WO2023108498A1

WO2023108498A1 - Zero-speed interval detection method, pedestrian navigation system and storage medium

Info

Publication number: WO2023108498A1
Application number: PCT/CN2021/138455
Authority: WO
Inventors: 孙方敏; 周攀; 李烨
Original assignee: 中国科学院深圳先进技术研究院
Priority date: 2021-12-15
Filing date: 2021-12-15
Publication date: 2023-06-22

Abstract

A zero-speed interval detection method, a pedestrian navigation system and a computer-readable medium. The zero-speed interval detection method comprises the steps of: acquiring data; determining an adaptive energy window and an action range; determining parameters of an angular rate energy difference detector; estimating an interval length; and adjusting the interval. The zero-speed interval detection method can reduce a zero-speed interval false detection rate.

Description

A zero-speed interval detection method, pedestrian navigation system and storage medium

technical field

The invention relates to the technical field of navigation, in particular to a detection method for a zero-speed interval, a pedestrian navigation system and a storage medium.

Background technique

In recent years, with the improvement of people's living standards, the demand for location-based services is getting higher and higher. The satellite navigation systems represented by China's Beidou and the US GPS are very mature. However, the satellite signal strength will be greatly attenuated in the occlusion environment, which cannot meet the positioning accuracy requirements or even realize the positioning function. In this context, other types of positioning technologies have been proposed one after another, among which the representative positioning technologies based on WiFi, Bluetooth, infrared rays and inertial sensors. The first three technologies all need to arrange a large number of signal transmitting/receiving devices in the environment in advance, and their equipment deployment and maintenance costs are proportional to the area of the working environment, and they cannot work in an environment without equipment deployment. In the process of device communication, the signal is easily blocked or limited by the signal transmission distance; while the inertial sensor is carried by the user, is not restricted by the environment, and has a lower cost of use, so compared with the previous three technologies, the inertial sensor has more advantages .

Today, inertial sensor-based pedestrian navigation technology has become a focus in both industry and academia. The inertial sensor has a built-in accelerometer and gyroscope that can collect acceleration signals and angular velocity signals, and then integrate the signals to obtain position and direction information. However, due to the noise and integral characteristics of the inertial sensor, the calculated speed and position will be in a short time. Rapid divergence, resulting in low positioning accuracy. During normal walking, the foot regularly touches the ground and remains stationary on the ground for short periods of time. This is the zero velocity zone. Resetting the speed to zero in the zero-speed interval can effectively suppress the divergence of speed and position caused by sensor noise. Correctly detecting the zero-speed interval and reducing the missed detection rate and false detection rate in the zero-speed interval are important means to improve positioning accuracy.

The current zero-speed detection methods based on inertial sensors mainly include two categories: (1) machine learning and (2) statistical methods. Machine learning methods usually achieve zero-speed interval detection of test gait data through feature extraction, feature screening, and model training. The statistical method usually substitutes the acceleration value and angular velocity value into a predefined formula to calculate the result, and detects the zero-speed interval by comparing the result with the custom threshold value. Compared with the machine learning method, the statistical method does not need to collect and mark a large amount of training data, the calculation amount is smaller, and a higher accuracy rate can be obtained under the condition of proper threshold optimization. The invention improves the traditional statistical method to transform the threshold problem into the extreme value detection problem, effectively avoids the limitations of the traditional method, and uses the angular velocity signal collected by the inertial sensor fixed on the user's feet to adaptively determine the window size for each step , and finally realize the accurate extraction of the zero-speed interval of both feet.

The research on zero-speed interval detection can be divided into three main methods: machine vision-based, pressure sensor-based and inertial sensor-based zero-speed detection.

Based on machine vision: This method uses a high-speed camera to capture the user's walking process, and then uses an image processing algorithm to obtain the trajectory signal of the reflective point, and then uses this signal to detect the zero-speed interval. The usability of this method is limited by occlusion, distance, and can only be used in environments where cameras are installed.

Based on the pressure sensor: This method installs the pressure sensor on the sole of the foot. When the foot surface touches the ground to support the body, the output value of the pressure sensor reaches the maximum, which is the zero-speed zone. However, the performance of the pressure sensor is easily affected by the installation method and the external environment.

Based on inertial sensors: Inertial sensors are often used in positioning, motion monitoring, etc. Machine vision and pressure sensors are usually used as the gold standard for the detection results of inertial sensors in the zero-speed interval to judge the latter’s missed detection and false detection rates.

The above-mentioned technical method has certain deficiencies in the simultaneous detection of zero speed of both feet, etc., which leads to high problems such as false detection in the zero speed interval.

Contents of the invention

The present invention aims to overcome the defective that prior art exists, and the present invention adopts following technical scheme:

In a first aspect, the present invention provides a method for detecting a zero-speed interval, which is applied to a pedestrian navigation system. The detection method comprises the steps of:

S1, data acquisition: use the gyroscope to collect data on the feet of pedestrians;

S2. Determine the adaptive energy window and the scope of action: determine the value of the adaptive energy window and the scope of action in the angular velocity energy difference detector AREDD through the collected data;

S3, determining the parameters of the angular velocity energy difference detector: calculating the ARED values of the left and right feet respectively according to the adaptive energy window value, and then taking the absolute value, and then detecting the minimum value point;

S4, Interval Length Estimation: Determine the interval length of each step through the gyroscope Z-axis data segmentation point interval, and then divide the initial zero-speed interval around the extreme point according to the length;

S5, Interval adjustment: After calculating the mean value of the gyro Z-axis data within the initial zero-speed interval, moving one sampling point to the left and right, move the initial zero-speed interval to the side with a smaller average value, and obtain the adjusted Zero speed range.

In some embodiments, the step S2, determining the adaptive energy window and the scope of action is specifically: using the periodicity of the Z-axis data of the biped gyroscope to determine the angular velocity energy difference and determine the size of the window in the angular velocity energy difference detector AREDD and scope of action.

In some embodiments, after the data collection in the step S1, a step is further included: detecting the standing state.

In some embodiments, the detection of the standing state in the step specifically includes: using the gyroscope data to calculate the Euclidean distance between the feet to divide the standing state and the walking state.

In some embodiments, the standing state detection in the step further includes: the detected part of the data in the standing still state is judged to belong to the zero-speed interval, and the walking state is detected, and then step S2 is performed: determining the adaptive energy window and the working range.

In some embodiments, the determining the angular velocity energy difference periodically using the Z-axis data of the biped gyroscope to determine the size of the window in the angular velocity energy difference detector AREDD further includes: performing a smoothing operation on the window value.

In some embodiments, the gyroscope data is gyroscope Z-axis data.

In some embodiments, the collected data includes: two-foot gait signals.

In a second aspect, the present invention provides a pedestrian navigation system, including: a zero-speed interval detection device, a processor and a memory;

The zero-speed interval detection device is arranged on a pedestrian, and obtains a signal based on the motion state of the pedestrian; the memory stores computer instructions that can be read by the processor, and when the computer instructions are read , the processor executes and implements the method for detecting a zero-speed interval as described above.

In a third aspect, the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the above-mentioned zero-speed interval detection method is realized.

It can be understood that, for the beneficial effects of the above-mentioned second aspect and the third aspect, reference may be made to the relevant description of the above-mentioned first aspect, and details are not repeated here.

Technical effect of the present invention: the zero-speed interval detection method disclosed in the present invention collects data on the feet of pedestrians through a gyroscope to determine the adaptive energy window value and range of action in the angular velocity energy difference detector AREDD, and according to the adaptive The energy window value is calculated separately by subtracting the ARED values of the left and right feet, and then the absolute value is taken. Finally, the interval length estimation and interval adjustment are performed to obtain the detection result of the zero-speed interval. It uses the angular velocity energy difference method of both feet to calculate the energy value of the angular velocity of both feet. The absolute value of the difference transforms the threshold selection problem in the traditional zero-speed detection method into the extreme value detection problem, avoiding the limitation that the traditional threshold method is difficult to apply to different walking speeds, and effectively reducing the missed detection rate. And different from the prior art, if you want to detect the zero-speed range of two feet, you must run the detection algorithm separately for the left and right feet, but the present invention utilizes the regularity and correlation of both feet when walking to detect the zero-speed interval of the left and right feet simultaneously. The speed range effectively reduces the calculation amount.

Description of drawings

Fig. 1 is the operation flowchart of the zero-speed zone detection method according to one embodiment of the present invention;

Fig. 2 is the gyroscope data of three axes collected during pedestrian walking according to an embodiment of the present invention;

Fig. 3 is part of Z-axis gyroscope data intercepted according to an embodiment of the present invention;

Fig. 4 is the data when the pedestrian starts to move according to one embodiment of the present invention;

Fig. 5 is the data of pedestrians when they stop moving according to one embodiment of the present invention;

Fig. 6 is the data of pedestrians swinging their feet when they start exercising according to one embodiment of the present invention;

Fig. 7 is the data of pedestrians swinging their feet when they stop moving according to an embodiment of the present invention;

Figure 8 is an initial window value curve obtained from an experiment according to an embodiment of the present invention;

Fig. 9 is the size of the window value after smoothing the initial window value curve according to an embodiment of the present invention;

Fig. 10 is a schematic diagram of the minimum point on the AREDD curve according to an embodiment of the present invention;

Fig. 11 is a schematic diagram of a detection result of a minimum point according to an embodiment of the present invention;

Fig. 12 is a schematic diagram of an initial zero-speed interval and an adjusted zero-speed interval according to an embodiment of the present invention;

Figure 13 is a comparison chart of detection results according to an embodiment of the present invention;

Fig. 14 is a schematic flowchart of a method for detecting a zero-speed interval according to an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

The present invention aims to overcome the defects in the prior art, and proposes a zero-speed interval detection method based on inertial sensors. An inertial sensor is fixed at the heel of the left and right feet, and then the built-in accelerometer and gyroscope of the inertial sensor are used to collect data respectively. The two-foot gait signal, through steps such as standing state detection, angular velocity energy difference detector (AREDD), energy window adjustment, and interval adjustment, can accurately detect the zero-speed interval at different movement speeds. The main technical content includes proposing a method for detecting the standing state based on the Euclidean distance between the feet, which can effectively detect the standing state (the standing state means that both feet are in the zero-speed range); based on the traditional angular velocity energy detector (ARED) The Angular Velocity Energy Difference Detector (AREDD) is proposed, which effectively avoids the limitation that the ARED threshold needs to be continuously adjusted manually as the movement speed changes, and effectively reduces the missed detection rate; a dynamic adjustment of the AREDD window based on the Z-axis data of the gyroscope is proposed. The length method makes the extreme point of the AREDD curve closer to the middle of the zero-speed interval, which effectively improves the detection accuracy; finally, a method of adjusting the length and range of the zero-speed interval is proposed to obtain the final zero-speed range, which effectively improves the detection accuracy.

Referring to FIG. 14 , in a first aspect, the present invention provides a method for detecting a zero-speed interval, which is applied to a pedestrian navigation system. The detection method comprises the steps of:

In some embodiments, the step S2, determining the adaptive energy window and the scope of action is specifically: Using the periodicity of the biped gyroscope Z-axis data to determine the angular velocity energy difference and determine the size of the window in the angular velocity energy difference detector AREDD and scope of action.

In some embodiments, the gyroscope data is gyroscope Z-axis data.

In some embodiments, the collected data includes: two-foot gait signals.

Shown in Fig. 1 with reference to, be the specific embodiment of the embodiment of the present invention, it mainly comprises following key steps:

1. Gyroscope data collection: the collected data includes: two-foot gait signals.

2. Standing/exercise state detection: Use gyroscope data to calculate the Euclidean distance between the feet to divide the standing and walking states.

3. Adaptive energy window and range of action: use the periodicity of the Z-axis data of the bipedal gyroscope to determine the size of the window and range of action in AREDD.

4. Angular Velocity Energy Difference Detector (AREDD): On the basis of the traditional ARED, it is improved. According to the window value of the second step, the ARED values of the left and right feet are respectively calculated and subtracted to take the absolute value, thus turning the problem of finding a suitable threshold into an extreme value detection problem. The limitation of ARED is that it needs to find different suitable thresholds for different speeds, and AREDD can effectively avoid this problem.

5. Interval length estimation and interval adjustment: The minimum value point found in the third step usually falls in the middle of the zero-speed interval. Interval adjustment needs to find a suitable interval length, determine the initial zero-speed interval range and adjust according to the extreme point interval and other steps. The interval length of each step is determined by the gyroscope Z-axis data segmentation point interval, and then the initial zero-speed interval is divided around the extreme point according to the length. However, when the speed is relatively large or small, compared with the real zero-speed interval, the initial zero-speed interval is relatively backward or forward. This embodiment calculates that the Z-axis data of the gyroscope moves to the left and right within the initial zero-speed interval. After the average value of a sampling point, the initial zero-speed interval is moved to the side with a smaller average value.

The detailed description of the above key steps is as follows:

Step 1: Standing state detection

Because the angular velocity energy difference detector AREDD is not applicable to the data in the standing state, it is necessary to distinguish between the standing state and the walking state. The formula is as follows:

Among them, w represents the gyroscope signal, i represents the i-th sampling point, representing the left foot, and r represents the right foot. Then collect the signal in the standing state, and calculate the maximum Euclidean distance in the signal:

Then the motion state is divided by the formula (4), where 0 represents the standing still state, 1 represents the standing shock state (both feet are standing but there is still a slight vibration), 2 represents the walking state,

Indicates that around the sampling point i

The Z-axis data of the gyroscope at a certain point in the interval of continuous points is greater than T ₁ , and T ₁ is a user-defined parameter.

The detected part of the data in the standing still state belongs to the zero-speed interval. The standing and oscillating state does not need to be processed. The walking state needs to further use AREDD to detect the zero-speed interval.

Step 2: Adaptive energy window and scope

Accompanying drawing 2 is the gyroscope data of three axes collected during people's walking, and accompanying drawing 3 is the Z-axis gyroscope data of part of two feet intercepted.

From attached drawing 2, it can be seen that the Z-axis data cycle is clear and distinct, the curve is smooth, and the zero-speed interval is more intuitive; and from attached drawing 3, it can be seen that the data parts of the left and right feet less than zero are regularly interlaced and the amplitude is relatively large, so this The embodiment selects the Z-axis gyroscope data to determine the size and scope of the window, which mainly includes the following steps:

1. Swing detection

Detect the data points where the Z-axis gyroscope data ω _z of both feet is less than the custom threshold T. These data points form a data segment with an amplitude of 1; then the s of the left and right feet are combined to form a swing. The formula is as follows, where T ₁ and The parameters T ₁ in the first step are equal.

swing＝-(s _l +s _r ) (6)

2. Determine the window length

Because this method subsequently needs to calculate E _k (formula 12), and the window value W in the formula is an important parameter. It is found through experiments that the most suitable W values corresponding to different gait cycle lengths are different, and the best W value is the length from near the midpoint of the swing phase to the midpoint of the next support phase (approximately half the gait cycle length). The detection method is mainly developed around the swing obtained in the previous step, and the data segment composed of non-zero continuous data points in the swing is recorded as f _i , i=1...k.

Find the points of one-third and two-thirds of each data segment f respectively, denoted as l and h:

Where floor means rounding down, ceil means rounding up, len means the length of the data segment, startPoint means the initial point of the data segment, endPoint means the end point of the data segment, T ₂ and T ₃ are custom parameters, then the window W value It can be determined by the following formula (9):

W _i = l _i -h _i (9)

When the subject moves forward at a constant speed, the W value will oscillate back and forth within a certain range, and further smoothing operations are required. The smoothing method is shown in formula (10):

3. Determine the window W _i action range

After the W value is calculated, it is also necessary to determine which data points are set as the window value W _i when calculating AREDD. It is found through experiments that switching W _i to W _i+1 at a certain point in time when both feet support the body at the same time works best, and record the data segment consisting of continuous data points with a value of 0 between f _i and f _i+1 Denote it as D _i , and then calculate the midpoint of D _i as the split point d, as shown in the following formula (11):

Where T ₄ is a custom parameter, and the window value corresponding to the data points between d _i and d _i+1 is set to W _i .

Step 3: Angular Velocity Energy Difference Detector (AREDD)

AREDD only uses gyroscope data, and this part needs to further detect the walking state data divided in the first step. ARED (Angular Rate Energy Detector) is a traditional zero-speed detection method, and its basic calculation formula is shown in (12):

Z _k =0 indicates that point k belongs to the zero-speed interval, W is the window length, and γ is a self-defined threshold. The value of γ is crucial to the zero-speed detection results. Too low a threshold will lead to missed detection in the zero-speed interval, while too high a threshold will lead to false detection, and the γ varies greatly between different subjects or different motion speeds , and it can only detect the zero-speed zone of a single foot. The AREDD proposed by the embodiment of the present invention is improved on the basis of the ARED, and can simultaneously detect the zero-speed interval of both feet.

1. Calculate AREDD

The calculation formula of AREDD is as follows:

abs means to take the absolute value,

and

Respectively represent the ARED of the left and right feet of point j, the formula is given by Equation 12, and the window size of all data points j between the split point d _i to d _i+1 is set to W _i when calculating ARED.

2. Minimum point detection

This embodiment traverses the AREDD curve composed of ED values, takes any sample point with a value less than 200, and compares it with the left and right adjacent sample points. If the value of the sample point is smaller than the left and right, this embodiment considers that The sample point is the minimum value point, and the minimum value point is recorded as Min _i , i=1...k.

The fourth step, interval length estimation and interval adjustment

The value formula (15) of the initial zero-speed interval length L is as follows:

After calculating the interval length L _i , take the minimum value point _Mini as the center, and regard the interval _Mi as the i-th initial zero-speed interval. The formula (16) of _Mi is as follows:

However, when the speed is too large/too small, the value of W _i will be too large/small, resulting in the minimum point behind/front, so that _Mi covers the data generated when the foot rolls on the ground (not zero speed ), so M _i needs to be further adjusted.

First of all, first distinguish whether _Mi is the zero-speed interval of the left foot or the right foot. A _Mi corresponds to a _Mini , assuming that _Mini is the jth data point in all data, then it can be distinguished by the following formula (17):

Belong(M _i )=0 means that M _i is the initial zero-speed interval of the left foot, otherwise it is the right foot. Assuming that a certain M _i belongs to the left foot, then according to the Z-axis data of the left foot gyroscope

To adjust the initial zero-speed range, the algorithm is as follows:

1. Calculate

The mean values in the range of Mi _-1 , _Mi and _Mi +1 (not Mi _-1 and Mi ₊₁ ) are A, B and C respectively, if C<B, skip to step 2; If A<B, skip to step 3; if B<A and B<C; skip to step 4;

2. _Mi moves one data point to the right as a whole, that is, _Mi = _Mi + 1, and skips to step 1;

3. _Mi moves one data point to the left as a whole, that is, _Mi = _Mi -1, skip to step 1;

4. The algorithm ends;

In a third aspect, the present invention provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the above-mentioned zero-speed interval detection method is implemented.

The so-called processor can be a central processing unit (Central Processing Unit, CPU), and the processor can also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC) ), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

In some embodiments, the storage may be an internal storage unit of the server, such as a hard disk or memory of the server. The memory may also be an external storage device of the server in other embodiments, such as a plug-in hard disk equipped on the server, a smart memory card (Smart Media Card, SMC), a secure digital card (Secure Digital, SD), flash card (Flash Card), etc. Further, the storage may also include both an internal storage unit of the server and an external storage device. The memory is used to store operating system, application program, boot loader (Boot Loader), data and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

It can be understood that, for the beneficial effects of the above-mentioned second aspect and the third aspect, reference can be made to the relevant description of the above-mentioned first aspect, and details are not repeated here.

Technical effect of the present invention: the zero-speed interval detection method disclosed in the present invention collects data on the feet of pedestrians through a gyroscope to determine the adaptive energy window value and range of action in the angular velocity energy difference detector AREDD, and according to the adaptive The energy window value is calculated separately by subtracting the ARED values of the left and right feet, and then the absolute value is taken. Finally, the interval length estimation and interval adjustment are performed to obtain the detection result of the zero-speed interval. It uses the angular velocity energy difference method of both feet to calculate the energy value of the angular velocity of both feet. The absolute value of the difference transforms the threshold selection problem in the traditional zero-speed detection method into the extreme value detection problem, avoiding the limitation that the traditional threshold method is difficult to apply to different walking speeds, and effectively reducing the missed detection rate. And different from the prior art, if you want to detect the zero-speed range of two feet, you must run the detection algorithm separately for the left and right feet, but the present invention utilizes the regularity and correlation of both feet when walking to detect the zero-speed interval of the left and right feet simultaneously. The speed range effectively reduces the calculation amount. The present invention only extracts information in the time domain of the signal, and does not involve the frequency domain, so the present invention can be used in the zero-speed interval detection of quasi-real-time gait data.

In order to prove the effect of the present invention, the embodiment of the present invention also provides test verification, and the verification process and preliminary test results are as follows.

The embodiment of the present invention collects inertial sensor and pressure sensor data at different motion speeds, wherein the inertial sensor uses Xsens Dot, and the pressure sensor uses the force sense technology insole pressure gauge RPPS-99, and the zero velocity phase detected by the pressure gauge is used as the gold Standard, to test and verify the proposed zero-speed detection method based on inertial sensor. In the embodiment of the present invention, the data of a subject is collected, aged 25, healthy and without disease in the lower limbs. Before the experiment, an inertial sensor (sampling rate 120HZ) was fixed on the outside of the ankle of the subject's feet, and a pressure gauge (sampling rate 50HZ) was placed in the two shoes at the same time. During the experiment, the subjects changed the forward speed according to their own ideas. After each speed change, they needed to move at a constant speed for a period of time, moving forward in a straight line for 80 seconds, and repeated data collection 5 times in total. Since the sampling rates of the pressure gauge and the inertial sensor are inconsistent, the embodiment of the present invention performs an up-sampling operation on the collected pressure gauge data at 120 Hz. The following data and pictures are from one of the experiments. Since there are too many data points collected in each experiment, it is not convenient to display them all, so the data at the beginning and end are selected for display.

Standing/exercise state detection method based on Euclidean distance: the principle of the experiment is described in detail in the first step, and the experimental results are shown in Figure 4 and Figure 5: Accompanying drawing 4 is the data when starting to move, and the accompanying drawing 5 is the data when the motion is stopped, where the thick solid line is the state detection result (in order to display intuitively, multiply the detection state by 100), the area with a value of 200 is the motion state, the area with a value of 100 is the standing vibration state, and the value is 0 is the standing still state. You can see that there is usually a standing vibration state before and after the exercise. Although the legs are not swinging at this time, the sensor still has a slight vibration. This method can accurately distinguish the standing state from the exercise state.

Adaptive window size and range estimation method: firstly, it is necessary to detect the swing process of both feet. The principle is explained above. The detection results are shown in Figure 6 and Figure 7 (in order to show intuitively, the swing is multiplied by 200). Fig. 6 is the data when starting to move, and accompanying drawing 7 is the data when stopping to move, and the thick solid line is the swing detection result.

Then determine the three detection points that determine the size of the window and the range of action according to the swing. The principle is explained in detail in the second step and item 2 and 3. Attached Figure 8 is the initial window value curve obtained by the experiment. It can be seen that the curve will oscillate back and forth around a certain value. For example, although it is in a constant speed stage between

steps

50 and 80, the window size changes back and forth around 55, so it is necessary to Further smoothing operations. Attached Figure 9 is the smoothed window value size. It can be seen that the window value oscillation in the constant speed stage is suppressed, and the window size is more reasonable.

Two-foot angular velocity energy difference method: mainly includes calculation of AREDD and minimum value detection. The principle of the experiment is explained above, and the calculation results are as follows: From Figure 10, it can be seen that the AREDD curve has a clear minimum value point , the minimum points are all less than 200 and close to the middle of the support phase. Accompanying drawing 11 is the minimum point detection result.

Zero-speed interval length estimation and zero-speed interval range adjustment method: Calculate the interval length and combine the minimum value points to determine the initial zero-speed interval, and then adjust the initial zero-speed interval range. The principle has been described in detail above. The initial zero-speed interval The interval and the adjusted zero-speed interval are shown in Figure 12:

Finally, in order to verify the detection effect in the zero-speed interval of the present invention, the embodiment of the present invention uses the pressure gauge data as a gold standard to evaluate the detection effect. The detection results of the embodiment of the present invention include the zero-speed interval of the left and right feet at the same time (the method for judging whether a certain interval belongs to the left foot or the right foot has been given in the foregoing description), and the two pressure gauges placed in the shoes provide the left or right foot respectively. Therefore, the number of zero-speed intervals of each pressure gauge should be reduced by half. The comparison of the test results of the two is shown in Figure 13 (a section of data is intercepted for easy display). It can be seen from FIG. 13 that each zero-speed interval detected by the embodiment of the present invention falls within the gold standard range of the left foot or right foot pressure gauge, without missing detection, and has good accuracy.

It can be seen from the above embodiments that the present invention reduces the false detection rate by proposing the energy difference method of the angular velocity of both feet, and improves the performance of the energy difference method through the method of adaptive window size and range estimation and the window oscillation suppression method . The invention adopts the Euclidean distance of the angular velocity of both feet to divide the user state into a standing static state, a standing oscillating state and a moving state, and solves the problem that the energy difference method cannot handle the first two user states. The invention determines the length L of the zero-speed interval based on the Z-axis gyroscope data of both feet, and determines the range of length L as the initial zero-speed interval with the extreme point as the center, and then adjusts the initial position of the interval by calculating the mean value, effectively reducing false detection Rate.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

The above are only optional embodiments of the application, and are not intended to limit the application. For those skilled in the art, various modifications and changes may occur in this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of the claims of the present application.

Technical effect of the embodiment of the present invention: the zero-speed interval detection method and device disclosed in the embodiment of the present invention collect and identify the marker pattern on the target screen, then calculate through optical communication transmission, and use the positioning algorithm to analyze the picture to obtain the marker Image information, obtain the relative position information between the image acquisition device and the target screen to complete the positioning. The positioning method and device of the present invention realize the positioning operation of a large-scale and high-precision pedestrian navigation system based on the optical communication between the target screen and the image acquisition module, and use the designed specific marker pattern to assist positioning, so that the positioning calculation does not depend on the image matching algorithm. As a result, the positioning accuracy is greatly improved, and the equipment cost is low, the influence of environmental factors on the ranging accuracy is reduced, and the positioning stability in industrial applications is improved. The embodiments of the present invention use deep learning models and traditional image processing methods to complete the recognition of marker patterns, and solve the problem that markers cannot be recognized because the images of markers are too small at a long distance from the camera. Therefore, compared with other camera positioning technologies, this solution greatly improves the scope of camera positioning. The embodiment of the present invention utilizes the screen optical communication mechanism to transmit the absolute position of the screen to the pedestrian navigation system, so that the range of positioning is greatly increased.

The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented by hardware, software modules executed by a processor, or a combination of both. Software modules can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other Any other known storage medium.

The above description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined in this invention may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention will not be limited to these embodiments shown in the present invention, but will conform to the widest scope consistent with the principles and novel features disclosed in the present invention.

Claims

A zero-speed interval detection method applied to a pedestrian navigation system, characterized in that it includes:

S1, data acquisition: use the gyroscope to collect data on the feet of pedestrians;

S2. Determine the adaptive energy window and the scope of action: determine the value of the adaptive energy window and the scope of action in the angular velocity energy difference detector AREDD through the collected data;

S3, determining the parameters of the angular velocity energy difference detector: calculating the ARED values of the left and right feet respectively according to the adaptive energy window value, and then taking the absolute value, and then detecting the minimum value point;

S4, Interval Length Estimation: Determine the interval length of each step through the gyroscope Z-axis data segmentation point interval, and then divide the initial zero-speed interval around the extreme point according to the length;

S5, Interval adjustment: After calculating the mean value of the gyro Z-axis data within the initial zero-speed interval, moving one sampling point to the left and right, move the initial zero-speed interval to the side with a smaller average value, and obtain the adjusted Zero speed range.
The method according to claim 1, characterized in that, the step S2, determining the adaptive energy window and the scope of action is specifically: using the periodicity of the biped gyroscope Z-axis data to determine the angular velocity energy difference to determine the angular velocity energy difference The size and scope of the window in the detector AREDD.
The method according to claim 1, characterized in that, after the data collection in the step S1, further comprising the step of: standing state detection.
The method according to claim 3, wherein the detection of the standing state in the step specifically comprises: using the gyroscope data to calculate the Euclidean distance between the feet to divide the standing state and the walking state.
The method according to claim 4, wherein the standing state detection in said step also includes: the detected standing still state partial data is judged to belong to the zero-speed interval, and the walking state is detected, then step S2 is performed: determining the self-adaptive Energy window and range of action.
The method according to claim 2, wherein the periodic determination of the angular velocity energy difference using the biped gyroscope Z-axis data to determine the size of the window in the angular velocity energy difference detector AREDD also includes: window value for smoothing.
The method according to claim 4, wherein the gyroscope data is gyroscope Z-axis data.
The method according to claim 1, characterized in that the collected data includes: two-foot gait signals.
A pedestrian navigation system, characterized in that it includes:

The zero-speed interval detection device is installed on the pedestrian and acquires a signal based on the motion state of the pedestrian;

processor;

A memory storing computer instructions that can be read by the processor, and when the computer instructions are read, the processor executes the method according to any one of claims 1 to 8.
A storage medium is characterized in that it is used for storing computer-readable instructions, and the computer-readable instructions are used for causing a computer to execute the method according to any one of claims 1 to 8.