Foot self-positioning method and device adapting to different exercise intensities and medium

Abstract

The invention discloses a foot self-positioning method and device adapting to different exercise intensities and a medium, and relates to the technical field of indoor positioning. The method comprises the following steps: carrying out inertial solution on data of an accelerometer and a gyroscope to obtain attitude, speed and position information of a target; intercepting data by using a sliding time window, and carrying out smooth pseudo-Wigner-Ville distribution transformation to obtain a spectral characteristic diagram; distinguishing the motion state of the target; determining a stride frequency result according to the motion state of the target; judging the current state of the target according to the established self-adaptive zero-speed detection threshold value; when the current state of the target is the static state, if the speed of the target is not zero, taking the speed of the target as the speed error of the static state; taking the speed error in the static state as an observed quantity, and correcting the position information of the target by using extended Kalman filtering to obtain a zero-speed detection result; and calculating the course of the target by taking one step as a gait cycle. According to the invention, the problem of misjudgment in a gait zero-speed interval detection method is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor positioning, and more particularly, to a foot-worn autonomous positioning method, device and medium adapted to different exercise intensities. Background Art

[0002] In outdoor positioning, the Global Positioning System has good positioning effects. However, in indoor environments with many obstacles, satellite signals will experience strong attenuation, resulting in a sharp decline in satellite positioning effects. In these blind areas, the satellite signal strength is weak and the availability is poor. Existing pedestrian indoor positioning and navigation technologies are mainly divided into two categories. One is infrastructure-based methods that require pre-installation or rely on positioning network devices such as WIFI signals, Bluetooth, and infrared rays. Although these signals have advantages such as wide coverage, long-term operation, and low cost, due to the susceptibility of electromagnetic waves to environmental influence and interference, the positioning effects relying solely on these signals are not good. The second category is positioning methods that do not rely on infrastructure, such as visual positioning and inertial positioning. Visual methods are affected by light, image texture, etc., and have high computational complexity and are not suitable for wearable positioning.

[0003] Indoor positioning based on IMU (Inertial Measurement Unit) is an autonomous positioning method. However, due to the low output accuracy of MEMS (Micro Electro Mechanical System)-IMU. In addition, during gait movement, the walking frequency of pedestrians cannot be maintained at a single fixed value. For different walking frequencies, there are also obvious differences in error drift. If a fixed threshold is set, it will affect the recognition of gait movements of different intensities, and thus affect the positioning effect. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a foot-worn autonomous positioning method, device and medium adapted to different exercise intensities to solve the problem of misjudgment in traditional gait zero-velocity interval detection methods. The present invention is not affected by external electromagnetic signals, does not require external infrastructure and preliminary survey work, and can identify five different walking frequency movements of stationary, normal walking, fast walking, normal running, and full-speed running, which are represented by 0-4 in sequence. Adaptive thresholds are set based on different walking frequencies, and then the precise division of the stationary state and the swinging state during gait movement is completed, reducing the integration error of the inertial system.

[0005] In a first aspect, the present invention provides a foot-worn autonomous positioning method adapted to different exercise intensities, the method comprising:

[0006] Obtain accelerometer and gyro data, and perform inertial solution to obtain the attitude, velocity, and position information of the target; among them, the accelerometer and gyro data are collected by a MEMS-IMU sensor installed at the foot position of the target;

[0007] Use a sliding time window to intercept data of size W for the gyroscope pitch axis, perform smooth pseudo-Wigner-Ville distribution transformation, and obtain a spectrogram;

[0008] According to the spectrogram, judge the sensor signal variance and signal energy value by setting a threshold to distinguish the motion state of the target;

[0009] Determine the step frequency result according to the motion state of the target;

[0010] According to the step frequency result, establish an adaptive zero-speed detection threshold, and judge the current state of the target according to the adaptive zero-speed detection threshold, and the current state of the target is a static state or a swinging state;

[0011] When the current state of the target is a static state, if the speed of the target is not zero, then use the speed of the target as the speed error in the static state;

[0012] Use the speed error in the static state as the observation quantity, and use the extended Kalman filter to correct the position information of the target to obtain a zero-speed detection result;

[0013] Based on the zero-speed detection result, calculate the heading of the target with one step as a gait cycle.

[0014] Furthermore, obtaining accelerometer and gyro data and performing inertial solution to obtain the attitude, velocity, and position information of the target includes:

[0015] According to the accelerometer and gyro data, perform inertial solution through formula (1) to obtain the attitude, velocity, and position information of the target:

[0016]

[0017] In the formula, the superscripts n and b represent the navigation coordinate system and the vehicle coordinate system respectively; q t and q t-1 are the quaternions at time t and t-1; θ t is the angle matrix obtained by integrating the sensor angular velocity; e is the natural constant; is the attitude matrix; is the acceleration value; P n is the velocity and position information; g n represents the earth gravity field vector; K ap is the integration matrix;

[0018] Integrate the acceleration values to obtain velocity and position information, expressed as:

[0019]

[0020] In the formula, Δt represents the time step;

[0021] Formula (1) involves the conversion between quaternion and attitude matrix, and the conversion relationship is as follows:

[0022]

[0023] In the formula, q0, q1, q2, and q3 are the elements of the quaternion.

[0024] Furthermore, use a sliding time window to intercept data of size W on the pitch axis of the gyroscope, and perform a smoothed pseudo-Wigner-Ville distribution transformation. The calculation process of obtaining the spectral characteristic diagram is expressed as:

[0025]

[0026] In the formula, N is the number of discrete Fourier transform points, x gyro_y is the angular velocity of the pitch axis intercepted by the sliding time window, t is the selected moment, m is the moment in the sliding window, and the value range of m is w is the size of the sliding window, e is the natural constant, k is the position representing the frequency component, x gyro_y * is the complex conjugate of the pitch angular velocity, is the frequency variable, is the spectral characteristic diagram.

[0027] Furthermore, according to the spectral characteristic diagram, judge the variance and signal energy value of the sensor signal by setting thresholds to distinguish the motion state of the target, including:

[0028] Judge the variance and signal energy value of the sensor signal through the following formula (5):

[0029]

[0030] In the formula, Γ ene is the set interval signal energy threshold, Γ var is the set interval signal variance threshold, vel t is the resultant acceleration at time t, is the mean value of the resultant acceleration in the interval;

[0031] If the variance and signal energy value of the sensor signal satisfy formula (5), then determine that the motion state of the target is normal running or full-speed running. If the variance and signal energy value of the sensor signal do not satisfy formula (5), then determine that the motion state of the target is stationary, normal walking, or fast walking.

[0032] Further, according to the motion state of the target, the step frequency result is determined by the following formula (6):

[0033]

[0034] In the formula, f is the frequency value of the target motion state, is the frequency corresponding to the maximum value taken by the amplitude-frequency, classification t is the motion state of the target, where classification t = 3, 4 indicates that the motion state of the target is normal running and full-speed running, classification t = 0, 1, 2 indicates that the motion state of the target is static, normal walking, and fast walking.

[0035] Further, taking the velocity error in the static state as the observation quantity, the extended Kalman filter is used to correct the position information of the target. The error state vector in the extended Kalman filter is expressed as:

[0036]

[0037] In the formula, represents the error state vector, and respectively represent the three-dimensional position error vector, three-dimensional velocity error vector, three-dimensional attitude error vector, three-dimensional gyro drift, and three-dimensional acceleration bias.

[0038] Further, based on the zero-speed detection result, taking one step as a gait cycle, the heading of the target is calculated by the following formula:

[0039] yaw m = -atan2(C1, C2) (8)

[0040] In the formula, yaw m represents the heading of the target, atan2 represents the arctangent function with a value range of [-pi, pi], and C1 and C2 are the elements in the attitude matrix corresponding to the sampling moment when switching from the static state to the swing state and

[0041] In the second aspect, the present invention provides a foot-worn autonomous positioning device adapted to different exercise intensities. The device includes:

[0042] An inertial solution unit, configured to obtain accelerometer and gyro data, and perform inertial solution to obtain the attitude, velocity, and position information of the target; wherein, the accelerometer and gyro data are collected by an MEMS-IMU sensor installed at the foot position of the target;

[0043] A spectral characteristic calculation unit, configured to intercept data of size W of the gyroscope pitch axis using a sliding time window, perform a smoothed pseudo-Wigner-Ville distribution transformation to obtain a spectral characteristic map;

[0044] A state discrimination unit, configured to, according to the spectral characteristic map, judge the variance and signal energy value of the sensor signal by setting thresholds to distinguish the motion state of the target;

[0045] A step frequency calculation unit, configured to determine the step frequency result according to the motion state of the target;

[0046] A state judgment unit, configured to establish an adaptive zero-speed detection threshold according to the step frequency result, and judge the current state of the target according to the adaptive zero-speed detection threshold, where the current state of the target is a stationary state or a swinging state;

[0047] An error determination unit, configured to, when the current state of the target is a stationary state, if the velocity of the target is not zero, use the velocity of the target as the velocity error in the stationary state;

[0048] A zero-speed detection unit, configured to use the velocity error in the stationary state as an observable quantity, and correct the position information of the target using an extended Kalman filter to obtain a zero-speed detection result;

[0049] A heading calculation unit, configured to calculate the heading of the target based on the zero-speed detection result, with one step as a gait cycle.

[0050] Furthermore, the inertial solution unit is further configured to:

[0051] Perform inertial solution according to the accelerometer and gyro data through formula (1) to obtain the attitude, velocity, and position information of the target:

[0052]

[0053] In the formula, the superscripts n and b respectively represent the navigation coordinate system and the carrier coordinate system; q t and q t-1 are the quaternions at time t and t-1; θ t is the angle matrix obtained by integrating the sensor angular velocity; e is the natural constant; is the attitude matrix; is the acceleration value; P n is the velocity and position information; gn represents the Earth's gravity field vector; K ap is an integration matrix;

[0054] Integrating the acceleration values to obtain velocity and position information, expressed as:

[0055]

[0056] where Δt represents the time step;

[0057] Formula (1) involves the conversion between quaternions and attitude matrices, and the conversion relationship is as follows:

[0058]

[0059] where q0, q1, q2, and q3 are the elements of the quaternion.

[0060] In a third aspect, the present invention provides a readable storage medium storing one or more programs, which can be executed by one or more processors to implement the method as described above.

[0061] The present invention has at least the following beneficial effects:

[0062] The autonomous positioning method in the present invention has autonomy, is not affected by external electromagnetic signals, and does not require any additional hardware deployment and preliminary survey work, etc. It can accurately identify the zero-velocity interval under various gait patterns and is suitable for estimating the displacement heading of pedestrians indoors. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 Shows the overall block diagram of a system for implementing a foot-worn autonomous positioning method adapted to different exercise intensities according to an embodiment of the present invention.

[0064] Figure 2 Shows the flowchart of a foot-worn autonomous positioning method adapted to different exercise intensities according to an embodiment of the present invention.

[0065] Figure 3 Shows the structural diagram of a foot-worn autonomous positioning device adapted to different exercise intensities according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific examples, but it is not a limitation to the present invention. For the steps described herein, if there is no necessity for the front-back relationship between them, the order in which they are described as examples herein should not be regarded as a limitation. Those skilled in the art should know that they can be adjusted in order as long as the logic between them is not destroyed and the entire process cannot be realized.

[0067] An embodiment of the present invention provides a foot-mounted autonomous positioning method adapted to different exercise intensities. The purpose of this method is to provide accurate heading and displacement information for indoor targets and the like under the conditions of not relying on external infrastructure and being unaffected by electromagnetic interference and object occlusion. It should be noted that the "target" described herein refers to a target that can move, including but not limited to pedestrians, animals, and objects driven by machinery (such as robots).

[0068] During implementation, the MEMS-IMU sensor is installed at the user's heel position. First, the accelerations and gyroscopes of the MEMS-IMU sensor are output, and strapdown inertial solution is performed under the traditional IEZ (Inertial Navigation System-Extended Kalman Filter-Zero Velocity Update, INS-EKF-ZUPT) framework. Secondly, the zero-velocity interval detection model established based on the pedestrian step frequency can correct the traditional IEZ framework. The system block diagram of the foot-mounted autonomous positioning method adapted to different exercise intensities is as Figure 1 shown.

[0069] The flow of the foot-mounted autonomous positioning method adapted to different exercise intensities is as Figure 2 shown, and is implemented through the following steps S10 to S90.

[0070] S10: Obtain the accelerometer and gyro data, and perform inertial solution to obtain the attitude, velocity, and position information of the target; wherein, the accelerometer and gyro data are collected by the MEMS-IMU sensor installed at the target's foot position.

[0071] In this embodiment, the accelerometer and gyro data of the MEMS-IMU sensor are collected, and strapdown inertial solution is performed in combination with Equation (1) to obtain the attitude, velocity, and position information of the pedestrian.

[0072]

[0073] In the formula, the superscripts n and b respectively represent the navigation coordinate system and the vehicle coordinate system; q t and q t-1are the quaternions at time t and t-1; θ t is the angle matrix obtained by integrating the sensor angular velocity; e is the natural constant; is the attitude matrix; is the acceleration value; P n is the velocity and position information; g n represents the Earth's gravity field vector; K ap is the integration matrix.

[0074] Integrate the acceleration value to obtain the velocity and position information, which is defined as follows:

[0075]

[0076] In the formula, Δt represents the time step.

[0077] Among them, equation (1) involves the conversion between the quaternion and the attitude matrix, and the conversion relationship is as follows:

[0078]

[0079] In the formula, g0, q1, q2, and q3 are the elements of the quaternion.

[0080] S20: Use a sliding time window to intercept data of size W of the gyroscope pitch axis, perform a smoothed pseudo-Wigner-Ville distribution transformation, and obtain a spectrogram.

[0081] In this embodiment, based on the output of the IMU, use a sliding time window to intercept data of size W of the gyroscope pitch axis (non-overlapping with the previous window), perform an SPWVD (Smoothed Pseudo Wigner-Ville Distribution) transformation, thereby obtaining a spectrogram. The calculation process is expressed as:

[0082]

[0083] In the formula, N is the number of discrete Fourier transform points, x gyro_y is the pitch axis angular velocity intercepted by the sliding time window, t is the selected moment, m is the moment in the sliding window, and the value range of m is w is the size of the sliding window, e is the natural constant, k is the position representing the frequency component, x gyro_y * is the complex conjugate of the pitch angular velocity, is the frequency variable, is the spectrogram.

[0084] S30: According to the spectrogram, judge the variance and signal energy value of the sensor signal by setting a threshold to distinguish the motion state of the target.

[0085] In this embodiment, based on the spectral characteristic results of step S20, when the pedestrian's movement speed is normal running or full-speed running, the frequency corresponding to the maximum value of the amplitude-frequency is the step frequency. When the pedestrian's movement speed is stationary, normal walking, or fast walking, the frequency corresponding to the maximum value of the amplitude-frequency is twice the step frequency, that is, the double frequency. By setting thresholds to judge the variance and energy value of the sensor signal, a rough distinction is made between them.

[0086]

[0087] In the formula, Γ ene is the set interval signal energy threshold, Γ var is the set interval signal variance threshold, vel t is the resultant acceleration at time t, is the mean value of the resultant acceleration in the interval.

[0088] S40: Determine the step frequency result according to the motion state of the target.

[0089] In this embodiment, based on the results of step S30, if the conditions of formula (5) are met, it can be indicated that the current state is normal running or full-speed running. Otherwise, it is considered that the current state is stationary, normal walking, or fast walking, and the following step frequency results can be obtained.

[0090]

[0091] In the formula, f is the frequency value of the target motion state, is the frequency corresponding to the maximum value of the amplitude-frequency, classification t is the motion state of the target, where classification t = 3, 4 indicates that the motion state of the target is normal running or full-speed running, classification t = 0, 1, 2 indicates that the motion state of the target is stationary, normal walking, or fast walking.

[0092] S50: Establish an adaptive zero-speed detection threshold according to the step frequency result, and judge the current state of the target according to the adaptive zero-speed detection threshold. The current state of the target is a stationary state or a swinging state.

[0093] Based on the step frequency result in step S40, an adaptive zero-speed detection threshold is established.

[0094] Threshold = k × f max + b#(7)

[0095] In the formula, Threshold is the zero-speed detection threshold; f maxis the maximum frequency obtained from the step frequency data; k and b are parameters to be determined and can be adjusted according to experimental data.

[0096] When the result meets the above conditions, it indicates that the current state is the static state; otherwise, it is the swinging state.

[0097] S60: When the current state of the target is the static state, if the speed of the target is not zero, then use the speed of the target as the speed error in the static state.

[0098] In this embodiment, based on the ZUPT detection result of step S50, when the pedestrian's motion state is the static state, the actual walking speed should be zero, while the speed calculated based on step S10 is not zero, and the difference between the two is the speed error in the static state.

[0099] S70: Use the speed error in the static state as the observation quantity, and use the extended Kalman filter to correct the position information of the target to obtain the zero-speed detection result.

[0100] Take the speed error in step S60 as the observation quantity, and use EKF (Extended Kalman Filter) to correct the positioning result obtained in step (2). The 15-dimensional error state vector in EKF is defined as follows:

[0101]

[0102] In the formula, represents the error state vector, and respectively represent the three-dimensional position error vector, three-dimensional speed error vector, three-dimensional attitude error vector, three-dimensional gyro drift, and three-dimensional acceleration bias.

[0103] S80: Based on the zero-speed detection result, take one step as a gait cycle, and calculate the heading of the target.

[0104] According to the zero-speed detection result obtained in the above step S70, taking one step as a gait cycle, in the m-th gait cycle, the heading of the pedestrian is denoted as yaw m .

[0105] yaw m =-atan2(C1, C2) (8)

[0106] In the formula, yaw m represents the heading of the target, atan2 represents the arctangent function with a value range of [-pi, pi], and C1 and C2 are the elements in the attitude matrix corresponding to the sampling moment when switching from the static state to the swinging state and

[0107] S90: Determine whether to end according to whether the MEMS-IMU continues to collect data. If so, end the operation; if not, return to step S10.

[0108] An embodiment of the present invention also provides a foot-mounted autonomous positioning device adapted to different exercise intensities, such as Figure 3 shown. The device includes:

[0109] An inertial solution unit 301, configured to obtain accelerometer and gyro data, and perform inertial solution to obtain the attitude, velocity, and position information of the target; wherein, the accelerometer and gyro data are collected by an MEMS-IMU sensor installed at the target foot position;

[0110] A spectral characteristic calculation unit 302, configured to intercept data of size W of the gyroscope pitch axis data using a sliding time window, perform a smoothed pseudo-Wigner-Ville distribution transform, and obtain a spectral characteristic map;

[0111] A state discrimination unit 303, configured to distinguish the motion state of the target by setting thresholds to judge the sensor signal variance and signal energy value according to the spectral characteristic map;

[0112] A step frequency calculation unit 304, configured to determine a step frequency result according to the motion state of the target;

[0113] A state judgment unit 305, configured to establish an adaptive zero-speed detection threshold according to the step frequency result, and judge the current state of the target according to the adaptive zero-speed detection threshold, where the current state of the target is a stationary state or a swinging state;

[0114] An error determination unit 306, configured to, when the current state of the target is a stationary state, if the speed of the target is not zero, use the speed of the target as the speed error in the stationary state;

[0115] A zero-speed detection unit 307, configured to use the speed error in the stationary state as an observable quantity, and correct the position information of the target using an extended Kalman filter to obtain a zero-speed detection result;

[0116] A heading calculation unit 308, configured to calculate the heading of the target based on the zero-speed detection result, with one step as a gait cycle.

[0117] In some embodiments, the inertial solution unit is further configured to:

[0118] Perform inertial solution according to the accelerometer and gyro data through formula (1) to obtain the attitude, velocity, and position information of the target:

[0119]

[0120] In the formula, the superscripts n and b respectively represent the navigation coordinate system and the vehicle coordinate system; q t and q t-1 are the quaternions at time t and t-1; θ t is the angle matrix obtained by integrating the angular velocity of the sensor; e is the natural constant; is the attitude matrix; is the acceleration value; P n is the velocity and position information; g n represents the earth's gravity field vector; K ap is the integration matrix;

[0121] Integrating the acceleration value to obtain the velocity and position information, which is expressed as:

[0122]

[0123] In the formula, Δt represents the time step;

[0124] Formula (1) involves the conversion between the quaternion and the attitude matrix, and the conversion relationship is as follows:

[0125]

[0126] In the formula, q0, q1, q2, and q3 are the elements of the quaternion

[0127] In some embodiments, the spectral characteristic calculation unit is further configured to intercept data with a size of W of the gyroscope pitch-axis data by using a sliding time window, perform a smoothed pseudo-Wigner-Ville distribution transform, and the calculation process of obtaining the spectral characteristic diagram is expressed as:

[0128]

[0129] In the formula, N is the number of discrete Fourier transform points, x gyrp_y is the pitch-axis angular velocity intercepted by the sliding time window, t is the selected moment, m is the moment in the sliding window, and the value range of m is w is the size of the sliding window, e is the natural constant, k is the position representing the frequency component, x gyro_y * is the complex conjugate of the pitch angular velocity, is the frequency variable, is the spectral characteristic diagram.

[0130] In some embodiments, the state discrimination unit is further configured to:

[0131] Judge the sensor signal variance and the signal energy value through the following formula (5):

[0132]

[0133] In the formula, Γ ene is the set interval signal energy threshold, and Γ var is the set interval signal variance threshold, and vel t is the combined acceleration at time t, is the mean value of the combined acceleration in the interval;

[0134] If the sensor signal variance and the signal energy value satisfy formula (5), then it is determined that the motion state of the target is normal running or full-speed running. If the sensor signal variance and the signal energy value do not satisfy formula (5), then it is determined that the motion state of the target is stationary, normal walking or fast walking.

[0135] In some embodiments, the stride frequency calculation unit is further configured to determine the stride frequency result according to the motion state of the target through the following formula (6):

[0136]

[0137] In the formula, f is the frequency value of the target motion state, is the frequency corresponding to the maximum value taken by the amplitude-frequency, and classification t is the motion state of the target, where classification t = 3, 4 indicates that the motion state of the target is normal running, full-speed running, and classification t = 0, 1, 2 indicates that the motion state of the target is stationary, normal walking, fast walking.

[0138] In some embodiments, the zero-degree detection unit is further configured to use the velocity error in the stationary state as the observation quantity, and correct the position information of the target by using the extended Kalman filter. The error state vector in the extended Kalman filter is expressed as:

[0139]

[0140] In the formula, represents the error state vector, and respectively represent the three-dimensional position error vector, the three-dimensional velocity error vector, the three-dimensional attitude error vector, the three-dimensional gyro drift and the three-dimensional acceleration bias.

[0141] In some embodiments, the heading calculation unit is further configured to calculate the heading of the target based on the zero-velocity detection result, with one step as a gait cycle, through the following formula:

[0142] yaw m = -atan2(C1, C2) (8)

[0143] wherein, yaw m represents the heading of the target, atan2 represents the arctangent function with a value range of [-pi, pi], and C1 and C2 are respectively the elements in the attitude matrix corresponding to the sampling moment when switching from the static state to the swinging state and

[0144] It should be noted that the structures of the foot-wearing autonomous positioning devices adapted to different exercise intensities described in this embodiment belong to the same inventive concept as the foot-wearing autonomous positioning method adapted to different exercise intensities described above, and achieve the same beneficial effects through the same principle, which will not be elaborated here

[0145] The embodiment of the present invention also provides a readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments

[0146] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention having equivalent elements, modifications, omissions, combinations (e.g., schemes of cross-combination of various embodiments), adaptations or changes. The elements in the claims will be broadly interpreted based on the language used in the claims and are not limited to the examples described in this specification or during the implementation of this application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered only as examples, and the true scope and spirit are indicated by the full scope of the following claims and their equivalents

[0147] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. In addition, in the above specific embodiments, various features can be grouped together to simplify the present invention. This should not be construed as an intention that the features of an invention not claimed are necessary for any claim. On the contrary, the subject matter of the present invention can be less than all the features of a specific embodiment of the invention. Thus, the following claims are incorporated herein as examples or embodiments into the specific embodiments, where each claim independently serves as a separate embodiment, and considering these embodiments, they can be combined with each other in various combinations or permutations. The scope of the present invention should be determined with reference to the appended claims and the full scope of the equivalent forms empowered by these claims

Claims

1. A foot self-positioning method adapted to different exercise intensities, characterized in that, The method includes: Obtaining accelerometer and gyro data, and performing inertial solution to obtain the attitude, velocity, and position information of the target; wherein, the accelerometer and gyro data are collected by an MEMS-IMU sensor installed at the foot position of the target; Using a sliding time window to intercept data of size W of the gyroscope pitch axis data, and performing a smoothed pseudo-Wigner-Ville distribution transform to obtain a spectral characteristic diagram; According to the spectral characteristic diagram, judging the sensor signal variance and signal energy value by setting a threshold to distinguish the motion state of the target; Determining the step frequency result according to the motion state of the target; According to the step frequency result, establishing an adaptive zero-speed detection threshold, and judging the current state of the target according to the adaptive zero-speed detection threshold, where the current state of the target is a static state or a swinging state; When the current state of the target is a static state, if the velocity of the target is not zero, then using the velocity of the target as the velocity error in the static state; Using the velocity error in the static state as an observation quantity, and using an extended Kalman filter to correct the position information of the target to obtain a zero-speed detection result; Based on the zero-speed detection result, taking one step as a gait cycle, and calculating the heading of the target.

2. The method for autonomous positioning of a foot device adapted to different exercise intensities according to claim 1, characterized in that, Obtaining accelerometer and gyro data, and performing inertial solution to obtain the attitude, velocity, and position information of the target, including: According to the accelerometer and gyro data, performing inertial solution through formula (1) to obtain the attitude, velocity, and position information of the target: Wherein, the superscripts n and b respectively represent the navigation coordinate system and the vehicle body coordinate system; q t and q t-1 are the quaternions at time t and time t-1; θ t is the angle matrix obtained by integrating the sensor angular velocity; e is the natural constant; is the attitude matrix; is the acceleration value; P n is the velocity and position information; g n represents the earth gravity field vector; K ap is the integration matrix; Integrating the acceleration value to obtain velocity and position information, expressed as: where Δt represents the time step; Formula (1) involves the conversion between quaternion and attitude matrix, and the conversion relationship is as follows: where q0, q1, q2, and q3 are the elements of the quaternion.

3. The foot-wear autonomous positioning method adapted to different exercise intensities according to claim 1, characterized in that The calculation process of using a sliding time window to intercept data of size W of the gyroscope pitch axis data and performing a smoothed pseudo-Wigner-Ville distribution transform to obtain a spectral characteristic diagram is expressed as: where N is the number of points of the discrete Fourier transform, x gyro_y is the pitch-axis angular velocity intercepted by the sliding time window, t is the selected moment, m is the moment in the sliding window, and the value range of m is w is the size of the sliding window, e is the natural constant, k is the position representing the frequency component, x gyro_y * is the complex conjugate of the pitch angular velocity, is the frequency variable, is the spectral characteristic diagram.

4. The foot autonomous positioning method for adapting to different exercise intensities according to claim 3, characterized in that According to the spectral characteristic diagram, judging the sensor signal variance and signal energy value by setting a threshold to distinguish the motion state of the target, including: Judging the sensor signal variance and signal energy value through the following formula (5): Where, Γ ene is the set interval signal energy threshold, Γ var is the set interval signal variance threshold, vel t is the combined acceleration at time t, is the mean value of the combined acceleration in the interval; If the sensor signal variance and signal energy value satisfy formula (5), then determining the motion state of the target as normal running or full-speed running; if the sensor signal variance and signal energy value do not satisfy formula (5), then determining the motion state of the target as static, normal walking, or fast walking.

5. The foot self-positioning method adapted to different exercise intensities according to claim 4, characterized in that According to the motion state of the target, determining the step frequency result through the following formula (6): where f is the frequency value of the target motion state, is the frequency corresponding to the maximum value taken by the amplitude-frequency, classification t is the motion state of the target, where classification t = 3, 4 indicates that the motion state of the target is normal running, full-speed running, classification t = 0, 1, 2 indicates that the motion state of the target is static, normal walking, fast walking.

6. The method for autonomous positioning of a footgear adapted to different exercise intensities according to claim 1, characterized in that, Using the velocity error in the static state as an observation quantity, and using an extended Kalman filter to correct the position information of the target. The error state vector in the extended Kalman filter is expressed as: wherein represents an error state vector and respectively represent a three-dimensional position error vector, a three-dimensional velocity error vector, a three-dimensional attitude error vector, a three-dimensional gyro drift, and a three-dimensional acceleration bias.

7. The method for autonomous foot positioning adapting to different exercise intensities according to claim 6, characterized in that, Based on the zero-speed detection result, taking one step as a gait cycle, and calculating the heading of the target through the following formula: yaw m = -atan2(C1, C2) (8) where yaw m represents the heading of the target, atan2 represents the arctangent function with a value range of [-pi, pi], and C1 and C2 are the elements in the attitude matrix corresponding to the sampling moment when switching from the static state to the swinging state and 8. A foot-wearing autonomous positioning device adapted to different exercise intensities, characterized in that, The device includes: An inertial solution unit configured to obtain accelerometer and gyro data, and perform inertial solution to obtain the attitude, velocity, and position information of the target; wherein, the accelerometer and gyro data are collected by an MEMS-IMU sensor installed at the foot position of the target; A spectrum characteristic calculation unit, configured to intercept data of size W of the gyroscope pitch axis data by using a sliding time window, perform a smoothed pseudo-Wigner-Ville distribution transformation, and obtain a spectrum characteristic diagram; A state discrimination unit, configured to, according to the spectrum characteristic diagram, judge the variance and signal energy value of the sensor signal by setting a threshold to discriminate the motion state of the target; A step frequency calculation unit, configured to determine a step frequency result according to the motion state of the target; A state judgment unit, configured to, according to the step frequency result, establish an adaptive zero-speed detection threshold, and judge the current state of the target according to the adaptive zero-speed detection threshold, where the current state of the target is a static state or a swinging state; An error determination unit, configured to, when the current state of the target is a static state, if the speed of the target is not zero, use the speed of the target as the speed error in the static state; A zero-speed detection unit, configured to use the speed error in the static state as an observation quantity, and correct the position information of the target by using an extended Kalman filter to obtain a zero-speed detection result; A heading calculation unit, configured to calculate the heading of the target based on the zero-speed detection result, with one step as a gait cycle; 9. The foot self-positioning device adapted to different exercise intensities according to claim 8, characterized in that, The inertial solution unit is further configured to: According to the accelerometer and gyro data, perform inertial solution through formula (1) to obtain the attitude, speed, and position information of the target: Wherein, superscripts n and b respectively represent the navigation coordinate system and the vehicle coordinate system; q t and q t-1 are the quaternions at time t and time t-1; θ t is the angle matrix obtained by integrating the sensor angular velocity; e is the natural constant; is the attitude matrix; is the acceleration value; P n is the velocity and position information; g n represents the earth gravity field vector; K ap is the integration matrix; Integrate the acceleration value to obtain the speed and position information, expressed as: where Δt represents the time step; Formula (1) involves the conversion between quaternions and attitude matrices, and the conversion relationship is as follows: where q0, q1, q2, and q3 are the elements of the quaternion; 10. A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, execute the method according to any one of claims 1 to 7.

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