Method, device, electronic device and storage medium for determining human gait parameters

Abstract

The present disclosure provides a method, device, electronic device and storage medium for determining human gait parameters. The method comprises: obtaining a numerical value of three-dimensional acceleration and a numerical value of three-dimensional angular velocity during human walking; determining a first threshold value of a three-dimensional acceleration norm and a second threshold value of a three-dimensional angular velocity norm according to motion data; dividing a gait phase into a stationary phase and a non-stationary phase according to the first threshold value and the second threshold value; judging whether to re-segment the gait phase, if a preset peak value of the three-dimensional acceleration norm and / or a preset peak value of the three-dimensional angular velocity norm corresponds to a time period of the non-stationary phase, the gait phase is not re-segmented, if a preset peak value of the three-dimensional acceleration norm and a preset peak value of the three-dimensional angular velocity norm both correspond to a time period of the stationary phase, the gait phase is re-segmented; obtaining a bipedal walking trajectory during human walking; and determining gait parameters according to the segmentation result of the gait phase and the bipedal walking trajectory.

Description

Technical Field

[0001] The present disclosure relates to the technical field of gait analysis, and in particular to a method, device, electronic device and storage medium for determining human gait parameters. Background Art

[0002] Stroke remains a huge global burden. Stroke is the leading cause of disability worldwide, and more than 80% of survivors experience balance disorders, increasing their risk of falling during daily activities. Restoring the ability to walk is one of the most common goals of stroke survivors. Gait is an important indicator for predicting functional independence and long-term survival after stroke, so gait analysis can evaluate treatment effectiveness and track rehabilitation progress. Although gait analysis equipment in some hospitals can very accurately assess rehabilitation effects and progress, it is difficult to use these methods to assess patients' gait function at home or in the community. Therefore, it is of great significance to develop wearable sensors for quantitative gait assessment of stroke patients.

[0003] Although there are many literatures on estimating gait parameters in the past, and the methods are varied, most of the estimation of multiple gait parameters adopts inertial sensor networks, which are not suitable for home environments. Portable sensors suitable for home use are expensive and have poor performance in estimating gait parameters. Summary of the invention

[0004] The present disclosure provides a method, device, electronic device and storage medium for determining human gait parameters, so as to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present disclosure, a method for determining a human gait parameter is provided, the method comprising:

[0006] Acquiring motion data of a human body during walking through a sensor, wherein the motion data includes a value of three-dimensional acceleration and a value of three-dimensional angular velocity, and obtaining a numerical curve of a three-dimensional acceleration norm and a numerical curve of a three-dimensional angular velocity norm according to the values ​​of the three-dimensional acceleration and the values ​​of the three-dimensional angular velocity respectively;

[0007] Determining, according to the motion data, a first threshold value of the three-dimensional acceleration norm and a second threshold value of the three-dimensional angular velocity norm;

[0008] Segmenting the gait phase according to the first threshold and the second threshold, and segmenting the gait phase into a stationary phase and a non-stationary phase;

[0009] Determining whether to re-segment the gait phase, if the preset peak value of the three-dimensional acceleration norm and / or the preset peak value of the three-dimensional angular velocity norm correspond to a time period of a non-stationary phase, then the gait phase is not re-segmented, and if the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm both correspond to a time period of a stationary phase, then the gait phase is re-segmented;

[0010] Obtain the bipedal walking trajectory of a human body during walking;

[0011] The gait parameters are determined according to the segmentation results of the gait phases and the trajectory of the bipedal walking.

[0012] In one possible implementation, determining the first threshold of the three-dimensional acceleration norm and the second threshold of the three-dimensional angular velocity norm according to the motion data includes:

[0013] The norm of the three-dimensional acceleration and the norm of the three-dimensional angular velocity are calculated according to the following formula (1) and formula (2) respectively:

[0014]

[0015] Among them, t k represents the kth time point in the numerical curve, is the norm of the three-dimensional acceleration, is the three-dimensional acceleration, is the norm of the three-dimensional angular velocity, is the three-dimensional angular velocity;

[0016] The first threshold and the second threshold are calculated according to the following formula (3):

[0017]

[0018] in, represents the first threshold or the second threshold. When i=a, represents the first threshold, when i=w, represents the second threshold; W i represents the weight, ranging from 0 to 1, where when i=a, W i The weight corresponding to the three-dimensional acceleration, when i = w, W i The weight corresponding to the three-dimensional angular velocity; T - represents the sum of the number of three-dimensional accelerations less than the first threshold, or the sum of the number of three-dimensional angular velocities less than the second threshold, T + It represents the sum of the number of three-dimensional accelerations greater than the first threshold, or the sum of the number of three-dimensional angular velocities greater than the second threshold.

[0019] In one possible implementation, segmenting the gait phase according to the first threshold and the second threshold, and segmenting the gait phase into a stationary phase and a non-stationary phase, includes:

[0020] Segmenting the numerical curve of the three-dimensional acceleration norm according to the first threshold value to obtain a plurality of time periods;

[0021] The numerical curve of the three-dimensional angular velocity norm is segmented according to the second threshold to obtain multiple time periods; wherein, within the same time period, when the norm of the three-dimensional acceleration is less than the first threshold and the norm of the three-dimensional angular velocity is less than the second threshold, it is determined that the gait phase is in a stationary phase; when the norm of the three-dimensional acceleration is greater than the first threshold and / or when the norm of the three-dimensional angular velocity is greater than the second threshold, it is determined that the gait phase is in a non-stationary phase.

[0022] In one possible implementation, before determining whether to re-segment the gait phase, the method further includes:

[0023] Determine whether a peak value in the numerical curve of the three-dimensional acceleration norm and a peak value in the numerical curve of the three-dimensional angular velocity norm are preset peak values; if within the same time period, the sum of the width of the peak value of the three-dimensional acceleration norm and the width of the peak value of the three-dimensional angular velocity norm is greater than a third threshold, then the peak value of the three-dimensional acceleration norm is the preset peak value of the three-dimensional acceleration norm, and the peak value of the three-dimensional angular velocity norm is the preset peak value of the three-dimensional angular velocity norm, wherein the width of the peak value is the sum of the rise time and the fall time of the peak.

[0024] In one possible implementation manner, the third threshold is greater than or equal to 30 ms and less than or equal to 110 ms.

[0025] In one possible implementation, the motion data is in a carrier coordinate system, which is a coordinate system of the sensor itself;

[0026] The step of obtaining the bipedal walking trajectory of a human body during walking comprises:

[0027] The forward direction of the human body is the X-axis direction, the right side of the human body is the Y-axis direction, and the Z-axis direction perpendicular to the XY plane is downward to form a reference coordinate system;

[0028] Determine the rotation matrix according to the vector in the X-axis direction, the vector in the Y-axis direction and the vector in the Z-axis direction;

[0029] Determining an initial heading angle according to the rotation matrix;

[0030] According to the initial heading angle, the carrier coordinate system is converted into a reference coordinate system;

[0031] Repeat the above steps, and after at least one iteration, determine the trajectory of the bipedal walking.

[0032] In one embodiment, determining the gait parameters according to the segmentation result of the gait phase and the trajectory of the bipedal walking includes:

[0033] The gait parameters include stride, step length, pace, and step frequency. The stride represents the distance from when one side's heel touches the ground to when the heel touches the ground again. The step length represents the distance between the front heel and the rear heel during walking. The pace represents the ratio of the stride to the walking cycle. The step frequency represents the number of steps per minute.

[0034] Among them, the stride, step length, pace and step frequency are obtained according to the following formulas (4) to (7):

[0035]

[0036] Among them, SL m represents the stride, m represents the number of steps, X and Y represent the coordinate values ​​of the heel in the X-axis direction and the Y-axis direction respectively;

[0037]

[0038] in, represents the step length. When i represents the right foot, i′ represents the left foot. When i represents the left foot, i′ represents the right foot.

[0039]

[0040] Among them, SV m Indicates the pace, T stride,m The walking cycle is the time from the heel of one side touching the ground to the heel of the same side touching the ground again.

[0041]

[0042] Among them, C m Indicates cadence.

[0043] According to a second aspect of the present disclosure, a device for determining a human gait parameter is provided, the device comprising:

[0044] a first acquisition unit, configured to acquire motion data of a human body during walking through a sensor, wherein the motion data includes a value of three-dimensional acceleration and a value of three-dimensional angular velocity, and obtain a numerical curve of a three-dimensional acceleration norm and a numerical curve of a three-dimensional angular velocity norm according to the values ​​of the three-dimensional acceleration and the values ​​of the three-dimensional angular velocity, respectively;

[0045] A first determining unit, configured to determine a first threshold of the three-dimensional acceleration norm and a second threshold of the three-dimensional angular velocity norm according to the motion data;

[0046] A segmentation unit, configured to segment the gait phase according to the first threshold and the second threshold, and segment the gait phase into a stationary phase and a non-stationary phase;

[0047] a judgment unit, used to judge whether to re-segment the gait phase, if the preset peak value of the three-dimensional acceleration norm and / or the preset peak value of the three-dimensional angular velocity norm corresponds to a time period of a non-stationary phase, then the gait phase is not re-segmented, and if the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm both correspond to a time period of a stationary phase, then the gait phase is re-segmented;

[0048] The second acquisition unit is used to acquire the bipedal walking trajectory of the human body during walking;

[0049] The second determining unit is used to determine the gait parameters according to the segmentation result of the gait phase and the trajectory of the bipedal walking.

[0050] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0051] at least one processor; and

[0052] a memory communicatively connected to the at least one processor; wherein,

[0053] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present disclosure.

[0054] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the present disclosure.

[0055] The human gait parameter determination method, device, electronic device and storage medium disclosed in the present invention first obtain a first threshold and a second threshold, then perform a preliminary segmentation of the gait phase according to the first threshold and the second threshold, and then re-segment the gait phase according to the correspondence between the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm and the time period of the non-stationary phase, and finally obtain the gait parameters according to the gait phase segmentation result and the trajectory of bipedal walking. This solution improves the accuracy of gait phase segmentation, can more accurately estimate the gait parameters of human motion, and provides an effective method for gait analysis of patients in homes and communities using wearable devices.

[0056] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, in which:

[0058] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0059] Figure 1 A flow chart of a method for determining human gait parameters provided by an embodiment of the present disclosure;

[0060] Figure 2 A detailed process diagram of a method for determining human gait parameters provided in an embodiment of the present disclosure;

[0061] Figure 3 It is the curve diagram of gait phase segmentation result;

[0062] Figure 4 A comparison chart of the stride results between this solution and the prior art solution;

[0063] Figure 5 A comparison chart of the step length results in this solution and the prior art solution;

[0064] Figure 6 A comparison chart of the pace results in this solution and the prior art solution;

[0065] Figure 7 A comparison chart of the step frequency results in this solution and the prior art solution;

[0066] Figure 8 A schematic diagram of the structure of a device for determining human gait parameters provided in an embodiment of the present disclosure;

[0067] Fig. 9 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0068] In order to make the purpose, features, and advantages of the present disclosure more obvious and easy to understand, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.

[0069] In the prior art, in order to accurately evaluate the patient's gait, inertial sensors (IMUs) are required to provide accurate gait parameters. Some studies have summarized the current progress of wearable sensors for various aspects of gait analysis and the challenges of quantitative gait analysis. These studies point out two main technical challenges. First, due to the inherent characteristics of IMUs, errors accumulate over time. Second, the algorithm needs to be adjusted according to the walking patterns of different people. Adaptive algorithms are the key technology to improve the accuracy of gait parameters.

[0070] A widely accepted technique for eliminating cumulative errors is the zero-velocity update (ZUPT) algorithm. The ZUPT algorithm uses the calculated velocity of the inertial system in the carrier as the observed value of the system velocity error when the carrier is stationary, and corrects other error quantities to improve the combined navigation results in the stationary state. It does not require the addition of external sensors, so it is an effective, cheap and easy-to-implement technology. The ZUPT algorithm detects the stationary phase of the gait and resets its velocity to zero, thereby eliminating the errors of velocity and position. Therefore, zero-velocity detection requires accurate detection of the stationary and non-stationary phases of the gait cycle. Common zero-velocity detectors include acceleration-movement variance detectors, acceleration-amplitude detectors, angular rate energy detectors, and posture hypothesis optimal detectors. With the implementation of the zero-velocity update algorithm, more robust zero-velocity detectors have emerged. Improvements from fixed thresholds to adaptive thresholds are suitable for gait patterns of different people. For example, Bayesian detectors, zero-velocity detection methods based on generalized likelihood ratios, and iterative threshold segmentation methods. Data-driven zero-velocity detection methods include Bayesian methods, hidden Markov models, and long short-term memory. However, machine learning requires a large amount of training data. In addition, a sparse wavelet denoising method with a small amount of training has been proposed, which can automatically verify and segment the gait cycle in real time, but it is based on a fixed threshold and has certain limitations. Therefore, adaptive threshold combined with effective gait cycle verification and segmentation is a key technology to improve the accuracy and flexibility of the zero-rate update algorithm.

[0071] Use multi-level information fusion based on human gait constraints to improve accuracy. Existing technologies use features based on the distance constraints of human walking feet, including the maximum distance upper limit method, the ball limit method, and the center of mass method algorithm. Among them, the center of mass method has the highest position accuracy. But their shortcomings are that they all use fixed thresholds for zero rate detection. Another study used four sensors to use single-foot and double-foot constraints to improve the accuracy of step length and foot clearance. The disadvantage of using multiple sensors in the system is that it increases the complexity of the hardware system and data processing. Therefore, how to strike a balance between multi-information fusion and the number of sensors is also a challenge.

[0072] The present disclosure provides a method for determining human gait parameters. Figure 1 A flowchart of a method for determining human gait parameters provided in an embodiment of the present disclosure, Figure 2 A detailed process diagram of the method for determining human gait parameters provided by the embodiment of the present disclosure, such as Figure 1 and Figure 2 As shown, the method comprises the following steps:

[0073] Step 101, obtaining motion data of a human body during walking through a sensor, the motion data including the value of three-dimensional acceleration and the value of three-dimensional angular velocity, and obtaining a numerical curve of a three-dimensional acceleration norm and a numerical curve of a three-dimensional angular velocity norm according to the value of the three-dimensional acceleration and the value of the three-dimensional angular velocity, respectively.

[0074] First, an IMU sensor is worn on the human foot and the person walks on the pressure blanket. The IMU sensor continuously records sampling data at a sampling frequency of 100 Hz per second.

[0075] Specifically, the three-dimensional acceleration is measured by the accelerometer, and the three-dimensional angular velocity is measured by the gyroscope.

[0076] Step 102: Determine a first threshold of a three-dimensional acceleration norm and a second threshold of a three-dimensional angular velocity norm according to the motion data.

[0077] In one embodiment, determining a first threshold of a three-dimensional acceleration norm and a second threshold of a three-dimensional angular velocity norm according to the motion data includes:

[0078] The norm of the three-dimensional acceleration and the norm of the three-dimensional angular velocity are calculated according to the following formula (1) and formula (2) respectively:

[0079]

[0080] Among them, t k represents the kth time point in the numerical curve, is the norm of the three-dimensional acceleration, is the three-dimensional acceleration, is the norm of the three-dimensional angular velocity, is the three-dimensional angular velocity;

[0081] The first threshold and the second threshold are calculated according to the following formula (3):

[0082]

[0083] in, represents the first threshold or the second threshold. When i=a, represents the first threshold, when i=w, represents the second threshold; W i represents the weight, ranging from 0 to 1, where when i=a, W i The weight corresponding to the three-dimensional acceleration, when i = w, W i The weight corresponding to the three-dimensional angular velocity; T - represents the sum of the number of three-dimensional accelerations less than the first threshold, or the sum of the number of three-dimensional angular velocities less than the second threshold, T + It represents the sum of the number of three-dimensional accelerations greater than the first threshold, or the sum of the number of three-dimensional angular velocities greater than the second threshold.

[0084] It can be understood that when i = a, y(t k ) (i) This is the norm of the three-dimensional acceleration in the above formula (1). When i = w, y(t k ) (i) This is the norm of the three-dimensional angular velocity in the above formula (2).

[0085] Figure 3 is the gait phase segmentation result curve. Among them, the data curve of the three-dimensional acceleration norm and the data curve of the three-dimensional angular velocity norm are as follows Figure 3 When the human body is in a stationary state, the data curve of the three-dimensional acceleration norm and the data curve of the three-dimensional angular velocity norm are relatively flat. When the human body is in a walking state, the data curve of the three-dimensional acceleration norm and the data curve of the three-dimensional angular velocity norm are relatively fluctuating.

[0086] In one embodiment, when the first threshold and the second threshold are calculated using the above formula (3), the first threshold and the second threshold are obtained by multiple iterations. Specifically, for example, taking the calculation of the first threshold as an example, in the first iteration, the three-dimensional acceleration norm can be divided into two halves according to the size, and then substituted into the formula (3) to calculate a first threshold, and then T is recalculated according to the obtained first threshold. - and T +, and then substitute the retrieved data into formula (3) again, and obtain a first threshold value again, and repeat the iterative calculation until the first threshold value tends to be stable, so as to determine the first threshold value. It can be understood that the calculation method of the second threshold value is the same as that of the first threshold value, which will not be repeated here.

[0087] Step 103 : segment the gait phase according to the first threshold and the second threshold, and segment the gait phase into a stationary phase and a non-stationary phase.

[0088] In one embodiment, segmenting the gait phase according to the first threshold and the second threshold, and segmenting the gait phase into a stationary phase and a non-stationary phase, includes:

[0089] The numerical curve of the three-dimensional acceleration norm is segmented according to the first threshold value to obtain multiple time periods.

[0090] The numerical curve of the three-dimensional angular velocity norm is segmented according to the second threshold to obtain multiple time periods; wherein, within the same time period, when the norm of the three-dimensional acceleration is less than the first threshold and the norm of the three-dimensional angular velocity is less than the second threshold, it is determined that the gait phase is in a stationary phase; when the norm of the three-dimensional acceleration is greater than the first threshold and / or when the norm of the three-dimensional angular velocity is greater than the second threshold, it is determined that the gait phase is in a non-stationary phase.

[0091] Each walking cycle contains a series of typical posture changes, which are usually divided into a series of time periods, called gait phases.

[0092] like Figure 2 As shown, zero rate detection includes three steps: gait phase segmentation, peak width threshold judgment and gait phase re-segmentation. Gait phase segmentation is performed first.

[0093] For details, see Figure 3 It can be seen that the numerical curve of the three-dimensional acceleration norm is relatively flat in some time periods, and the curve fluctuates greatly in some time periods. Therefore, by segmenting the numerical curve of the three-dimensional acceleration norm according to the first threshold, multiple time periods can be obtained. Similarly, by segmenting the numerical curve of the three-dimensional angular velocity norm according to the second threshold, multiple time periods can also be obtained. The time periods of the three-dimensional acceleration and the three-dimensional angular velocity are mostly overlapped, but there may also be non-overlapping times.

[0094] When judging whether the gait phase of a certain time period is in a stationary phase or a non-stationary phase, the gait phase in the time period is in a stationary phase only when the three-dimensional acceleration norm in the time period is less than a first threshold value and the three-dimensional angular velocity norm is less than a second threshold value; otherwise, as long as one of the three-dimensional acceleration norm and the three-dimensional angular velocity norm is greater than the threshold value, the gait phase in the time period is in a non-stationary phase.

[0095] Step 104, determine whether to re-segment the gait phase. If the preset peak value of the three-dimensional acceleration norm and / or the preset peak value of the three-dimensional angular velocity norm correspond to a time period of a non-stationary phase, the gait phase is not re-segmented. If the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm both correspond to a time period of a stationary phase, the gait phase is re-segmented.

[0096] like Figure 2 As shown, before the gait phase is re-segmented, the peak width threshold is determined.

[0097] Specifically, before determining whether to re-segment the gait phase, the method further includes:

[0098] Determine whether a peak value in a numerical curve of the three-dimensional acceleration norm and a peak value in a numerical curve of the three-dimensional angular velocity norm are preset peak values; if within the same time period, the sum of the width of the peak value of the three-dimensional acceleration norm and the width of the peak value of the three-dimensional angular velocity norm is greater than a third threshold, then the peak value of the three-dimensional acceleration norm is the preset peak value of the three-dimensional acceleration norm, and the peak value of the three-dimensional angular velocity norm is the preset peak value of the three-dimensional angular velocity norm, wherein the width of the peak value is the sum of the rise time and the fall time of the peak.

[0099] The third threshold is a peak width threshold (PWT), that is, the peak value of the three-dimensional acceleration norm and the peak value of the three-dimensional angular velocity norm are the minimum thresholds of the preset peak value. The preset peak value is screened by the minimum threshold. Specifically, it can be obtained by the following formula (8):

[0100] minpw j,m =τ (8)

[0101] Where ow represents the width of the peak, j is the jth step among all steps, m represents the mth peak in the jth step, and τ represents the third threshold.

[0102] In one embodiment, the third threshold is greater than or equal to 30 ms and less than or equal to 110 ms. In a specific embodiment, for example, it is 50 ms, 80 ms, or 100 ms.

[0103] Next, determine whether to re-segment the gait phase. Figure 3As shown in the figure, if the first gait phase segmentation result has the situation circled in the figure, some preset peak values ​​of the three-dimensional acceleration norm and the preset peak values ​​of the three-dimensional angular velocity norm correspond to the time period of the static phase, indicating that there is an error in the first gait phase segmentation, then the wrong gait phase is re-segmented. Based on the priority selection of the closest distance, the wrong gait phase is divided into the same gait phase that is closest to it. For example, a certain time period should be a non-static phase, but it is divided into a static phase in the first segmentation. In the second segmentation, the gait phase in the time period is divided into the non-static phase that is closest to it.

[0104] In the present disclosure, the gait phase is re-segmented based on the third threshold value, which increases the accuracy of the gait phase segmentation, thereby improving the accuracy of the gait parameters finally obtained.

[0105] Step 105, obtaining the bipedal walking trajectory of the human body during walking.

[0106] In one embodiment, the motion data is in a carrier coordinate system, which is the coordinate system of the sensor itself;

[0107] Obtain the bipedal walking trajectory of a human body, including:

[0108] The forward direction of the human body is the X-axis direction, the right side of the human body is the Y-axis direction, and the Z-axis direction perpendicular to the XY plane is downward to form a reference coordinate system;

[0109] Determine the rotation matrix according to the vector in the X-axis direction, the vector in the Y-axis direction and the vector in the Z-axis direction;

[0110] According to the rotation matrix, determine the initial heading angle;

[0111] According to the initial heading angle, the carrier coordinate system is converted into a reference coordinate system;

[0112] Repeat the above steps for at least one iteration to determine the trajectory of bipedal walking.

[0113] See also Figure 2 The IMU three-dimensional acceleration in the carrier coordinate system is converted and the harmful acceleration (gravitational acceleration) is removed to obtain the pedestrian motion three-dimensional acceleration in the reference coordinate system. The carrier velocity can be obtained after integration. The carrier position parameters, that is, the trajectory of bipedal walking, can be solved by velocity integration. Among them, the velocity is corrected by zero rate update, and the position is corrected by the center of mass method.

[0114] For details, see Figure 2, the fusion of multiple sensor data in the same coordinate system can ensure the validity of multiple sensor fusion data, however, the directions of the three-dimensional acceleration and three-dimensional angular velocity data of the IMU sensor are based on the coordinate system of the IMU itself (carrier coordinate system), so a unified coordinate system is required. The reference coordinate system selected in the embodiment of the present disclosure is: the forward direction of human walking is the X-axis direction, the right side of the human body is the Y-axis direction, and the vertical direction downward to the XY plane is the Z-axis direction, and the right-hand rule is followed. Then, the Kalman filter is used to compensate the error of the speed in the time period when the human body is in a stationary state. In this step, the division of the stationary phase and the non-stationary phase in the gait phase segmentation result is also needed to correctly judge whether it is in a stationary state. Among them, the Kalman filter is an algorithm that uses the linear system state equation to optimally estimate the system state through the system input and output observation data. Since the observation data includes the influence of noise and interference in the system, the optimal estimate can also be regarded as a filtering process. After the speed is corrected, the centroid method is used to compensate the position error of the bipedal distance constraint to obtain the trajectory of bipedal walking. After the position is corrected, the vectors in the X-axis direction, the Y-axis direction, and the Z-axis direction are obtained during the human body's walking process. The vectors in the X-axis direction, the Y-axis direction, and the Z-axis direction are obtained according to the following formulas (9) to (12):

[0115] x X =pos end -pos start (9)

[0116]

[0117] x Y =x Z × X (12)

[0118] Among them, x X is the vector in the direction of the X axis, x Y is the vector in the Y-axis direction, x z is the vector in the Z-axis direction, pos end is the direction vector of the end position, pos start is the direction vector of the starting position, and a is the three-dimensional acceleration at rest.

[0119] Next, a rotation matrix is ​​obtained according to the vector in the X-axis direction, the vector in the Y-axis direction, and the vector in the Z-axis direction. The rotation matrix is ​​shown in the following formula (13):

[0120] R seg =(x X x Y x Z ) (13)

[0121] Among them, R seg Represents a rotation matrix.

[0122] Next, the initial heading angle is obtained according to the rotation matrix. The initial heading angle is obtained according to the following formula (14) and formula (15):

[0123] θ init,l =tan -1 (R seg (2,1), R seg (1,1)) (14)

[0124] θ=θ init,1 +θ init,l (15)

[0125] Where θ is the initial heading angle, l is the number of iterations, the number of iterations is greater than or equal to 1 and less than or equal to 6, θ init,1 is the initial heading angle after the first iteration, θ init,l is the initial heading angle after the lth iteration.

[0126] like Figure 2 As shown, when the number of iterations is less than or equal to 6 times, or θ is greater than 0.005, after one iteration, the initial posture matrix is ​​obtained, and the initial posture matrix is ​​used to describe the initial orientation information of the human body. According to the initial posture matrix, the human body posture is updated, and then according to the updated posture, the acceleration coordinate conversion is once again converted to the posture update for cyclic correction until θ is less than 0.005 and the number of iterations exceeds 6 times. After θ is less than 0.005 and the number of iterations exceeds 6 times, the final bipedal walking trajectory is obtained. The bipedal walking trajectory is the three-dimensional spatial coordinate in the reference coordinate system.

[0127] During walking, the initial carrier coordinate system positions of the two inertial sensors placed on the insteps of the left and right feet of the human body cannot be guaranteed to be completely consistent, which will cause the following two problems: one is the sensor placement error, and the other is the misalignment error caused by the subject's inability to keep the two feet in the same direction before walking, resulting in the positions of the left and right feet in different coordinate systems, which increases the error of the fused data. The present disclosure uses the relative angle of the sensor X-axis direction at the end point and the starting point of walking as the initial heading angle, and proposes a multiple iteration method to convert the carrier coordinate system dependent on the initial posture into a reference coordinate system, so that the X-axis direction of the converted coordinate system is infinitely close to the forward direction of walking.

[0128] Step 106, determining gait parameters according to the segmentation results of the gait phases and the trajectory of bipedal walking.

[0129] According to the segmentation results of the gait phase and the trajectory of bipedal walking, the gait parameters are determined, including:

[0130] Gait parameters include stride length, step length, pace, and step frequency. Stride length refers to the distance from when one side’s heel touches the ground to when the heel touches the ground again. Stride length refers to the distance between the front heel and the back heel during walking. Step speed refers to the ratio of stride length to walking cycle. Step frequency refers to the number of steps per minute.

[0131] Among them, the stride, step length, pace and step frequency are obtained according to the following formulas (4) to (7):

[0132]

[0133] Among them, SL m represents the stride, m represents the number of steps, X and Y represent the coordinate values ​​of the heel in the X-axis direction and the Y-axis direction respectively;

[0134]

[0135] in, represents the step length. When i represents the right foot, i′ represents the left foot. When i represents the left foot, i′ represents the right foot.

[0136]

[0137] Among them, SV m Indicates the pace, T stride,m represents a walking cycle, which represents the time from the heel of one side touching the ground to the heel of the same side touching the ground again; the walking cycle can be obtained from the time point in the segmentation result of the gait phase mentioned above;

[0138]

[0139] Among them, C m Indicates cadence.

[0140] Table 1 below shows the numerical values ​​of gait parameters obtained by the method for determining human gait parameters provided by an embodiment of the present disclosure.

[0141] Table 1

[0142]

[0143] Among them, μ is the mean error, SD is the standard deviation, MAE is the mean absolute error, RMSE is the root mean square error, MAPE is the mean absolute percentage error, R 2is the coefficient of determination.

[0144] Figures 4 to 7 They are the result graphs of stride, stride length, stride speed and stride frequency, where: Figures 4 to 7 The two figures on the left are the results obtained by the solution of the present disclosure, and the two figures on the right are the results obtained by the prior art. Figures 4 to 7 It can be seen from the comparison of the results that the gait parameters obtained by the solution disclosed in the present invention are more accurate.

[0145] The present disclosure also provides a device for determining human gait parameters. Figure 8 A schematic diagram of the structure of a device for determining human gait parameters provided by an embodiment of the present disclosure, such as Figure 8 As shown, the device comprises:

[0146] A first acquisition unit 81 is used to acquire motion data of a human body during walking through a sensor, wherein the motion data includes a value of three-dimensional acceleration and a value of three-dimensional angular velocity, and obtain a numerical curve of a three-dimensional acceleration norm and a numerical curve of a three-dimensional angular velocity norm according to the values ​​of the three-dimensional acceleration and the values ​​of the three-dimensional angular velocity respectively;

[0147] A first determining unit 82, configured to determine a first threshold of a three-dimensional acceleration norm and a second threshold of a three-dimensional angular velocity norm according to the motion data;

[0148] A segmentation unit 83, configured to segment the gait phase according to the first threshold and the second threshold, and segment the gait phase into a stationary phase and a non-stationary phase;

[0149] A judging unit 84 is used to judge whether to re-segment the gait phase. If the preset peak value of the three-dimensional acceleration norm and / or the preset peak value of the three-dimensional angular velocity norm correspond to the time period of the non-stationary phase, the gait phase is not re-segmented. If the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm both correspond to the time period of the stationary phase, the gait phase is re-segmented.

[0150] A second acquisition unit 85 is used to acquire a bipedal walking trajectory of a human body during walking;

[0151] The second determining unit 86 is used to determine gait parameters according to the segmentation result of the gait phase and the trajectory of the bipedal walking.

[0152] In one embodiment, the first determination unit 82 is specifically configured to calculate the norm of the three-dimensional acceleration and the norm of the three-dimensional angular velocity according to the following formula (1) and formula (2), respectively:

[0153]

[0154] Among them, t k represents the kth time point in the numerical curve, is the norm of the three-dimensional acceleration, is the three-dimensional acceleration, is the norm of the three-dimensional angular velocity, is the three-dimensional angular velocity;

[0155] The first threshold and the second threshold are calculated according to the following formula (3):

[0156]

[0157] in, represents the first threshold or the second threshold. When i=a, represents the first threshold, when i=w, represents the second threshold; W i represents the weight, ranging from 0 to 1, where when i=a, W i The weight corresponding to the three-dimensional acceleration, when i = w, W i The weight corresponding to the three-dimensional angular velocity; T - represents the sum of the number of three-dimensional accelerations less than the first threshold, or the sum of the number of three-dimensional angular velocities less than the second threshold, T + It represents the sum of the number of three-dimensional accelerations greater than the first threshold, or the sum of the number of three-dimensional angular velocities greater than the second threshold.

[0158] In one embodiment, the segmentation unit 83 includes:

[0159] A first segmentation unit is used to segment the numerical curve of the three-dimensional acceleration norm according to a first threshold value to obtain multiple time periods;

[0160] The second segmentation unit is used to segment the numerical curve of the three-dimensional angular velocity norm according to the second threshold value to obtain multiple time periods; wherein, within the same time period, when the norm of the three-dimensional acceleration is less than the first threshold value, and the norm of the three-dimensional angular velocity is less than the second threshold value, it is determined that the gait phase is in a stationary phase; when the norm of the three-dimensional acceleration is greater than the first threshold value, and / or when the norm of the three-dimensional angular velocity is greater than the second threshold value, it is determined that the gait phase is in a non-stationary phase.

[0161] In one embodiment, the device also includes: a first judgment unit, used to judge whether the peak value in the numerical curve of the three-dimensional acceleration norm and the peak value in the numerical curve of the three-dimensional angular velocity norm are preset peak values. If within the same time period, the sum of the width of the peak value of the three-dimensional acceleration norm and the width of the peak value of the three-dimensional angular velocity norm is greater than a third threshold, then the peak value of the three-dimensional acceleration norm is the preset peak value of the three-dimensional acceleration norm, and the peak value of the three-dimensional angular velocity norm is the preset peak value of the three-dimensional angular velocity norm, wherein the width of the peak value is the sum of the rise time and fall time of the peak.

[0162] The third threshold is greater than or equal to 30 ms and less than or equal to 110 ms.

[0163] In one embodiment, the motion data is in a carrier coordinate system, which is the coordinate system of the sensor itself;

[0164] The second acquisition unit 85 includes:

[0165] A forming unit is used to form a reference coordinate system with the forward direction of the human body as the X-axis direction, the right side of the human body as the Y-axis direction, and the vertical direction downward from the XY plane as the Z-axis direction;

[0166] A third determining unit determines a rotation matrix according to the vector in the X-axis direction, the vector in the Y-axis direction, and the vector in the Z-axis direction;

[0167] A fourth determining unit determines an initial heading angle according to the rotation matrix;

[0168] a fifth determining unit, which converts the carrier coordinate system into a reference coordinate system according to the initial heading angle;

[0169] The sixth determination unit is used to repeat the above steps and determine the trajectory of bipedal walking after at least one iteration.

[0170] In one embodiment, the gait parameters include stride, step length, pace, and step frequency, wherein the stride represents the distance from when the heel of one side touches the ground to when the heel of the same side touches the ground again, the step length represents the distance between the front heel and the rear heel during walking, the pace represents the ratio of the stride to the walking cycle, and the step frequency represents the number of steps walked per minute;

[0171] Among them, the stride, step length, pace and step frequency are obtained according to the following formulas (4) to (7):

[0172]

[0173] Among them, SL m represents the stride, m represents the number of steps, X and Y represent the coordinate values ​​of the heel in the X-axis direction and the Y-axis direction respectively;

[0174]

[0175] in, represents the step length. When i represents the right foot, i′ represents the left foot. When i represents the left foot, i′ represents the right foot.

[0176]

[0177] Among them, SV m Indicates the pace, T stride,m The walking cycle is the time from the heel of one side touching the ground to the heel of the same side touching the ground again.

[0178]

[0179] Among them, C m Indicates cadence.

[0180] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0181] Fig. 9 A schematic block diagram of an example electronic device 900 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0182] like Fig. 9 As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0183] A number of components in the device 900 are connected to the I / O interface 905, including: an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0184] The computing unit 901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 901 performs the various methods and processes described above, such as a human gait parameter determination method. For example, in some embodiments, the human gait parameter determination method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 900 via ROM 902 and / or a communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the human gait parameter determination method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the human gait parameter determination method in any other appropriate manner (e.g., by means of firmware).

[0185] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0186] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0187] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0188] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0189] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0190] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0191] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0192] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0193] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present disclosure, which should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for determining human gait parameters, characterized in that: The method comprises: Acquiring motion data of a human body during walking through a sensor, wherein the motion data includes a value of three-dimensional acceleration and a value of three-dimensional angular velocity, and obtaining a numerical curve of a three-dimensional acceleration norm and a numerical curve of a three-dimensional angular velocity norm according to the values ​​of the three-dimensional acceleration and the values ​​of the three-dimensional angular velocity respectively; Determining, according to the motion data, a first threshold value of the three-dimensional acceleration norm and a second threshold value of the three-dimensional angular velocity norm; Segmenting the gait phase according to the first threshold and the second threshold, and segmenting the gait phase into a stationary phase and a non-stationary phase; Determine whether a peak value in the numerical curve of the three-dimensional acceleration norm and a peak value in the numerical curve of the three-dimensional angular velocity norm are preset peak values; if within the same time period, the sum of the width of the peak value of the three-dimensional acceleration norm and the width of the peak value of the three-dimensional angular velocity norm is greater than a third threshold, then the peak value of the three-dimensional acceleration norm is the preset peak value of the three-dimensional acceleration norm, and the peak value of the three-dimensional angular velocity norm is the preset peak value of the three-dimensional angular velocity norm, wherein the width of the peak value is the sum of the rise time and the fall time of the peak; Determining whether to re-segment the gait phase, if the preset peak value of the three-dimensional acceleration norm and / or the preset peak value of the three-dimensional angular velocity norm correspond to a time period of a non-stationary phase, then the gait phase is not re-segmented, and if the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm both correspond to a time period of a stationary phase, then the gait phase is re-segmented; Obtain the bipedal walking trajectory of a human body during walking; The gait parameters are determined according to the segmentation results of the gait phases and the trajectory of the bipedal walking.

2. The method according to claim 1, characterized in that The step of determining, according to the motion data, a first threshold of the three-dimensional acceleration norm and a second threshold of the three-dimensional angular velocity norm comprises: The norm of the three-dimensional acceleration and the norm of the three-dimensional angular velocity are calculated according to the following formula (1) and formula (2) respectively: Among them, t k represents the kth time point in the numerical curve, is the norm of the three-dimensional acceleration, is the three-dimensional acceleration, is the norm of the three-dimensional angular velocity, is the three-dimensional angular velocity; The first threshold and the second threshold are calculated according to the following formula (3): in, represents the first threshold or the second threshold. When i=a, represents the first threshold, when i=w, represents the second threshold; W i represents the weight, ranging from 0 to 1, where when i=a, W i The weight corresponding to the three-dimensional acceleration, when i = w, W i The weight corresponding to the three-dimensional angular velocity; T - represents the sum of the number of three-dimensional accelerations less than the first threshold, or the sum of the number of three-dimensional angular velocities less than the second threshold, T + It represents the sum of the number of three-dimensional accelerations greater than the first threshold, or the sum of the number of three-dimensional angular velocities greater than the second threshold.

3. The method according to claim 1, characterized in that: The step of segmenting the gait phase according to the first threshold and the second threshold, and segmenting the gait phase into a stationary phase and a non-stationary phase, comprises: Segmenting the numerical curve of the three-dimensional acceleration norm according to the first threshold value to obtain a plurality of time periods; The numerical curve of the three-dimensional angular velocity norm is segmented according to the second threshold to obtain multiple time periods; wherein, within the same time period, when the norm of the three-dimensional acceleration is less than the first threshold and the norm of the three-dimensional angular velocity is less than the second threshold, it is determined that the gait phase is in a stationary phase; when the norm of the three-dimensional acceleration is greater than the first threshold and / or when the norm of the three-dimensional angular velocity is greater than the second threshold, it is determined that the gait phase is in a non-stationary phase.

4. The method according to claim 1, characterized in that The third threshold is greater than or equal to 30 ms and less than or equal to 110 ms.

5. The method according to claim 1, characterized in that The motion data is in a carrier coordinate system, which is the coordinate system of the sensor itself; The step of obtaining the bipedal walking trajectory of a human body during walking comprises: The forward direction of the human body is the X-axis direction, the right side of the human body is the Y-axis direction, and the Z-axis direction perpendicular to the XY plane is downward to form a reference coordinate system; Determine the rotation matrix according to the vector in the X-axis direction, the vector in the Y-axis direction and the vector in the Z-axis direction; Determining an initial heading angle according to the rotation matrix; According to the initial heading angle, the carrier coordinate system is converted into a reference coordinate system; Repeat the above steps, and after at least one iteration, determine the trajectory of the bipedal walking.

6. The method according to claim 1, characterized in that Determining the gait parameters according to the segmentation result of the gait phase and the trajectory of the bipedal walking includes: The gait parameters include stride, step length, pace, and step frequency. The stride represents the distance from when one side's heel touches the ground to when the heel touches the ground again. The step length represents the distance between the front heel and the rear heel during walking. The pace represents the ratio of the stride to the walking cycle. The step frequency represents the number of steps per minute. Among them, the stride, step length, pace and step frequency are obtained according to the following formulas (4) to (7): Among them, SL m represents the stride, m represents the number of steps, X and Y represent the coordinate values ​​of the heel in the X-axis direction and the Y-axis direction respectively; in, represents the step length. When i represents the right foot, i′ represents the left foot. When i represents the left foot, i′ represents the right foot. Among them, SV m Indicates the pace, T stride,m The walking cycle is the time from the heel of one side touching the ground to the heel of the same side touching the ground again. Among them, C m Indicates cadence.

7. A device for determining human gait parameters, characterized in that: The device comprises: a first acquisition unit, configured to acquire motion data of a human body during walking through a sensor, wherein the motion data includes a value of three-dimensional acceleration and a value of three-dimensional angular velocity, and obtain a numerical curve of a three-dimensional acceleration norm and a numerical curve of a three-dimensional angular velocity norm according to the values ​​of the three-dimensional acceleration and the values ​​of the three-dimensional angular velocity, respectively; A first determining unit, configured to determine a first threshold of the three-dimensional acceleration norm and a second threshold of the three-dimensional angular velocity norm according to the motion data; A segmentation unit, configured to segment the gait phase according to the first threshold and the second threshold, and segment the gait phase into a stationary phase and a non-stationary phase; A first judgment unit is used to judge whether a peak value in the numerical curve of the three-dimensional acceleration norm and a peak value in the numerical curve of the three-dimensional angular velocity norm are preset peak values, if within the same time period, the sum of the width of the peak value of the three-dimensional acceleration norm and the width of the peak value of the three-dimensional angular velocity norm is greater than a third threshold value, then the peak value of the three-dimensional acceleration norm is the preset peak value of the three-dimensional acceleration norm, and the peak value of the three-dimensional angular velocity norm is the preset peak value of the three-dimensional angular velocity norm, wherein the width of the peak value is the sum of the rise time and the fall time of the peak; a judgment unit, used to judge whether to re-segment the gait phase, if the preset peak value of the three-dimensional acceleration norm and / or the preset peak value of the three-dimensional angular velocity norm corresponds to a time period of a non-stationary phase, then the gait phase is not re-segmented, and if the preset peak value of the three-dimensional acceleration norm and the preset peak value of the three-dimensional angular velocity norm both correspond to a time period of a stationary phase, then the gait phase is re-segmented; The second acquisition unit is used to acquire the bipedal walking trajectory of the human body during walking; The second determining unit is used to determine the gait parameters according to the segmentation result of the gait phase and the trajectory of the bipedal walking.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-6.

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