Self-adaptive zero-speed detection algorithm based on interval search under variable-speed motion condition

Abstract

The invention relates to a self-adaptive zero-speed detection algorithm based on interval search under variable-speed motion conditions, and belongs to the technical field of pedestrian navigation. Comprising the following steps: 1) analyzing a zero-speed interval proportion; 2) gait cycle extraction; 3) preliminary screening of zero-speed intervals; and 4) finely determining a zero-speed interval. According to the method, SWA filtering processing is carried out on the mode of the angular speed, the search interval of the gait cycle is determined, a zero-speed interval is preliminarily determined according to the statistical characteristics of the zero-speed interval in the gait cycle at different speeds, a new threshold value is set based on the 3 sigma criterion, and self-adaptive zero-speed detection is achieved. The invention not only provides an efficient and accurate new positioning scheme for the fields of wearable equipment and location services, but also provides valuable reference for pedestrian navigation research in a complex motion mode.

Description

Technical Field

[0001] The invention relates to an adaptive zero-speed detection algorithm based on interval search under variable-speed motion conditions, belonging to the technical field of pedestrian navigation. Background Art

[0002] In the current technical field, indoor pedestrian positioning and navigation technologies are mainly divided into two categories: infrastructure-dependent methods and infrastructure-independent methods. Although infrastructure-dependent methods, such as radio frequency identification (RFID), Wi-Fi, Bluetooth, and ultra wide band (UWB) technology, can provide high positioning accuracy, their high installation and maintenance costs greatly limit the widespread application of such technologies. In contrast, infrastructure-independent methods, especially the Pedestrian Inertial Navigation System (PINS), have shown great application potential in many fields such as fire rescue and disaster relief due to their autonomy, flexibility, and immunity to weather and electromagnetic interference.

[0003] With the continuous advancement of micro-electromechanical system inertial measurement unit (IMU) technology, its advantages of low cost and high autonomy have further promoted the research and development of PINS. However, pedestrian navigation systems based on miniature inertial measurement units (MIMU) face a major problem, namely the accumulated errors over time, which seriously affects the positioning accuracy. In order to effectively deal with this problem and achieve continuous and accurate positioning in unknown environments, the zero velocity update (ZUPT) technology based on foot MIMU came into being. ZUPT technology detects the zero velocity interval (ZVI) of foot movement and uses the velocity error as the observation value of the Kalman filter to estimate and compensate the error state of the pedestrian inertial navigation system, thereby significantly improving the positioning accuracy.

[0004] Traditional ZUPT algorithms mainly rely on threshold-based accelerometer or gyroscope measurements for zero velocity detection, such as acceleration movement variance detectors, acceleration amplitude detectors, and angular rate energy detectors. Although these methods can achieve zero velocity detection to a certain extent, they are difficult to adapt to a variety of motion patterns and are highly sensitive to the setting of thresholds. In order to optimize and improve the ZUPT algorithm, researchers have proposed methods based on adaptive thresholds, such as weighted average detectors that consider sensor prior performance, methods that model zero velocity thresholds based on functions of interval time and foot impact level, and zero velocity detectors based on adaptive simulated energy consumption curves. Although these methods have improved the accuracy of zero velocity detection to a certain extent, their accuracy is still limited by the accuracy of threshold modeling. In recent years, with the development of artificial intelligence technology, deep learning methods have been applied to intelligent detection of ZVI and have achieved remarkable results. However, deep learning methods require a large amount of training data to continuously train models, which not only consumes huge human and computing resources, but also has poor generalization ability of the algorithm. For new situations that are not within the scope of training data, the stability and reliability of its classification will be significantly reduced. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned prior art, the present invention provides an adaptive zero-speed detection algorithm based on interval search under variable speed motion conditions. The algorithm determines the search interval of the gait cycle by performing SWA filtering on the modulus of the angular velocity, and preliminarily determines a zero-speed interval based on the statistical characteristics of the zero-speed interval in the gait cycle under different speeds. Subsequently, a new threshold is set based on the 3σ criterion to achieve adaptive zero-speed detection. The present invention not only provides a new efficient and accurate positioning solution for the field of wearable devices and location services, but also provides a valuable reference for the study of pedestrian navigation under complex motion modes.

[0006] The adaptive zero-speed detection algorithm based on interval search under variable speed motion conditions of the present invention is special in that it includes the following steps: Step 1) Analysis of the proportion of zero-speed intervals Through experimental data analysis, the proportion of zero-speed intervals at different walking speeds is determined, and the minimum proportion of walking zero-speed intervals is set accordingly, which provides a basis for the subsequent preliminary screening of zero-speed intervals and enhances the robustness of the algorithm. Step 2) Gait cycle extraction The angular velocity is synthesized and filtered, and the maximum points of the gait cycle are identified by taking the first-order and second-order derivatives. Representative maximum points are screened out based on the intervals and characteristics of the maximum points, thereby determining the complete gait cycle. Step 3) Preliminary screening of zero speed interval A mathematical model for zero-speed detection was constructed, and the detection statistics were calculated using the generalized likelihood ratio detection method. Combined with the duration of each gait cycle and the minimum proportion of the zero-speed interval set in step 1, the time range of some zero-speed intervals was preliminarily determined, providing a basis for subsequent fine-grained determination. Step 4) Fine determination of zero speed range Within the preliminarily determined zero-speed interval, the mean and standard deviation of the detection data are calculated, the threshold of the gait cycle is adaptively adjusted, and the zero-speed interval and non-zero-speed interval are judged according to the new threshold to achieve precise determination of the zero-speed interval.

[0007] Preferably, the specific steps of step 1 are: First, an experiment was conducted to analyze the zero-speed interval at different walking speeds. The analysis showed that the proportion of zero-speed intervals decreases with increasing speed. The daily walking speed of pedestrians does not exceed 8 km / h. Within this speed range, the minimum zero-speed interval proportion is as low as 13.58%, which is the lowest value among all test speeds. A robust minimum proportion of zero-speed intervals is determined. , that is, the minimum proportion of the walking zero speed interval is set to 10%.

[0008] Preferably, the specific steps of step 2 are: To identify a complete gait cycle, the angular velocity is first synthesized to obtain : ; Where: , , MIMU in The angular velocity components of the three axes are measured at all times. for The modulus of the angular velocity at the moment, and the SWA filtering of the modulus of the angular velocity: ; Where: is the window size of SWA, which is set to half of the IMU output frequency, is the angular velocity modulus obtained after SWA filtering. The first and second order derivatives are calculated, and the following equation is established: ; Find n maximum points, recorded as ,in Indicates The time corresponding to the maximum point, in order to screen out the representative maximum point, the following formula can be used to determine and eliminate the pseudo maximum point : ; in, and Used to represent the first two retained maximum points. If the formula is satisfied, Add to new collection If it is not satisfied, it means that the two maximum points are close to each other. and The corresponding values ​​are compared, if , then Value Assignment ,if ,but remain unchanged, and The time between 1 and 2 is a complete gait cycle. Preferably, the specific steps of step 3 are: The mathematical model of zero speed detection is regarded as a binary hypothesis testing problem, which is expressed as: ; Where: express The hypothesis detection statistic of the pedestrian gait at time represents the threshold value, The calculation methods used include acceleration amplitude detection, acceleration variance detection, angular velocity amplitude detection, angular velocity energy detection and generalized likelihood ratio detection. Based on the generalized likelihood ratio detection method, first, define the first Subsampling , including 3-axis acceleration and 3-axis angular rate: ; Is The data samples are a data set consisting of sliding windows: ; Always Using the generalized likelihood ratio test method, we get the test statistic : ; Where: express The generalized likelihood ratio test statistic at time , Set the sliding window to 30, and is the variance of acceleration and angular rate noise, is the average acceleration of each axis in the current window, is the acceleration due to gravity In order to improve the accuracy of zero-speed detection, based on the generalized likelihood ratio detection algorithm and combined with the duration of each gait cycle, the accurate conservative zero-speed interval time range is preliminarily determined. : ; In this range The size is used as a sliding window, and the of Perform the calculation: ; Traverse to find The minimum value of Value is ,but It is a preliminarily determined zero-speed interval.

[0009] Preferably, the specific steps of step 4) are: Calculate the average value of GLR data within the initially determined zero speed range and standard deviation : ; ; By analyzing the data characteristics of the zero-speed interval initially determined in each gait cycle, the threshold of the gait cycle is adaptively adjusted. : ; Then, the zero speed interval and non-zero speed interval are determined according to the formula: ; Where N=1 represents the zero speed interval, and N=0 represents the non-zero speed interval.

[0010] The adaptive zero-speed detection algorithm based on interval search under variable speed motion conditions of the present invention has the following beneficial effects: (1) Traditional methods use fixed thresholds, which are difficult to adapt to changes in different walking speeds and complex motion patterns. The present invention dynamically adjusts the threshold based on the 3σ criterion through interval search and statistical characteristic analysis, thereby adapting to the zero-speed detection requirements under various walking speeds.

[0011] (2) Compared with the zero-speed detection algorithm based on deep learning, this algorithm does not require a large amount of training data, reducing the consumption of manpower and computing resources.

[0012] Through experiments, it is verified that the interval search adaptive threshold zero-speed detection algorithm proposed in the present invention not only effectively solves the fixed threshold dependence problem in the existing algorithm, but also achieves a significant improvement in navigation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of the present invention; Figure 2 This is a comparison chart of the proportion of zero-speed intervals at different walking speeds, where (a), (b), (c), and (d) are the proportion of zero-speed intervals at walking speeds of 8km / h, 6km / h, 4km / h, and 2km / h, respectively; Figure 3 It is a comparison chart of the original and filtered angular velocity modulus values; Figure 4 It is the angular velocity maximum point determination diagram after filtering; Figure 5 It is a schematic diagram of the low threshold selection situation; Figure 6 It is a low threshold local magnification image; Figure 7 It is a schematic diagram of the high threshold selection situation; Figure 8 It is a high threshold local magnification image; Fig. 9 It is a comparison diagram of the present invention and the fixed threshold method for complex motion paths; Fig.10 1 is a motion solution comparison diagram, wherein (a) is a comparison of the position error solved by the present invention and the fixed threshold method, (b) is a comparison of the average position error solved by the present invention and the fixed threshold method, and (c) is a comparison of the standard deviation solved by the present invention and the fixed threshold method; Fig.11 It is a comparison diagram of the present invention and the fixed threshold method in a long-distance experimental motion path. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] Embodiment 1: This embodiment is an adaptive zero speed detection algorithm based on interval search under variable speed motion conditions. The flowchart is shown in the attached Figure 1 , including the following steps: Step 1) Analysis of the proportion of zero-speed intervals Through experimental data analysis, such as Figure 2 As shown, (a), (b), (c), and (d) are the proportions of zero-speed intervals at walking speeds of 8km / h, 6km / h, 4km / h, and 2km / h, respectively, and the minimum proportion of zero-speed intervals for walking is set at 10% accordingly.

[0016] Step 2) Gait cycle extraction The angular velocity is synthesized and filtered, such as Figure 3 As shown in , the maximum points of the gait cycle are identified by finding the first-order and second-order derivatives. According to the interval and characteristics of the maximum points, representative maximum points are selected, such as Figure 4 As shown, the complete gait cycle is determined.

[0017] Step 3) Preliminary screening of zero speed interval A mathematical model for zero-speed detection was constructed, and the detection statistics were calculated using the generalized likelihood ratio detection method. Combined with the duration of each gait cycle and the set minimum proportion of the walking zero-speed interval of 10%, the time range of some zero-speed intervals was preliminarily determined, providing a basis for subsequent fine-tuning.

[0018] Step 4) Fine determination of zero speed range In the initially determined zero-speed interval, the average value and standard deviation of the detection data are calculated, and the threshold of the gait cycle is set adaptively. The zero-speed interval and the non-zero-speed interval are judged according to the new threshold, so as to achieve the precise determination of the zero-speed interval.

[0019] The specific steps of step 1 are: First, an experiment was conducted to analyze the zero-speed interval at different walking speeds. The analysis showed that the proportion of zero-speed intervals decreases with increasing speed. The daily walking speed of pedestrians does not exceed 8 km / h. Within this speed range, the minimum zero-speed interval proportion is as low as 13.58%, which is the lowest value among all test speeds. A robust minimum proportion of zero-speed intervals is determined. , that is, the minimum proportion of the walking zero speed interval is set to 10%.

[0020] The specific steps of step 2 are: To identify a complete gait cycle, the angular velocity is first synthesized to obtain : ; Where: , , MIMU in The angular velocity components of the three axes are measured at all times. for The modulus of the angular velocity at the moment, and the SWA filtering of the modulus of the angular velocity: ; Where: is the window size of SWA, which is set to half of the IMU output frequency, is the angular velocity modulus obtained after SWA filtering. The first and second order derivatives are calculated, and the following equation is established: ; Find n maximum points, recorded as ,in Indicates The time corresponding to the maximum point, in order to screen out the representative maximum point, the following formula can be used to determine and eliminate the pseudo maximum point : ; in, and Used to represent the first two retained maximum points. If the formula is satisfied, Add to new collection If it is not satisfied, it means that the two maximum points are close to each other. and The corresponding values ​​are compared, if , then Value Assignment ,if ,but remain unchanged, and The time between 1 and 2 is a complete gait cycle. The specific steps of step 3 are: The mathematical model of zero speed detection is regarded as a binary hypothesis testing problem, which is expressed as: ; Where: express The hypothesis detection statistic of the pedestrian gait at time represents the threshold value, The calculation methods used include acceleration amplitude detection, acceleration variance detection, angular velocity amplitude detection, angular velocity energy detection and generalized likelihood ratio detection. Based on the generalized likelihood ratio detection method, first, define the first Subsampling , including 3-axis acceleration and 3-axis angular rate: ; Is The data samples are a data set consisting of sliding windows: ; Always Using the generalized likelihood ratio test method, we get the test statistic : ; Where: express The generalized likelihood ratio test statistic at time , Set the sliding window to 30, and is the variance of acceleration and angular rate noise, is the average acceleration of each axis in the current window, is the acceleration due to gravity In order to improve the accuracy of zero-speed detection, based on the generalized likelihood ratio detection algorithm and combined with the duration of each gait cycle, the accurate conservative zero-speed interval time range is preliminarily determined. : ; In this range The size is used as a sliding window, and the of Perform the calculation: ; Traverse to find The minimum value of Value is ,but It is a preliminarily determined zero-speed interval.

[0021] The specific steps of step 4) are: Calculate the average value of GLR data within the initially determined zero speed range and standard deviation : ; ; By analyzing the data characteristics of the zero-speed interval initially determined in each gait cycle, the threshold of the gait cycle is adaptively adjusted. : ; Then, the zero speed interval and non-zero speed interval are determined according to the formula: ; Where N=1 represents the zero speed interval, and N=0 represents the non-zero speed interval.

[0022] Embodiment 2: (1) Verification of the effectiveness of the short-distance complex variable speed motion algorithm.

[0023] This invention simulates walking on a short-distance complex road. The experimental subjects travel along a 100-meter concave path, experience 6 turns and adjust the speed irregularly. Position error is used as a key indicator to measure walking accuracy and algorithm performance. In the process of calculating navigation error, the choice of reference point must be clarified first. and its corresponding point in the actual trajectory , through the position error To directly reflect the difference between the solved trajectory and the actual trajectory: ; To comprehensively evaluate the navigation performance over the entire trajectory, the average values ​​of these position errors were also calculated. . Provides an intuitive indicator to measure the overall deviation between the solved trajectory and the actual trajectory, that is, the average level of navigation error: ; in, Indicates the turning points and end points passed in sequence during the walking process. express The total number of this experiment is 7.

[0024] In addition, in order to reflect the fluctuation range of the position error, that is, the stability of the difference between the calculated trajectory and the actual trajectory, the standard deviation of the position error needs to be calculated. : ; By using and The two indicators can be used to quantitatively compare the solution trajectory deviations of different algorithms, and then evaluate the difference in navigation accuracy between Algorithm 1 (the algorithm of the present invention) and Algorithm 2 (the traditional fixed threshold algorithm).

[0025] Before the comparison, considering the randomness of walking speed, in order to make Algorithm 2 work best, the threshold traversal screening method is used, such as Figure 5-Figure 8 As shown in the figure, a fixed threshold of 40000 suitable for the speed of this experiment was finally determined. This threshold can not only stably identify the zero-speed interval, but also effectively reduce misjudgment. The selection of this threshold ensures the stability of the algorithm when dealing with the randomness of walking speed.

[0026] like Fig. 9 As shown in Table 1, when the experimental starting point is the same, Algorithm 1 is closer to the real trajectory than Algorithm 2. Specifically, as shown in Table 1, when comparing the performance of Algorithm 1 and Algorithm 2, an analysis is performed based on the distance between each reference point and its corresponding point, the average position error, and the standard deviation. Fig.10 As shown in (a), the distance value of algorithm 1 is relatively stable, most of which are between 0.5 and 1.4, and the difference between its maximum and minimum distance error values ​​is small, indicating that the error distribution of the algorithm at each reference point is relatively uniform. In contrast, the distance error value of algorithm 2 fluctuates greatly, especially the distance values ​​of the last three reference points are obviously higher, indicating that its error at certain specific points is large and unstable. Further analysis of the average position error, such as Fig.10As shown in (b), the error of Algorithm 1 is 0.93 meters, which is 0.21 meters lower than that of Algorithm 2 (1.14 meters), and the reduction percentage is about 18.53%, which highlights the advantage of Algorithm 1 in positioning accuracy. Fig.10 As shown in (c), the standard deviation of Algorithm 1 is 0.37 meters, slightly lower than 0.39 meters of Algorithm 2, and the reduction percentage is about 6.0%, indicating that Algorithm 1 performs more stably at different reference points. In summary, Algorithm 1 outperforms Algorithm 2 in terms of average position error and standard deviation, demonstrating the significant advantage of Algorithm 1 in navigation accuracy.

[0027] Table 1 Position error statistics (unit: m)

[0028] (2) Verification of the effectiveness of the long-distance variable speed motion algorithm.

[0029] In order to verify the effectiveness of the algorithm in long-distance movement, the experimental design uses a 400-meter standard runway, with the innermost line of the runway as the precise reference trajectory. The experimenter walks along the white inner line, and the speed changes randomly throughout the process to test the performance of the algorithm when the speed changes. In the algorithm application, three thresholds of Algorithm 2 were selected: 50000, 60000 and 70000, and the inertial sensor data were processed using different thresholds of Algorithm 1 and Algorithm 2. As shown in Table 2 and Fig.11 As shown in Figure 2, Algorithm 1 is closer to the actual trajectory than Algorithm 2.

[0030] Table 2 Relative position error of the end point calculated by two algorithms In view of the problem that the traditional zero-speed detection algorithm relies on a fixed threshold, the present invention proposes an interval search adaptive threshold algorithm. The algorithm determines the search interval through SWA filtering, preliminarily determines the zero-speed interval based on statistical characteristics, and then sets a new threshold based on the 3σ criterion to achieve adaptive detection. After verification by short-distance complex variable speed motion experiments, by comparing Algorithm 1 with Algorithm 2, the results show that the average position error of Algorithm 1 is reduced by about 18.53%, reaching a low level of 0.93 meters, which is significantly improved compared to 1.14 meters of Algorithm 2. In addition, the standard deviation of Algorithm 1 is also slightly lower, indicating that its results are more consistent and reliable under different test conditions. In the 400-meter walking experiment, the relative error of Algorithm 1 is only 0.72%, which is 42.74% lower than that of Algorithm 2 at the optimal threshold of 1.24%. This experimental result proves that the proposed algorithm has significantly improved the positioning accuracy. In summary, the interval search adaptive threshold zero-speed detection algorithm proposed in this paper not only effectively solves the problem of fixed threshold dependence in the existing algorithm, but also achieves a significant improvement in navigation accuracy. This research result not only provides new technical ideas for wearable devices, location services and pedestrian navigation, but also lays a solid foundation for subsequent related research and practice.

[0031] The above calculation examples of the present invention are only used to explain the calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. An adaptive zero speed detection algorithm based on interval search under variable speed motion conditions, characterized in that The following steps are involved: Step 1) Analysis of the proportion of zero-speed intervals Through experimental data analysis, the proportion of zero-speed intervals under different walking speeds is determined, and the minimum proportion of walking zero-speed intervals is set accordingly, which provides a basis for the subsequent preliminary screening of zero-speed intervals and enhances the robustness of the algorithm. Step 2) Gait cycle extraction The angular velocity is synthesized and filtered, and the maximum points of the gait cycle are identified by taking the first-order and second-order derivatives. Representative maximum points are screened out based on the intervals and characteristics of the maximum points, thereby determining the complete gait cycle. Step 3) Preliminary screening of zero speed interval A mathematical model for zero-speed detection was constructed, and the detection statistics were calculated using the generalized likelihood ratio detection method. Combined with the duration of each gait cycle and the minimum proportion of the zero-speed interval set in step 1, the time range of some zero-speed intervals was preliminarily determined, providing a basis for subsequent fine-grained determination. Step 4) Fine determination of zero speed range Within the preliminarily determined zero-speed interval, the mean and standard deviation of the detection data are calculated, the threshold of the gait cycle is adaptively adjusted, and the zero-speed interval and non-zero-speed interval are judged according to the new threshold to achieve precise determination of the zero-speed interval.

2. The adaptive zero speed detection algorithm based on interval search under variable speed motion conditions according to claim 1 is characterized in that The specific steps of step 1 are: First, an experiment was conducted to analyze the zero-speed interval at different walking speeds. The analysis showed that the proportion of zero-speed intervals decreases with increasing speed. The daily walking speed of pedestrians does not exceed 8 km / h. Within this speed range, the minimum zero-speed interval proportion is as low as 13.58%, which is the lowest value among all test speeds. A robust minimum proportion of zero-speed intervals is determined. , that is, the minimum proportion of the walking zero speed interval is set to 10%.

3. The adaptive zero speed detection algorithm based on interval search under variable speed motion conditions according to claim 1 is characterized in that The specific steps of step 2 are: To identify a complete gait cycle, the angular velocity is first synthesized to obtain : ； Where: , , MIMU in The angular velocity components of the three axes are measured at all times. for The modulus of the angular velocity at the moment, and the SWA filtering of the modulus of the angular velocity: ； Where: is the window size of SWA, which is set to half of the IMU output frequency, is the angular velocity modulus obtained after SWA filtering, and the first and second order derivatives are calculated to establish the following equation: ； Find n maximum points, recorded as ,in Indicates The time corresponding to the maximum point, in order to screen out the representative maximum point, the following formula can be used to determine and eliminate the pseudo maximum point : ； in, and Used to represent the first two retained maximum points. If the formula is satisfied, Add to new collection If it is not satisfied, it means that the two maximum points are close to each other. and The corresponding values ​​are compared, if , then Value Assignment ,if ,but remain unchanged, and A complete gait cycle is between 1 and 2.

4. The adaptive zero speed detection algorithm based on interval search under variable speed motion conditions according to claim 1 is characterized in that The specific steps of step 3 are: The mathematical model of zero speed detection is regarded as a binary hypothesis testing problem, which is expressed as: ； Where: express The hypothesis detection statistic of the pedestrian gait at time represents the threshold value, The calculation methods used include acceleration amplitude detection, acceleration variance detection, angular velocity amplitude detection, angular velocity energy detection and generalized likelihood ratio detection. Based on the generalized likelihood ratio detection method, first, define the first Subsampling , including 3-axis acceleration and 3-axis angular rate: ； Is The data samples are a data set consisting of sliding windows: ； Always Using the generalized likelihood ratio test method, we get the test statistic : ； Where: express The generalized likelihood ratio test statistic at time , Set the sliding window to 30, and is the variance of the acceleration and angular rate noise, is the average acceleration of each axis in the current window, is the acceleration due to gravity In order to improve the accuracy of zero-speed detection, based on the generalized likelihood ratio detection algorithm, combined with the duration of each gait cycle, the accurate conservative zero-speed interval time range is preliminarily determined. : ； In this range The size is used as a sliding window, and the of Perform the calculation: ； Traverse to find The minimum value of Value is ,but It is a preliminarily determined zero-speed interval.

5. The adaptive zero speed detection algorithm based on interval search under variable speed motion conditions according to claim 1 is characterized in that The specific steps of step 4) are: Calculate the average value of GLR data within the initially determined zero speed range and standard deviation : ； ； By analyzing the data characteristics of the zero-speed interval initially determined in each gait cycle, the threshold of the gait cycle is adaptively adjusted. : ； Then, the zero speed interval and non-zero speed interval are determined according to the formula: ； Where N=1 represents the zero speed interval, and N=0 represents the non-zero speed interval.

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