installed on the backside of the arch of the foot, and a world coordinate system (x-axis, y-axis, z-axis) set relative to the ground. In the world coordinate system (x-axis, y-axis, z-axis), when a user is standing upright facing the direction of travel, the user's lateral direction is set as the x-axis direction (leftward positive), the user's forward direction is set as the y-axis direction (backward positive), and the direction of gravity is set as the z-axis direction (vertically upward positive). The example in FIG. 3 conceptually illustrates the relationship between the local coordinate system (x-axis, y-axis, z-axis) and the world coordinate system (x-axis, y-axis, z-axis). The coordinate system in FIG. 3 does not accurately illustrate the relationship between the local coordinate system and the world coordinate system, which changes depending on the user's walking.

[0027] FIG. 4 is a conceptual diagram illustrating the foot tilt angle measured by the measurement device 10. The foot tilt angle is the angle of the sole of the foot with respect to the ground (XY plane). The foot tilt angle is defined as negative when the toes point upward (dorsiflexion). The foot tilt angle is defined as positive when the toes point downward (plantar flexion). For example, the sign of the foot tilt angle may be defined as positive or negative inversely. In this case, dorsiflexion is defined as positive, and plantar flexion is defined as negative.

[0028] FIG. 5 is a conceptual diagram illustrating planes (also called human body planes) set for the human body. A human body plane that divides the body into left and right halves is called a sagittal plane. A human body plane that divides the body into front and back halves is called a coronal plane. A human body plane that divides the body horizontally is called a horizontal plane. As shown in FIG. 5, when the user is standing upright with the centerline of the feet pointing in the direction of travel, the world coordinate system and the local coordinate system coincide. Rotation in the sagittal plane around the X-axis as the axis of rotation is defined as roll. Rotation in the coronal plane around the Y-axis as the axis of rotation is defined as pitch. Rotation in the horizontal plane around the Z-axis as the axis of rotation is defined as yaw. The rotation angle in the sagittal plane around the X-axis as the axis of rotation is called the roll angle. The rotation angle in the coronal plane around the Y-axis as the axis of rotation is called the pitch angle. The rotation angle in the horizontal plane around the Z-axis as the axis of rotation is called the yaw angle. In the following, the foot inclination angle and roll angle are used synonymously.

[0029] FIG. 6 is a conceptual diagram illustrating a step cycle based on the right foot. The step cycle based on the left foot is similar to that of the right foot. The horizontal axis of FIG. 6 represents one step cycle of the right foot, starting from the point when the heel of the right foot hits the ground and ending from the point when the heel of the right foot hits the ground again. The horizontal axis of FIG. 6 is normalized so that the step cycle is 100%. One step cycle of one foot is broadly divided into a stance phase, in which at least a portion 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. The horizontal axis of FIG. 6 is normalized so that the stance phase accounts for 60% and the swing phase accounts for 40%. The stance phase is further divided into a load response period T1, a mid-stance period T2, a final stance period T3, and an early swing period T4. The swing phase is further divided into an early swing period T5, a mid-swing period T6, and a final swing period T7. Note that FIG. 6 is an example and does not limit the periods that make up a walking cycle or the names of these periods.

[0030] As shown in Figure 6, multiple walking events occur during walking. E1 represents the event in which the heel of the right foot touches the ground (heel strike). HS: Heel Strike ) E2 represents the event where the toe of the left foot leaves the ground while the sole of the right foot is in contact with the ground (OTO: Opposite Toe Off). E3 represents the event where the heel of the right foot rises while the sole of the right foot is in contact with the ground (HR: Heel Rise). E4 represents the event where the heel of the left foot touches the ground ( Opposite foot heel touchdown ) (OHS: Opposite Heel Strike). E5 represents the event where the toe of the right foot leaves the ground (Toe Off) while the sole of the left foot is on the ground (TO: Toe Off). E6 represents the event where the left and right feet cross (Foot Cross) while the sole of the left foot is on the ground (FA: Foot Adjacent). E7 represents the event where the tibia of the right foot becomes almost perpendicular to the ground (Tibia Vertical) while the sole of the left foot is on the ground (TV: Tibia Vertical). E8 represents the event where the heel of the right foot touches the ground (Heel Strike) ( HS: Heel Strike) E8 corresponds to the end point of the walking cycle that starts from E1 and corresponds to the starting point of the next walking cycle. Note that FIG. 6 is an example and does not limit the events that occur during walking or the names of these events. The detection system 1 of this embodiment detects heel strike as a walking event.

[0031] 7 is a block diagram showing an example of the configuration of measurement device 10. Measurement device 10 has sensor 11 and peak detection unit 12. Sensor 11 includes an acceleration sensor 111 and an angular velocity sensor 112. Peak detection unit 12 includes coordinate conversion unit 121, low-pass filter 122, roll angle calculation unit 123, dorsiflexion peak detection unit 125, plantar flexion peak detection unit 126, and data transmission unit 127. Dorsiflexion peak detection unit 125 and plantar flexion peak detection unit 126 constitute plantar angle peak detection unit 124.

[0032] The acceleration sensor 111 is a sensor that measures acceleration in three axial directions (also called spatial acceleration). The acceleration sensor 111 measures the acceleration in three axial directions as a physical quantity related to foot movement. The acceleration sensor 111 outputs the measured acceleration in three axial directions to the peak detection unit 12. For example, a piezoelectric, piezo-resistive, or capacitive sensor can be used as the acceleration sensor 111. There is no limitation on the measurement method of the sensor used as the acceleration sensor 111 as long as it can measure acceleration.

[0033] The angular velocity sensor 112 is a sensor that measures angular velocity around three axes (also called spatial angular velocity). The angular velocity sensor 112 measures the angular velocity around three axes as a physical quantity related to foot movement. The angular velocity sensor 112 outputs the measured angular velocity to the peak detection unit 12. For example, a vibration type or capacitance type sensor can be used as the angular velocity sensor 112. There is no limitation on the measurement method of the sensor used as the angular velocity sensor 112 as long as it can measure angular velocity.

[0034] The sensor 11 is realized by, for example, an inertial measurement unit that measures acceleration and angular velocity. An example of an inertial measurement unit is an IMU (Inertial Measurement Unit). The IMU includes an acceleration sensor 111 that measures acceleration in three axial directions and an angular velocity sensor 112 that measures angular velocity around three axes. The sensor 11 may be realized by an inertial measurement unit such as a VG (Vertical Gyro) or an AHRS (Attitude Heading). The sensor 11 may also be realized by a GPS / INS (Global Positioning System / Inertial Navigation System). The sensor 11 may also be realized by a device other than an inertial measurement unit as long as it can measure physical quantities related to foot movement. The sensor 11 may also include sensors other than the acceleration sensor 111 and the angular velocity sensor 112. Description of other sensors that may be included in the sensor 11 will be omitted.

[0035] The coordinate transformation unit 121 acquires acceleration data and angular velocity data from the sensor 11. The coordinate transformation unit 121 calculates a quaternion and a traveling direction acceleration using the acceleration data and angular velocity data. The coordinate transformation unit 121 calculates a quaternion that represents the attitude of the sensor 11 using a Madgwick Filter algorithm. The coordinate transformation unit 121 calculates a traveling direction acceleration converted from the local coordinate system of the sensor 11 to the world coordinate system. The coordinate transformation unit 121 outputs the calculated quaternion and traveling direction acceleration.

[0036] The low-pass filter 122 acquires the traveling direction acceleration from the coordinate transformation unit 121. The low-pass filter 122 smoothes the traveling direction acceleration by removing high-frequency components of the traveling direction acceleration. The low-pass filter 122 outputs the smoothed traveling direction acceleration (also referred to as smoothed traveling direction acceleration). Hereinafter, the smoothed traveling direction acceleration will also be referred to as traveling direction acceleration.

[0037] The roll angle calculation unit 123 acquires the quaternion from the coordinate conversion unit 121. The roll angle calculation unit 123 uses the quaternion to calculate Euler angles that represent the attitude of the sensor 11. The Euler angles calculated by the roll angle calculation unit 123 are foot inclination angles (roll angles). The roll angle calculation unit 123 outputs the calculated roll angles.

[0038] The dorsiflexion peak detection unit 125 acquires the roll angle from the roll angle calculation unit 123. From the acquired time-series data of the roll angle, the dorsiflexion peak detection unit 125 detects the time at which the foot is most dorsiflexed in a walking cycle (also called the dorsiflexion peak time). For example, the dorsiflexion peak detection unit 125 detects the time at which the time-series data of the roll angle takes a minimum value as the dorsiflexion peak time. For example, the dorsiflexion peak detection unit 125 detects the time at which the roll angle takes a minimum value in the time-series data of the roll angle and the minimum value falls below a threshold as the dorsiflexion peak time. The dorsiflexion peak detection unit 125 outputs the detected dorsiflexion peak time.

[0039] The plantar flexion peak detection unit 126 acquires the roll angle from the roll angle calculation unit 123. From the acquired time-series data of the roll angle, the plantar flexion peak detection unit 126 detects the time at which the foot is most plantar flexed in a walking cycle (also called the plantar flexion peak time). For example, the plantar flexion peak detection unit 126 detects the time at which the time-series data of the roll angle takes a maximum value as the plantar flexion peak time. For example, the plantar flexion peak detection unit 126 detects the time at which the roll angle takes a maximum value in the time-series data of the roll angle and the maximum value exceeds a threshold as the plantar flexion peak time. The plantar flexion peak detection unit 126 outputs the detected plantar flexion peak time.

[0040] The dorsiflexion peak detecting unit 125 and the plantar flexion peak detecting unit 126 may be a single component (the plantar angle peak detecting unit 124). For example, the plantar angle peak detecting unit 124 detects the time when the time series data of the roll angle takes a minimum value as the dorsiflexion peak time. For example, the plantar angle peak detecting unit 124 detects the time when the time series data of the roll angle takes a maximum value as the plantar flexion peak time. The plantar angle peak detecting unit 124 sequentially outputs the alternately detected dorsiflexion peak time and plantar flexion peak time.

[0041] FIG. 8 shows an example of time-series data of the roll angle. The time-series data of the roll angle has a minimum value at a minimum point P d The time is the peak time of dorsiflexion t d The maximum point P b The time is the peak plantar flexion time t b For example, the sign of the foot inclination angle may be defined as being positive or negative. In this case, the time of the minimum point in the time series data of the roll angle corresponds to the time of the plantar flexion peak, and the time of the maximum point in the time series data of the roll angle corresponds to the time of the dorsiflexion peak.

[0042] The data transmitting unit 127 acquires the traveling direction acceleration from the low-pass filter 122. The data transmitting unit 127 acquires the dorsiflexion peak time from the dorsiflexion peak detecting unit 125. The data transmitting unit 127 acquires the plantar flexion peak time from the plantar flexion peak detecting unit 126. The data transmitting unit 127 transmits transmission data including the traveling direction acceleration, the dorsiflexion peak time, and the plantar flexion peak time to the detecting device 13. The transmission data may include data such as left-right acceleration (X-direction acceleration), vertical acceleration (Z-direction acceleration), and angular velocity and angle around three axes.

[0043] [Detection device] 9 is a block diagram showing an example of the configuration of the detection device 13. The detection device 13 has a data acquisition unit 131, a candidate detection unit 135, and an output unit 137. The candidate detection unit 135 has an acceleration peak time detection unit 151, a search end time calculation unit 152, a signal distance calculation unit 153, and a candidate time detection unit 154. The acceleration peak time detection unit 151 and the search end time calculation unit 152 constitute a search time period setting unit 150.

[0044] The data acquisition unit 131 acquires transmission data from the measurement device 10. The data acquisition unit 131 outputs the traveling direction acceleration and dorsiflexion peak time included in the acquired transmission data to the acceleration peak time detection unit 151. The traveling direction acceleration includes time series data of the signal point values ​​at the measurement timing (time) of the sensor data. The data acquisition unit 131 outputs the dorsiflexion peak time and plantar flexion peak time included in the acquired transmission data to the search end time calculation unit 152.

[0045] For example, the data acquisition unit 131 receives transmission data from the measurement device 10 via wireless communication. For example, the data acquisition unit 131 is configured to receive transmission data from the measurement device 10 via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or WiFi (registered trademark). The communication function of the data acquisition unit 131 may conform to standards other than Bluetooth (registered trademark) or WiFi (registered trademark). Furthermore, the data acquisition unit 131 may receive transmission data from the measurement device 10 via a wired connection such as a cable. The data acquisition unit 131 may receive transmission data via the communication function of a mobile terminal or the like in which the detection device 13 is implemented.

[0046] The acceleration peak time detection unit 151 acquires the travel direction acceleration and the dorsiflexion peak time from the data acquisition unit 131. The acceleration peak time detection unit 151 detects the time (also called the acceleration peak time) when the travel direction acceleration reaches its maximum value within a search time period before and after the dorsiflexion peak time. That is, the acceleration peak time detection unit 151 detects the acceleration peak time based on the dorsiflexion peak time. The acceleration peak time is the start time of the search time period (also called the first search time period) for the heel strike time. When the sign of the travel direction acceleration is opposite, the time when the travel direction acceleration reaches its minimum value corresponds to the acceleration peak time. For example, the acceleration peak time detection unit 151 detects the acceleration peak time within a predetermined time based on the dorsiflexion peak time. For example, the acceleration peak time detection unit 151 detects the acceleration peak time within a data range of several samples based on the dorsiflexion peak time. The acceleration peak time detection unit 151 outputs the detected acceleration peak time to the signal distance calculation unit 153.

[0047] 10 is a conceptual diagram for explaining an example of an acceleration peak detected by the acceleration peak time detection unit 151. FIG. 10 shows an example of a time series data waveform of the direction of travel of the foot measured in response to the user's walking, and a time series data waveform of the angle (roll angle) in the sagittal plane. The acceleration peak time detection unit 151 detects the dorsiflexion peak time t d The acceleration peak time is detected within the search time period before and after the step. In normal walking, the timing when the roll angle time series data takes on a minimum value (dorsiflexion peak) appears around the time of heel strike. The maximum peak that appears in the forward acceleration time series data is caused by the sudden deceleration that occurs just before heel strike. Usually, the difference between the dorsiflexion peak time and the acceleration peak time falls within a range of about 10% of the walking cycle. Therefore, the acceleration peak search time period should be set to a time range of about 10% of the walking cycle.

[0048] The search end time calculation unit 152 acquires the dorsiflexion peak time and the plantarflexion peak time from the data acquisition unit 131. The search end time calculation unit 152 detects the midpoint between the dorsiflexion peak time and the plantarflexion peak time (also called the mid-stance time) as the search end time. The search end time (also called the first search end time) calculated by the search end time calculation unit 152 is the end time of the first search time period. The search end time calculation unit 152 outputs the detected first search end time to the signal distance calculation unit 153.

[0049] Figure 11 shows an example of time series data of the roll angle. d and plantar flexion peak time t b The time of the midpoint of m At two consecutive mid-stance times t m The time period between these corresponds to a walking cycle.

[0050] The signal distance calculation unit 153 acquires the traveling direction acceleration from the data acquisition unit 131. The signal distance calculation unit 153 may acquire the traveling direction acceleration from the acceleration peak time detection unit 151. The signal distance calculation unit 153 also acquires the acceleration peak time from the acceleration peak time detection unit 151. Furthermore, the signal distance calculation unit 153 acquires the first search end time from the search end time calculation unit 152. The signal distance calculation unit 153 sets the time period from the acceleration peak time to the first search end time as the search time period (also referred to as the first search time period).

[0051] The signal distance calculation unit 153 draws a line (also referred to as a first reference line) that passes through the signal points at the acceleration peak time and the first search end time in the time-series data waveform of the traveling direction acceleration. The time-series data waveform of the traveling direction acceleration is expressed as a graph with the horizontal axis representing time and the vertical axis representing the traveling direction acceleration. For example, the signal distance calculation unit 153 draws a first reference line that passes through the signal points at the acceleration peak time and the first search end time in the time-series data waveform of the traveling direction acceleration. For example, the signal distance calculation unit 153 may draw a line segment connecting the signal points of the traveling direction acceleration at the acceleration peak time and the first search end time as the first reference line.

[0052] Signal distance calculation unit 153 calculates, as the first signal distance, the Euclidean distance between the signal point at each time of the time-series data waveform of the traveling direction acceleration and the first reference line during the first search time period. The first signal distance corresponds to the length of a perpendicular line drawn from the signal point at each time of the traveling direction acceleration to the first reference line. Signal distance calculation unit 153 outputs the first signal distance calculated for each time of the time-series data waveform of the traveling direction acceleration to candidate time detection unit 154.

[0053] Candidate time detection unit 154 acquires the first signal distance calculated for each time of the time-series data waveform of the traveling direction acceleration from signal distance calculation unit 153. Candidate time detection unit 154 detects the signal point at which the first signal distance is the longest. The signal point at which the first signal distance is the longest is called a feature signal point. Candidate time detection unit 154 detects the time of the feature signal point as a candidate time of heel strike. Candidate time detection unit 154 outputs the detected candidate time (also called a first candidate time) to output unit 137.

[0054] 12 and 13 show the first candidate time t h1 10 is a graph illustrating an example of detecting the acceleration peak time t a and the first search end time t s1 Draw a first reference line L1 that passes through the signal points at the first search end time ts1 is the mid-stance time t m The signal distance calculation unit 153 calculates the acceleration peak time t a and the first search end time t s1 The first search time period T s1 In this case, the signal point D at each time of the time series data waveform of the acceleration in the traveling direction t1 The Euclidean distance between the first reference line L1 and the first signal distance d t1 The candidate time detection unit 154 detects the first search time period T s1 At the first signal distance d t1 is the maximum (d max1 ) is called the first candidate time t h1 Detect as.

[0055] The output unit 137 acquires a first candidate time from the candidate time detection unit 154. The output unit 137 outputs the acquired first candidate time as a heel strike time. For example, the output unit 137 outputs the heel strike time to a system or device (not shown). For example, the output unit 137 outputs the heel strike time to other software installed inside a terminal device in which the detection device 13 is implemented. For example, the output unit 137 outputs the heel strike time from the terminal device in which the detection device 13 is implemented to a system or device (not shown) executed in a cloud or server. There are no limitations on the destination to which the heel strike time is output.

[0056] For example, the detection device 13 is connected to an external system, such as a cloud or a server, via a mobile terminal (not shown) carried by the user. The mobile terminal is a portable terminal device having a communication function. For example, the mobile terminal is a portable communication device having a communication function, such as a smartphone, a smart watch, or a mobile phone. For example, the detection device 13 is connected to the mobile terminal via a wired connection, such as a cable. For example, the detection device 13 is connected to the mobile terminal via wireless communication. For example, the detection device 13 is connected to the mobile terminal via a wireless communication function (not shown) conforming to standards such as Bluetooth (registered trademark) or WiFi (registered trademark). Note that the communication function of the detection device 13 may conform to standards other than Bluetooth (registered trademark) or WiFi (registered trademark). The heel strike time may be used by an application installed on the mobile terminal. In this case, the mobile terminal performs processing using the heel strike time using application software, etc., installed on the mobile terminal.

[0057] (operation) Next, the operation of the detection device 13 included in the detection system 1 will be described with reference to the drawings. Figure 14 10 is a flowchart for explaining an example of the operation of the detection device 13. Figure 14 In the explanation following the flowchart, the detection device 13 will be described as the subject of the operation.

[0058] Figure 14 In the process, first, the detection device 13 acquires the transmission data transmitted from the measurement device 10 (step S11). The transmission data includes the acceleration in the forward direction, the dorsiflexion peak time, and the plantar flexion peak time.

[0059] Next, the detection device 13 detects the acceleration peak time in the time-series data waveform of the traveling direction acceleration based on the dorsiflexion peak time (step S12). For example, the detection device 13 detects the acceleration peak time within a time range of about 10% of the walking cycle based on the dorsiflexion peak time. For example, the detection device 13 detects the acceleration peak time within a data range of several samples based on the dorsiflexion peak time.

[0060] Next, the detection device 13 calculates the time of the midpoint between the dorsiflexion peak time and the plantar flexion peak time (mid-stance time) as the first search end time (step S13). The first search end time corresponds to the mid-stance time.

[0061] Next, the detection device 13 calculates the first signal distance in the first search time period between the acceleration peak time and the first search end time (step S14). For example, the detection device 13 calculates the first signal distance in the time series data waveform of the acceleration in the traveling direction between the acceleration peak time and the first search end time. time A first reference line is drawn that passes through the signal points at the time. Detector 13 calculates the Euclidean distance (first signal distance) between the signal points at each time in the time-series data waveform of the acceleration in the traveling direction and the first reference line during the search time period.

[0062] Next, the detection device 13 detects the time when the first signal distance is maximum in the first search time period as the first candidate time (step S15).

[0063] Next, the detection device 13 outputs the detected first candidate time as a heel strike time (step S16). The heel strike time output from the detection device 13 is used for detecting a walking event, estimating the user's physical state, and the like.

[0064] As described above, the detection system of this embodiment includes a measurement device and a detection device. The measurement device includes a sensor and a peak detection unit. The sensor is attached to the user's footwear. The sensor measures spatial acceleration and spatial angular velocity. The sensor generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity. The sensor outputs the generated sensor data. The peak detection unit acquires time series data of the sensor data. The peak detection unit smoothes time series data of heading acceleration included in the sensor data. The peak detection unit detects dorsiflexion peak times and plantarflexion peak times from time series data of roll angle included in the sensor data. The peak detection unit outputs data including the smoothed heading acceleration, dorsiflexion peak times, and plantarflexion peak times to the detection device.

[0065] The detection device includes a data acquisition unit, a candidate detection unit, and an output unit. The data acquisition unit acquires data including a dorsiflexion peak time, a plantar flexion peak time, and a traveling direction acceleration obtained from sensor data related to foot movement. The candidate detection unit calculates a mid-stance time corresponding to the midpoint between the dorsiflexion peak time and the plantar flexion peak time as a first search end time. The candidate detection unit sets a time period from the acceleration peak time to the first search end time as a first search time period. The candidate detection unit sets a first reference line passing through a signal point of traveling direction acceleration at the acceleration peak time and a signal point of traveling direction acceleration at the first search end time. The candidate detection unit calculates a first signal distance corresponding to the Euclidean distance of the signal point of traveling direction acceleration to the first reference line for each traveling direction acceleration signal point included in the first search time period. The candidate detection unit detects a time of a feature signal point at which the calculated first signal distance is maximum as a candidate time. The output unit outputs the detected candidate time as a heel strike time.

[0066] In this embodiment, a first search time period is set, beginning with the acceleration peak time obtained from sensor data measured by sensors attached to the user's feet and ending with a first search end time. In this embodiment, the time of the feature signal point at which the first signal distance, which corresponds to the Euclidean distance of the signal point of the traveling direction acceleration from the first reference line set in the first search time period, is maximum, is detected as the candidate time. In this embodiment, the candidate time detected in the set first search time period is detected as the heel strike time. Therefore, according to this embodiment, heel strikes during the user's walking can be uniquely detected using data measured by sensors attached to the user's feet.

[0067] The method of this embodiment can be applied to gait analysis in fields such as medicine and healthcare. Heel strike is an important walking event in gait analysis. For example, the heel strike time detected by the method of this embodiment is used to analyze the relationship between the foot angle at that heel strike time and a specific disease. For example, the heel strike time detected by the method of this embodiment is used as a criterion for detecting other walking events.

[0068] (Second embodiment) Next, a detection device according to a second embodiment will be described with reference to the drawings. The detection device according to this embodiment differs from the first embodiment in the search time period for candidate heel strike times. The detection device according to this embodiment acquires transmission data from the measurement device according to the first embodiment.

[0069] (composition) 15 is a block diagram showing an example of the configuration of the detection device 23. The detection device 23 has a data acquisition unit 231, a candidate detection unit 235, and an output unit 237. The candidate detection unit 235 has an acceleration peak time detection unit 251, a search end time calculation unit 252, a signal distance calculation unit 253, and a candidate time detection unit 254. The acceleration peak time detection unit 251 and the search end time calculation unit 252 constitute a search time period setting unit 250.

[0070] The data acquisition unit 231 has the same configuration as the data acquisition unit 131 in the first embodiment. The data acquisition unit 231 acquires transmission data from a measurement device (not shown). The data acquisition unit 231 outputs the travel direction acceleration and dorsiflexion peak time included in the transmission data to the acceleration peak time detection unit 251. The travel direction acceleration includes time series data of the signal point values ​​at the measurement timing (time) of the sensor data. The data acquisition unit 231 outputs the dorsiflexion peak time and plantar flexion peak time included in the transmission data to the search end time calculation unit 252.

[0071] The acceleration peak time detection unit 251 has the same configuration as the acceleration peak time detection unit 151 included in the detection device 13 of the first embodiment. The acceleration peak time detection unit 251 acquires the traveling direction acceleration and the dorsiflexion peak time from the data acquisition unit 231. The acceleration peak time detection unit 251 detects the acceleration peak time at which the traveling direction acceleration reaches its maximum value in a search time period before and after the dorsiflexion peak time. That is, the acceleration peak time detection unit 251 detects the acceleration peak time based on the dorsiflexion peak time. The acceleration peak time is the start time of the search time period for heel strike (also called the second search time period). When the positive and negative signs of the traveling direction acceleration are opposite, the time at which the traveling direction acceleration reaches its minimum value corresponds to the acceleration peak time. The acceleration peak time detection unit 251 outputs the detected acceleration peak time to the signal distance calculation unit 253.

[0072] The search end time calculation unit 252 acquires the dorsiflexion peak time and the plantarflexion peak time from the data acquisition unit 231. The search end time calculation unit 252 acquires the acceleration peak time from the acceleration peak time detection unit 251. The search end time calculation unit 252 detects the midpoint time between the dorsiflexion peak time and the plantarflexion peak time (the mid-stance time). The search end time calculation unit 252 detects the time period between consecutive mid-stance times as a gait cycle. For example, the search end time calculation unit 252 calculates the difference between the latest mid-stance time being verified and the immediately preceding mid-stance time as the gait cycle. The search end time calculation unit 252 calculates the time a predetermined percentage of the gait cycle after the acceleration peak time as the search end time. In typical walking, heel strike occurs during approximately 10% of the time period immediately after the forward acceleration peaks. Therefore, the predetermined percentage of the gait cycle may be set to approximately 10% of the gait cycle. The search end time (also called the second search end time) calculated by search end time calculation unit 252 is the end time of the second search time period. Search end time calculation unit 252 outputs the calculated second search end time to signal distance calculation unit 253.

[0073] The signal distance calculation unit 253 acquires the traveling direction acceleration from the data acquisition unit 231. The signal distance calculation unit 253 may acquire the traveling direction acceleration from the acceleration peak time detection unit 251. The signal distance calculation unit 253 also acquires the acceleration peak time from the acceleration peak time detection unit 251. Furthermore, the signal distance calculation unit 253 acquires the second search end time from the search end time calculation unit 252. The signal distance calculation unit 253 sets the time period from the acceleration peak time to the second search end time as the search time period (also referred to as the second search time period).

[0074] The signal distance calculation unit 253 draws a line (second reference line) that passes through the signal points at the acceleration peak time and the second search end time in the time-series data waveform of the traveling direction acceleration. The time-series data waveform of the traveling direction acceleration is expressed as a graph with the horizontal axis representing time and the vertical axis representing the traveling direction acceleration. For example, the signal distance calculation unit 253 draws a second reference line that passes through the signal points at the acceleration peak time and the second search end time in the time-series data waveform of the traveling direction acceleration. For example, the signal distance calculation unit 253 may draw a line segment connecting the signal points of the traveling direction acceleration at the acceleration peak time and the second search end time as the second reference line.

[0075] Signal distance calculation unit 253 calculates, as the second signal distance, the Euclidean distance between the signal point at each time of the time-series data waveform of the traveling direction acceleration and the second reference line during the second search time period. The second signal distance corresponds to the length of a perpendicular line drawn from the signal point at each time of the traveling direction acceleration to the second reference line. Signal distance calculation unit 253 outputs the second signal distance calculated for each time of the time-series data waveform of the traveling direction acceleration to candidate time detection unit 254.

[0076] Candidate time detection unit 254 acquires the second signal distance calculated for each time of the time-series data waveform of the traveling direction acceleration from signal distance calculation unit 253. Candidate time detection unit 254 detects the signal point at which the second signal distance is maximum. The signal point at which the second signal distance is maximum is called a feature signal point. Candidate time detection unit 254 detects the time of the feature signal point as a candidate time of heel strike. Candidate time detection unit 254 outputs the detected candidate time (also called a second candidate time) to output unit 237.

[0077] Figure 16 shows the second candidate time t h2 10 is a graph for explaining an example of detecting the acceleration peak time t a and the second search end time t s2 The signal distance calculation unit 253 draws a second reference line L2 that passes through the signal points at the acceleration peak time ta and the second search end time t s2 The second search time period T s2 In this case, the signal point D at each time of the time series data waveform of the acceleration in the traveling direction t2 The Euclidean distance between the second reference line L2 and the second signal distance d t2 The candidate time detection unit 254 detects the second search time period T s2 At the second signal distance d t2 is the maximum (d max2 ) is called the second candidate time t h2 The second signal distance d t2 is the maximum (d max2 ) signal point D t2 are also called feature signal points.

[0078] The output unit 237 acquires a second candidate time from the candidate time detection unit 254. The output unit 237 outputs the acquired second candidate time as a heel strike time. For example, the output unit 237 outputs the heel strike time to a system or device (not shown). For example, the output unit 237 outputs the heel strike time to other software installed inside a terminal device in which the detection device 23 is implemented. For example, the output unit 237 outputs the heel strike time from the terminal device in which the detection device 23 is implemented to a system or device (not shown) executed in a cloud or server. There are no limitations on the destination to which the heel strike time is output.

[0079] (operation) Next, the operation of the detection device 23 will be described with reference to the drawings. Fig. 17 is a flowchart for explaining an example of the operation of the detection device 23. In the explanation following the flowchart of Fig. 17, the detection device 23 will be described as the subject of the operation.

[0080] Figure 17 In the process, first, the detection device 23 acquires transmission data transmitted from a measurement device (not shown) (step S21). The transmission data includes the acceleration in the forward direction, the dorsiflexion peak time, and the plantar flexion peak time.

[0081] Next, detection device 23 detects the acceleration peak time in the time-series data waveform of the traveling direction acceleration, using the dorsiflexion peak time as a reference (step S22). For example, detection device 23 detects the acceleration peak time within a time range of about 10% of the walking cycle, using the dorsiflexion peak time as a reference. For example, detection device 23 detects the acceleration peak time within a data range of several samples, using the dorsiflexion peak time as a reference.

[0082] Next, detection device 23 calculates the midpoint between the dorsiflexion peak time and the plantar flexion peak time as the mid-stance time (step S23).

[0083] Next, the detection device 23 calculates the time between successive mid-stance times as a step gait cycle (step S24).

[0084] Next, the detection device 23 calculates the time that is a predetermined percentage of the walking cycle after the acceleration peak time as the second search end time (step S25).

[0085] Next, detection device 23 calculates a second signal distance during a second search time period between the acceleration peak time and the second search end time (step S26). For example, detection device 23 draws a second reference line that passes through the signal points at the acceleration peak time and the second search end time in the time-series data waveform of the traveling direction acceleration. Detection device 23 calculates the Euclidean distance (second signal distance) between the signal points at each time in the time-series data waveform of the traveling direction acceleration and the second reference line during the second search time period.

[0086] Next, the detection device 23 detects the time when the second signal distance is maximum in the second search time period as a second candidate time (step S27).

[0087] Next, detection device 23 outputs the detected second candidate time as a heel strike time (step S28). The heel strike time output from detection device 23 is used for detecting walking events, estimating the user's physical state, and the like.

[0088] As described above, the detection device of this embodiment includes a data acquisition unit, a candidate detection unit, and an output unit. The data acquisition unit acquires data including dorsiflexion peak time, plantar flexion peak time, and traveling acceleration obtained from sensor data related to foot movement. The candidate detection unit calculates a mid-stance time corresponding to the midpoint between the dorsiflexion peak time and the plantar flexion peak time. The candidate detection unit calculates the time period between consecutive mid-stance times as one gait cycle. The candidate detection unit sets the time a predetermined percentage of the walking cycle after the acceleration peak time as the second search end time. The candidate detection unit sets the time period from the acceleration peak time to the second search end time as the second search time period. The candidate detection unit sets a second reference line passing through the traveling acceleration signal point at the acceleration peak time and the traveling acceleration signal point at the second search end time. The candidate detection unit calculates a second signal distance corresponding to the Euclidean distance of the traveling acceleration signal point to the second reference line for the traveling acceleration signal point included in the second search time period. The candidate detection unit detects the time of the feature signal point at which the calculated second signal distance is maximum as a candidate time, and the output unit outputs the detected candidate time as a heel strike time.

[0089] In this embodiment, a second search time period is set, starting from the acceleration peak time obtained from sensor data measured by sensors attached to the user's feet and ending at the second search end time. In this embodiment, the time of the feature signal point at which the second signal distance, which corresponds to the Euclidean distance of the signal point of the traveling direction acceleration from the second reference line set in the second search time period, is maximum, is detected as the candidate time. In this embodiment, the candidate time detected in the second search time period is detected as the heel strike time. Therefore, according to this embodiment, heel strike during the user's walking can be uniquely detected using data measured by sensors attached to the user's feet.

[0090] (Third embodiment) Next, a detection device according to a third embodiment will be described with reference to the drawings. The detection device according to this embodiment differs from the first and second embodiments in the search time period for candidate heel strike times. The detection device according to this embodiment also differs from the first and second embodiments in that it does not use a reference line. The detection device according to this embodiment acquires transmission data from the measurement device according to the first embodiment.

[0091] (composition) 18 is a block diagram showing an example of the configuration of the detection device 33. The detection device 33 has a data acquisition unit 331, a candidate detection unit 335, and an output unit 337. The candidate detection unit 335 has an acceleration peak time detection unit 351 and a candidate time detection unit 354.

[0092] The data acquisition unit 331 has the same configuration as the data acquisition unit 131 in the first embodiment. The data acquisition unit 331 acquires transmission data from a measurement device (not shown). The data acquisition unit 331 outputs the traveling direction acceleration and dorsiflexion peak time included in the transmission data to the acceleration peak time detection unit 351. The traveling direction acceleration includes time series data of the signal point values ​​at the measurement timing (time) of the sensor data. The data acquisition unit 331 outputs the traveling direction acceleration included in the transmission data to the candidate time detection unit 354.

[0093] The acceleration peak time detection unit 351 has the same configuration as the acceleration peak time detection unit 151 included in the detection device 13 of the first embodiment. The acceleration peak time detection unit 351 acquires the traveling direction acceleration and the dorsiflexion peak time from the data acquisition unit 331. The acceleration peak time detection unit 351 detects the acceleration peak time at which the traveling direction acceleration reaches its maximum value in a search time period before and after the dorsiflexion peak time. That is, the acceleration peak time detection unit 351 detects the acceleration peak time based on the dorsiflexion peak time. When the positive and negative signs of the traveling direction acceleration are opposite, the time at which the traveling direction acceleration reaches its minimum value corresponds to the acceleration peak time. The acceleration peak time detection unit 351 outputs the detected acceleration peak time to the candidate time detection unit 354.

[0094] Candidate time detection unit 354 acquires the traveling direction acceleration from data acquisition unit 331. Candidate time detection unit 354 acquires the acceleration peak time from acceleration peak time detection unit 351. Candidate time detection unit 354 sets the time period following the acceleration peak time as the third search time period.

[0095] Candidate time detection unit 354 detects the signal point where traveling direction acceleration first reaches a minimum value in the third search time period. If the positive and negative signs of traveling direction acceleration are opposite, candidate time detection unit 354 detects the signal point where traveling direction acceleration first reaches a maximum value in the third search time period. In other words, candidate time detection unit 354 detects the signal point where traveling direction acceleration first reaches an extreme value in the third search time period. The signal point where traveling direction acceleration first reaches an extreme value in the third search time period is called a feature signal point. Candidate time detection unit 354 detects the time of the feature signal point as a candidate time of heel strike.

[0096] The beginning of the third search period is the acceleration peak time t a The end of the third search period is the acceleration peak time t a For example, the candidate time detection unit 354 may detect a time after the time when the traveling direction acceleration first reaches a minimum value after the time t n-1 , time t n , and time t n+1 (n is a natural number). Candidate time detection unit 354 detects the time at which the signal value at time t is smallest as the candidate time. Candidate time detection unit 354 outputs the detected candidate time (also called the third candidate time) to output unit 337.

[0097] Figure 19 shows the third candidate time t h3 10 is a graph illustrating an example of detecting the acceleration peak time t a The third search time period T S3 The time when the acceleration in the forward direction first reaches a minimum value is called the third candidate time t h3 Detect as.

[0098] The output unit 337 acquires a third candidate time from the candidate time detection unit 354. The output unit 337 outputs the acquired third candidate time as the heel strike time. For example, the output unit 337 outputs the heel strike time to a system or device (not shown). For example, the output unit 337 outputs the heel strike time to other software installed inside a terminal device in which the detection device 33 is implemented. For example, the output unit 337 outputs the heel strike time from the terminal device in which the detection device 33 is implemented to a system or device (not shown) executed on a cloud or server. There are no limitations on the destination to which the heel strike time is output.

[0099] (operation) Next, the operation of the detection device 33 will be described with reference to the drawings. Fig. 20 is a flowchart for explaining an example of the operation of the detection device 33. In the explanation following the flowchart of Fig. 20, the detection device 33 will be described as the subject of the operation.

[0100] 20, first, the detection device 33 acquires transmission data transmitted from a measurement device (not shown) (step S31). The transmission data includes the acceleration in the forward direction and the time of peak dorsiflexion.

[0101] Next, the detection device 33 detects the acceleration peak time in the time-series data waveform of the traveling direction acceleration, using the dorsiflexion peak time as a reference (step S32). For example, the detection device 33 detects the acceleration peak time within a time range of about 10% of the walking cycle, using the dorsiflexion peak time as a reference. For example, the detection device 33 detects the acceleration peak time within a data range of several samples, using the dorsiflexion peak time as a reference.

[0102] Next, the detector 33 detects the acceleration Degree Pi In a third search time period after the target time, the time at which the acceleration in the traveling direction first reaches a minimum value is detected as a third candidate time (step S33).

[0103] Next, the detection device 33 outputs the detected third candidate time as a heel strike time (step S34). The heel strike time output from the detection device 33 is used for detecting walking events, estimating the user's physical state, etc.

[0104] As described above, the detection device of this embodiment includes a data acquisition unit, a candidate detection unit, and an output unit. The data acquisition unit acquires data including dorsiflexion peak time, plantar flexion peak time, and forward acceleration obtained from sensor data related to foot movement. The candidate detection unit sets a time period starting from the acceleration peak time as a third search end time period. The candidate detection unit detects, within the third search end time period, the time at which the forward acceleration first reaches an extreme value as a candidate time. The output unit outputs the detected candidate time as a heel strike time.

[0105] In this embodiment, a third search time period is set starting from the time of the acceleration peak obtained from sensor data measured by sensors attached to the user's feet. In this embodiment, the time at which the forward acceleration first reaches an extreme value within the third search end time period is detected as a candidate time. Therefore, according to this embodiment, heel strikes during the user's walking can be uniquely detected using data measured by sensors attached to the user's feet.

[0106] (Fourth embodiment) Next, a detection device according to a fourth embodiment will be described in detail with reference to the drawings. The detection device according to this embodiment differs from the first embodiment in that it combines the techniques of the first to third embodiments to detect multiple candidates for heel strike timing and determines the heel strike time based on the detection results. The detection device according to this embodiment acquires transmission data from the measurement device according to the first embodiment.

[0107] The detection device of this embodiment includes one candidate detection unit each of the first to third embodiments. The number of candidate detection units included in the detection device of this embodiment is not limited to three. For example, the detection device of this embodiment may include four or more candidate detection units. For example, the detection device of this embodiment may be configured to combine two of the candidate detection units included in the first to third embodiments.

[0108] (composition) 21 is a block diagram showing an example of the configuration of detection device 43 according to this embodiment. Detection device 43 has a data acquisition section 431, a candidate detection section 435, and an output section 437. Candidate detection section 435 has a first candidate detection section 451, a second candidate detection section 452, a third candidate detection section 453, and a heel strike determination section 455.

[0109] The data acquisition unit 431 has the same configuration as the data acquisition unit 131 of the first embodiment. The data acquisition unit 431 acquires transmission data from a measurement device (not shown). The data acquisition unit 431 outputs the traveling direction acceleration, dorsiflexion peak time, and plantar flexion peak time included in the acquired transmission data to the first candidate detection unit 451, the second candidate detection unit 452, and the third candidate detection unit 453. The traveling direction acceleration includes time-series data of the signal point values ​​at the measurement timing (time) of the sensor data. The data output to each of the first candidate detection unit 451, the second candidate detection unit 452, and the third candidate detection unit 453 will be described later.

[0110] The first candidate detection unit 451 has the same configuration as the candidate detection unit 135 of the first embodiment. The first candidate detection unit 451 acquires the traveling direction acceleration, dorsiflexion peak time, and plantar flexion peak time from the data acquisition unit 431. The first candidate detection unit 451 detects the acceleration peak time at which the traveling direction acceleration is at its maximum, based on the dorsiflexion peak time. The first candidate detection unit 451 detects the mid-stance time that is the midpoint between the dorsiflexion peak time and the plantar flexion peak time as the first search end time. The first candidate detection unit 451 detects a first candidate for the heel strike time (first candidate time) within the first search time period between the acceleration peak time and the first search end time. The first candidate detection unit 451 outputs the detected first candidate time to the heel strike determination unit 455.

[0111] The second candidate detection unit 452 has the same configuration as the candidate detection unit 235 of the second embodiment. The second candidate detection unit 452 acquires the forward acceleration, dorsiflexion peak time, and plantar flexion peak time from the data acquisition unit 431. The second candidate detection unit 452 detects the acceleration peak time at which the forward acceleration is maximum, based on the dorsiflexion peak time. The second candidate detection unit 452 determines the mid-stance time that is the midpoint between the dorsiflexion peak time and the plantar flexion peak time as No. 2 The second candidate detection unit 452 detects the second search end time as the search end time. The second candidate detection unit 452 calculates the time between successive stance mid-phase times as the gait cycle. The second candidate detection unit 452 calculates the time a predetermined percentage of the gait cycle after the acceleration peak time as the second search end time. The second candidate detection unit 452 detects a second candidate for the heel strike time (second candidate time) in the second search time period between the acceleration peak time and the second search end time. The second candidate detection unit 452 outputs the detected second candidate time to the heel strike determination unit 455.

[0112] The third candidate detection unit 453 has the same configuration as the candidate detection unit 335 of the third embodiment. The third candidate detection unit 453 acquires the traveling direction acceleration and the dorsiflexion peak time from the data acquisition unit 431. The third candidate detection unit 453 detects the acceleration peak time at which the traveling direction acceleration is at its maximum, based on the dorsiflexion peak time. The third candidate detection unit 453 detects the time at which the traveling direction acceleration first reaches its minimum value within a third search time period after the acceleration peak time, as the third candidate time. The third candidate detection unit 453 outputs the detected third candidate time to the heel strike determination unit 455.

[0113] Heel strike determination unit 455 acquires a first candidate time from first candidate detection unit 451. Heel strike determination unit 455 acquires a second candidate time from second candidate detection unit 452. Heel strike determination unit 455 acquires a third candidate time from third candidate detection unit 453. Heel strike determination unit 455 determines the heel strike time using the first candidate time, second candidate time, and third candidate time.

[0114] For example, the heel strike determination unit 455 calculates the weighted average of the first candidate time, the second candidate time, and the third candidate time as the heel strike time. For example, the heel strike determination unit 455 uses the following equation 1 to determine the first candidate time t h1 , second candidate time t h2 , and the third candidate time t h3 Weighted average value of (heel contact time t h ) is calculated.

number

[0115] For example, the weighting coefficients set for the first, second, and third candidate times are set based on the accurate heel strike times measured using motion capture. For example, the weighting coefficients for the candidate times calculated by each detection method are set according to the evaluation results of the accuracy of the candidate times detected by each detection method of the first candidate detection unit 451, the second candidate detection unit 452, and the third candidate detection unit 453. The smaller the difference from the accurate heel strike time, the higher the accuracy of the candidate time. The higher the accuracy of the candidate time calculated by each detection method, the larger the weighting coefficient is set to.

[0116] The heel strike determination unit 455 may calculate a statistical value other than the weighted average value as the heel strike time. For example, the heel strike determination unit 455 may calculate the arithmetic mean or median of the first candidate time, the second candidate time, and the third candidate time as the heel strike time. For example, the heel strike determination unit 455 may calculate the heel strike time using machine learning including a linear regression model, a support vector machine, or a neural network.

[0117] The output unit 437 outputs the heel strike time determined by the heel strike determination unit 455. For example, the output unit 437 outputs the determined heel strike time to a system or device (not shown). For example, the output unit 437 outputs the heel strike time to other software installed inside a terminal device in which the detection device 43 is implemented. For example, the output unit 437 outputs the heel strike time from the terminal device in which the detection device 43 is implemented to a system or device (not shown) executed on a cloud or server.

[0118] 22 is a graph illustrating an example of how the heel strike determination unit 455 determines the heel strike time. h1 , second candidate time t h2 , and the third candidate time t h3 The weighted average value of each of these is calculated at the heel contact time t h For example, the heel strike determination unit 455 calculates the heel strike time t h Calculate.

number

[0119] (operation) Next, the operation of the detection device 43 will be described with reference to the drawings. Fig. 23 is a flowchart for explaining an example of the operation of the detection device 43. In the explanation following the flowchart of Fig. 23, the detection device 43 will be described as the subject of the operation.

[0120] 23, first, the detection device 43 acquires transmission data transmitted from a measurement device (not shown) (step S41). The transmission data includes the acceleration in the forward direction, the dorsiflexion peak time, and the plantar flexion peak time.

[0121] Next, the detection device 43 executes a first candidate detection process to detect a first candidate time (step S42). The first candidate detection process (step S42) is the process of the detection device 13 according to the first embodiment (steps S12 to S15 in FIG. 14).

[0122] Next, the detection device 43 executes a second candidate detection process to detect a second candidate time (step S43). The second candidate detection process (step S43) is the process of the detection device 23 according to the second embodiment (steps S22 to S27 in FIG. 17).

[0123] Next, the detection device 43 executes a third candidate detection process to detect a third candidate time (step S44). The third candidate detection process (step S44) is the process of the detection device 33 according to the third embodiment (steps S32 to S33 in FIG. 20).

[0124] Next, detection device 43 determines the heel strike time using the detected first, second, and third candidate times (step S45). For example, detection device 43 determines the weighted average of the first, second, and third candidate times as the heel strike time.

[0125] Next, the detection device 43 outputs the determined heel strike time (step S46). The heel strike time output from the detection device 43 is used to detect walking events, estimate the user's physical state, and so on.

[0126] As described above, the detection device of this embodiment includes a data acquisition unit, a candidate detection unit, and an output unit. The data acquisition unit acquires data including dorsiflexion peak time, plantar flexion peak time, and traveling direction acceleration obtained from sensor data related to foot movement. The candidate detection unit determines the heel strike time according to preset conditions from among multiple candidate times detected within a search time period set for traveling direction acceleration. For example, the candidate detection unit multiplies each of the multiple candidate times by a weight set for each candidate time to calculate a weighted average as the heel strike time. The output unit outputs the determined heel strike time.

[0127] In this embodiment, a third search time period is set, starting from the time of the peak acceleration obtained from the sensor data measured by the sensors attached to the user's feet. In this embodiment, the time when the traveling direction acceleration first reaches an extreme value in the third search end time period is detected as a candidate time.

[0128] In this embodiment, the heel strike time is determined using candidate times detected by multiple methods. Therefore, according to this embodiment, even if heel strike detection using one method fails, heel strike can be detected as long as heel strike is detected by another method. In other words, according to this embodiment, heel strike during a user's walking can be reliably detected by using candidate times for heel strike detected by multiple methods.

[0129] (Fifth embodiment) Next, a gait measurement system according to a fifth embodiment will be described with reference to the drawings. The gait measurement system of this embodiment includes the configuration of the measurement device according to the first embodiment. The gait measurement system of this embodiment also includes the configuration of any one of the detection devices according to the first to fourth embodiments.

[0130] 24 is a block diagram showing an example of the configuration of a gait measurement system 5 according to this embodiment. Gait measurement system 5 includes a measurement device 50, a detection device 53, and a gait measurement device 55. Measurement device 50 and detection device 53 configure a detection system.

[0131] The measurement device 50 has the same configuration as the measurement device 10 of the first embodiment. The measurement device 50 is placed on the user's foot. The measurement device 50 measures sensor data related to foot movement. The measurement device 50 includes sensors such as an acceleration sensor and an angular velocity sensor. The measurement device 50 generates sensor data using measurement values ​​measured by the sensors according to foot movement. The measurement device 50 smoothes the forward acceleration. The measurement device 50 also detects the dorsiflexion peak time and the plantarflexion peak time from the measured sensor data. The measurement device 50 outputs transmission data including the smoothed forward acceleration (forward acceleration), the dorsiflexion peak time, and the plantarflexion peak time to the detection device 53.

[0132] The detection device 53 has the same configuration as any of the detection devices of the first to fourth embodiments. The detection device 53 acquires transmission data from the measurement device 50. The detection device 53 detects candidate heel strike times using the forward acceleration, dorsiflexion peak time, and plantar flexion peak time included in the acquired transmission data. The detection device 53 outputs the heel strike time corresponding to the detected candidate time to the gait measurement device 55. The detection device 53 may output times such as the dorsiflexion peak time, plantar flexion peak time, mid-stance time, and acceleration peak time. For example, the mid-stance time can be used as a reference for extracting time series data of sensor data for one gait cycle.

[0133] The gait measurement device 55 acquires heel-strike times from the detection device 53. The gait measurement device 55 uses the acquired heel-strike times to detect other walking events, calculate gait parameters, and so on. For example, the gait measurement device 55 estimates the user's physical state using the calculated gait parameters. The gait measurement device 55 outputs information related to the timing of detected walking events, calculated gait parameters, estimated physical information, and so on. A detailed description of the information output from the gait measurement device 55 will be omitted.

[0134] For example, the gait measurement device 55 detects gait events such as opposite toe-off, heel lift, opposite heel-off, toe-off, foot crossing, and tibia vertical based on the heel-contact time. For example, the gait measurement device 55 detects gait events from time-series data (also called gait waveform) of sensor data of a stride cycle starting from the mid-stance time. For example, the gait measurement device 55 detects gait events according to features appearing in the gait waveform, such as forward acceleration, vertical acceleration, roll angular velocity, and roll angle.

[0135] For example, the gait measurement device 55 may identify characteristic sections (also referred to as gait periods) included in the evaluation section based on gait events detected from the gait waveform. For example, the gait measurement device 55 identifies the section between heel strike and toe off of the opposite foot as the load response period. For example, the gait measurement device 55 identifies the section between toe off of the opposite foot and heel lift as the mid-stance phase. For example, the gait measurement device 55 identifies the section between heel lift and heel strike of the opposite foot as the end-stance phase. For example, the gait measurement device 55 identifies the section between heel strike of the opposite foot and toe off as the early swing phase. For example, the gait measurement device 55 identifies the section between toe off and foot crossing as the early swing phase. For example, the gait measurement device 55 identifies the section between foot crossing and tibia vertical as the mid-swing phase. For example, the gait measurement device 55 identifies the section between tibia vertical and heel strike as the end of the swing phase.

[0136] For example, the gait measurement device 55 calculates gait parameters such as walking speed, stride length, contact angle, takeoff angle, foot lift height, circumduction, and foot angle according to the time of a walking event and the duration of a walking period. For example, the gait measurement device 55 calculates walking speed by dividing the distance traveled between detection times, obtained by second-order integration of the forward acceleration, by the time interval between the detection times, for consecutively detected identical walking events. For example, the gait measurement device 55 calculates, as the stride length, the absolute value of the difference between the spatial position at the time of foot crossing and the spatial position at the time of toe-off, for the walking waveform of the forward trajectory. For example, the gait measurement device 55 calculates, as the contact angle, the posture angle at the time of heel-contact. For example, the gait measurement device 55 calculates, as the takeoff angle, the posture angle at the time of toe-off. For example, the gait measurement device 55 calculates, as the takeoff angle, the maximum foot lift height based on a trajectory in the sagittal plane obtained by second-order integration of the vertical acceleration. For example, gait measurement device 55 calculates the circular motion based on the trajectory in the horizontal plane obtained by second-order integration of the lateral acceleration. For example, gait measurement device 55 uses the velocity vector and posture angle of the foot to calculate the angle between the velocity vector and the center line of the foot as the foot angle.

[0137] For example, the gait measurement device 55 estimates physical conditions such as gait symmetry, the progression of hallux valgus, and the degree of foot pronation / supination based on walking events and gait parameters. For example, the gait measurement device 55 estimates gait symmetry by comparing the extreme values ​​immediately before heel contact in time-series data of posture angles measured by the measurement devices 50 installed on the left and right feet. For example, the gait measurement device 55 estimates the progression of hallux valgus using a model trained on hallux valgus feature values ​​extracted from sensor data related to foot movement. For example, the gait measurement device 55 estimates the degree of foot pronation / supination using feature values ​​extracted from the coronal plane angle waveform during the final stance phase.

[0138] [Application example] Next, an application example of gait measurement device 55 according to this embodiment will be described with reference to the drawings. Here, an example is shown in which the function of gait measurement device 55 installed on a mobile terminal carried by a user estimates the physical state of the user using feature amount data measured by measurement devices 50 placed on the user's shoes.

[0139] 25 is a conceptual diagram showing an example in which information according to the measurement results by the gait measurement device 55 is displayed on the screen of a mobile terminal 560 carried by a user walking while wearing shoes 500 on which a measurement device 50 is placed. Fig. 25 shows an example in which estimation results and recommendation information according to sensor data measured while the user is walking are displayed on the screen of the mobile terminal 560.

[0140] For example, gait measurement device 55 calculates a numerical score based on a preset standard as an estimation result regarding the physical condition. Gait measurement device 55 displays information regarding the estimation result of the physical condition, such as "Your left-right balance is deteriorating," on the screen of mobile terminal 560, according to the score regarding the physical condition. Furthermore, gait measurement device 55 displays recommendation information, such as "Try to walk with the same stride length on the left and right," on the screen of mobile terminal 560, according to the score of the physical condition. After checking the information displayed on the screen of mobile terminal 560, the user can practice exercises that improve left-right balance by walking while being conscious of their left and right stride length in accordance with the displayed recommendation information.

[0141] As described above, the gait measurement system of this embodiment includes a measurement device, a detection device, and a gait measurement device. The measurement device includes a sensor and a peak detection unit. The sensor is attached to the user's footwear. The sensor measures spatial acceleration and spatial angular velocity. The sensor generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity. The sensor outputs the generated sensor data. The peak detection unit acquires time-series data of the sensor data. The peak detection unit smoothes time-series data of heading acceleration included in the sensor data. The peak detection unit detects dorsiflexion peak times and plantarflexion peak times from time-series data of roll angle included in the sensor data. The peak detection unit outputs data including the smoothed heading acceleration, dorsiflexion peak times, and plantarflexion peak times to the detection device.

[0142] The detection device includes a data acquisition unit, a candidate detection unit, and an output unit. The data acquisition unit acquires data including dorsiflexion peak times, plantar flexion peak times, and traveling acceleration obtained from sensor data related to foot movement. The candidate detection unit detects, as candidate heel strike times, times of feature signal points extracted from time-series data of traveling acceleration within a search time period starting from an acceleration peak time detected from traveling acceleration with the dorsiflexion peak time as the reference point. The output unit outputs the detected candidate times as heel strike times.

[0143] The gait measurement device detects walking events from the sensor data based on heel contact times detected by the detection device. The gait measurement device calculates gait parameters according to the detected walking events. The gait measurement device measures the user's gait using the calculated gait parameters.

[0144] In this embodiment, the user's gait can be measured using gait parameters calculated based on the heel strike times detected by the detection device.

[0145] (Sixth embodiment) Next, a detection device according to a sixth embodiment will be described with reference to the drawings. The detection device according to this embodiment has a simplified configuration of the detection devices according to the first to fifth embodiments. FIG. 26 is a block diagram showing an example of the configuration of a detection device 60 according to this embodiment. The detection device 60 includes a data acquisition unit 61, a candidate detection unit 65, and an output unit 67.

[0146] The data acquisition unit 61 acquires data including dorsiflexion peak times, plantar flexion peak times, and traveling direction acceleration obtained from sensor data related to foot movement. The candidate detection unit 65 detects, as candidate heel strike times, times of feature signal points extracted from the time-series data of traveling direction acceleration within a search time period starting from an acceleration peak time detected from traveling direction acceleration, with the dorsiflexion peak time as the reference point. The output unit 67 outputs the detected candidate times as heel strike times.

[0147] In this embodiment, a search time period for heel strike is set starting from the time of peak acceleration obtained from sensor data measured by a sensor attached to the user's foot. In this embodiment, candidate times detected within the set search time period are detected as heel strike times. Therefore, according to this embodiment, heel strikes during the user's walking can be detected using data measured by the sensor attached to the user's foot.

[0148] (Hardware) Here, regarding the hardware configuration for executing the processes according to each embodiment of the present disclosure, Figure 27 The following description will be given taking the information processing device 90 as an example. Figure 27 The information processing device 90 is an example configuration for executing the processes of each embodiment, and does not limit the scope of the present disclosure.

[0149] Figure 27As shown in FIG. 30, the 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. 30, 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. The processor 91, the main storage device 92, the auxiliary storage device 93, and the input / output interface 95 are also connected to a network such as the Internet or an intranet via the communication interface 96.

[0150] The processor 91 loads a program stored in an auxiliary storage device 93 or the like into a 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 processing according to each embodiment.

[0151] The main memory device 92 has an area in which programs are loaded. Programs stored in the auxiliary memory device 93 or the like are loaded into the main memory device 92 by the processor 91. The main memory device 92 is realized by a volatile memory such as a DRAM (Dynamic Random Access Memory). Furthermore, a non-volatile memory such as an MRAM (Magnetoresistive Random Access Memory) may be configured / added to the main memory device 92.

[0152] 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 flash memory. Note that it is also possible to configure the main storage device 92 to store various data, thereby omitting the auxiliary storage device 93.

[0153] 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 via 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.

[0154] Input devices such as a keyboard, mouse, and touch panel may be connected to the information processing device 90 as needed. These input devices are used to input information and settings. When a touch panel is used as the input device, the display screen of the display device may also serve as the interface for the input device. Data communication between the processor 91 and the input devices may be mediated by an input / output interface 95.

[0155] The information processing device 90 may also be equipped with a display device for displaying information. When a display device is equipped, the information processing device 90 preferably includes 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.

[0156] The information processing device 90 may also be equipped with a drive device. The drive device acts as an intermediary 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, etc. The drive device may be connected to the information processing device 90 via an input / output interface 95.

[0157] The above is an example of a hardware configuration for enabling processing according to each embodiment of the present invention. Note that the hardware configuration in FIG. 30 is an example of a hardware configuration for executing processing according to each embodiment and does not limit the scope of the present invention. Furthermore, a program that causes a computer to execute processing according to each embodiment is also within the scope of the present invention. Furthermore, a program recording medium on which a program according to each embodiment is recorded is also within the scope of the present invention. The recording medium can be realized, for example, as an optical recording medium such as a CD (Compact Disc) or a DVD (Digital Versatile Disc). The recording medium may also be realized as a semiconductor recording medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) card. The recording medium may also be realized as 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.

[0158] The components of each embodiment may be combined in any manner, and may be realized by software or by a circuit.

[0159] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above 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]

[0160] 1. Detection System 5 Gait measurement system 10, 50 Measuring equipment 11 Sensors 12 Peak detector 13 Detection equipment 111 Acceleration Sensor 112 Angular rate sensor 121 Coordinate conversion unit 122 Low-pass filter 123 Roll Angle Calculation Unit 124 Plantar angle peak detector 125 Dorsiflexion peak detector 126 Plantar flexion peak detector 127 Data transmission unit 131, 231, 331 Data acquisition section 135, 235, 335 Candidate detection section 137, 237, 337 Output section 150, 250 Search time zone setting section 151, 251, 351 Acceleration peak time detection section 152, 252 Search end time calculation unit 153, 253 Signal distance calculation section 154, 254, 354 candidate time detection section

Claims

1. A data acquisition means for acquiring data including peak dorsiflexion time, peak plantar flexion time, and forward acceleration obtained from sensor data relating to foot movement; a candidate detection means for detecting, as a candidate heel strike time, a time of a feature signal point extracted from the time series data of the traveling direction acceleration within a search time period starting from an acceleration peak time detected from the traveling direction acceleration, with the dorsiflexion peak time as a reference; an output means for outputting the detected candidate time as a heel strike time; The candidate detection means A mid-stance time corresponding to the midpoint between the dorsiflexion peak time and the plantar flexion peak time is calculated as a first search end time; A time period from the acceleration peak time to the first search end time is set as a first search time period; calculating a first signal distance corresponding to the Euclidean distance of the signal point of the traveling direction acceleration to a first reference line that passes through the signal point of the traveling direction acceleration at the acceleration peak time and the signal point of the traveling direction acceleration at the first search end time, for the signal point of the traveling direction acceleration included in the first search time period; a detection device that detects, as the candidate time, the time of the feature signal point at which the calculated first signal distance takes a maximum value.

2. A data acquisition means for acquiring data including peak dorsiflexion time, peak plantar flexion time, and forward acceleration obtained from sensor data relating to foot movement; a candidate detection means for detecting, as a candidate heel strike time, a time of a feature signal point extracted from the time series data of the traveling direction acceleration within a search time period starting from an acceleration peak time detected from the traveling direction acceleration, with the dorsiflexion peak time as a reference; an output means for outputting the detected candidate time as a heel strike time; The candidate detection means Calculating a mid-stance time corresponding to the midpoint between the dorsiflexion peak time and the plantar flexion peak time; A time period between successive mid-stance times is calculated as one gait cycle; setting a time that is a predetermined percentage of the walking cycle from the acceleration peak time as a second search end time; A time period from the acceleration peak time to the second search end time is set as a second search time period; calculate a second signal distance corresponding to the Euclidean distance of the signal point of the traveling direction acceleration to a second reference line that passes through the signal point of the traveling direction acceleration at the acceleration peak time and the signal point of the traveling direction acceleration at the second search end time, for the signal point of the traveling direction acceleration included in the second search time period; a detection device that detects, as the candidate time, the time of the feature signal point at which the calculated second signal distance takes a maximum value.

3. A data acquisition means for acquiring data including a dorsiflexion peak time, a plantar flexion peak time, and a forward acceleration obtained from sensor data relating to foot movement; a candidate detection means for detecting, as a candidate heel strike time, a time of a feature signal point extracted from the time series data of the traveling direction acceleration within a search time period starting from an acceleration peak time detected from the traveling direction acceleration, with the dorsiflexion peak time as a reference; an output means for outputting the detected candidate time as a heel strike time; The candidate detection means a time period starting from the acceleration peak time is set as a third search time period; a detection device that detects, as the candidate time, a time at which the traveling direction acceleration first reaches an extreme value within the third search time period.

4. The candidate detection means The detection device according to any one of claims 1 to 3, wherein the heel strike time is determined in accordance with preset conditions from among a plurality of candidate times detected within the search time period set for the travel direction acceleration.

5. The candidate detection means The detection device according to claim 4 , wherein the heel strike time is calculated as a weighted average value obtained by multiplying each of the plurality of candidate times by a weight set for each candidate time.

6. A detection device according to any one of claims 1 to 5; a sensor that is attached to a user's footwear, measures spatial acceleration and spatial angular velocity, generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data; and a peak detection means that acquires time-series data of the sensor data, smooths time-series data of forward acceleration included in the sensor data, detects dorsiflexion peak times and plantarflexion peak times from time-series data of roll angle included in the sensor data, and outputs data including the smoothed forward acceleration, dorsiflexion peak times, and plantarflexion peak times to the detection device.

7. A detection device according to any one of claims 1 to 5; a measurement device having: a sensor that is attached to a user's footwear, measures spatial acceleration and spatial angular velocity, generates sensor data related to foot movement using the measured spatial acceleration and spatial angular velocity, and outputs the generated sensor data; and peak detection means that acquires time-series data of the sensor data, smooths time-series data of forward acceleration included in the sensor data, detects dorsiflexion peak times and plantarflexion peak times from time-series data of roll angle included in the sensor data, and outputs data including the smoothed forward acceleration, dorsiflexion peak times, and plantarflexion peak times to the detection device; a gait measurement device that detects walking events from the sensor data based on heel contact times detected by the detection device, calculates gait parameters according to the detected walking events, and measures the gait of the user using the calculated gait parameters.

8. The computer Acquire data including a dorsiflexion peak time, a plantar flexion peak time, and a forward acceleration obtained from sensor data relating to foot movement; detecting, as a candidate heel strike time, a time of a feature signal point extracted from the time-series data of the traveling direction acceleration in a search time period starting from an acceleration peak time detected from the traveling direction acceleration with the dorsiflexion peak time as a reference; The detected candidate time is output as a heel strike time. In the detection, A mid-stance time corresponding to the midpoint between the dorsiflexion peak time and the plantar flexion peak time is calculated as a first search end time; A time period from the acceleration peak time to the first search end time is set as a first search time period; calculating a first signal distance corresponding to the Euclidean distance of the signal point of the traveling direction acceleration to a first reference line that passes through the signal point of the traveling direction acceleration at the acceleration peak time and the signal point of the traveling direction acceleration at the first search end time, for the signal point of the traveling direction acceleration included in the first search time period; a detection method for detecting, as the candidate time, a time of the feature signal point at which the calculated first signal distance is maximum;

9. A computer comprising: acquiring data obtained from sensor data relating to foot movement, the data including peak dorsiflexion time, peak plantarflexion time, and forward acceleration; detecting, as candidate heel strike times, times of feature signal points extracted from the time-series data of the traveling direction acceleration within a search time period starting from an acceleration peak time detected from the traveling direction acceleration, with the dorsiflexion peak time as a reference; a process of outputting the detected candidate time as a heel strike time; In the detecting process, a process of calculating a mid-stance time corresponding to a time of the midpoint between the dorsiflexion peak time and the plantar flexion peak time as a first search end time; a process of setting a time period from the acceleration peak time to the first search end time as a first search time period; calculating a first signal distance corresponding to the Euclidean distance of the signal point of the traveling direction acceleration to a first reference line that passes through the signal point of the traveling direction acceleration at the acceleration peak time and the signal point of the traveling direction acceleration at the first search end time, for the signal point of the traveling direction acceleration included in the first search time period; and detecting, as the candidate time, the time of the feature signal point at which the calculated first signal distance is maximum.

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