CN104913772B - A kind of pedestrian movement's detection method based on leg posture information - Google Patents

Abstract

A kind of pedestrian movement's detection method based on leg posture information：3 axis gyro sensors are placed in the huckle of measured and the angular velocity information of acquisition is wirelessly transmitted to computer；It obtains pedestrian's leg angular velocity information during the motion and carries out the resolving at leg detection angle；Leg detection angle measurement after resolving is subjected to positive peak extraction and zero passage detection；Classification of motions judgement is carried out according to the result of detection and is exported.The present invention such as can effectively distinguish race, walk, stand, sitting, squatting at the movements, it estimates that pedestrian movement's state and position provide help for systems such as personal navigation, M-health, a kind of effective solution scheme especially is provided in terms of improving its positioning accuracy to the indoor pedestrian navigation positioning system for being based on pedestrian's reckoning (PDR).

Description

A Pedestrian Motion Detection Method Based on Leg Pose Information

technical field

The invention belongs to the field of personal motion detection, in particular to a method for obtaining angular velocity information of thighs when pedestrians are moving, and proposing a motion classification method based on 3-axis gyroscope sensor signals according to the obtained data signals.

Background technique

Nowadays, with the rapid increase of data services and multimedia services, people's demand for positioning and navigation is increasing, especially in complex indoor environments, such as airport halls, exhibition halls, warehouses, supermarkets, libraries, underground parking lots, mines, etc. , it is often necessary to determine the indoor location information of the mobile terminal or its holder. Traditional navigation systems usually require external auxiliary signals for positioning, but due to the limitations of positioning time, positioning accuracy, and complex indoor environments, the cost of positioning is often high.

With the rapid development of MEMS technology, its volume and cost have been continuously reduced, so it is widely used in the field of civil navigation. Because the navigation system using MEMS technology can achieve positioning in complex environments such as indoors without external auxiliary signals, a new technology was born. A series of indoor pedestrian navigation and positioning algorithms based on Pedestrian Dead Reckoning (PDR), if the movement of pedestrians can be accurately classified, it will provide an effective solution to improve the positioning accuracy of the PDR algorithm navigation system.

Compared with related patent applications in the same field, the inventiveness and advantages of the present invention are more obvious. For example, the application number is: 201010134122.1, and the patent name is "A Walking Classification Method Based on 3-Axis Acceleration Sensor Signal", which uses a 3-axis accelerometer sensor placed on the ankle to measure the acceleration information of pedestrians when they are moving. The acceleration threshold in different motion states realizes the classification of different motions, but this method cannot effectively distinguish motions such as sitting, standing, and squatting, and the discrimination effect of slow walking, slow climbing stairs and other motions with insignificant acceleration changes is poor. . For another example, the application number is: 201010248929.8, and the patent name is "Personal Positioning Method and Device Based on Motion Measurement Information", which mentions a motion category determination method, which requires modeling of walking, running and other motions, and The model parameters are calibrated according to the needs of different users. This method covers too few classification types, only including walking and running, and needs to be re-calibrated for different users, so the applicability is poor. Finally, a motion classification method is introduced in the paper "Weightlessness feature-a novel feature for single tri-axial accelerometer based activity recognition". The author regards motion classification as a pattern recognition problem, and performs time-domain features and frequency-based analysis of acceleration data. Domain features are extracted, and motion is classified using a support vector machine (SVM) approach. This method has a high amount of calculation, which is not conducive to the low cost of the equipment.

Contents of the invention

The purpose of the present invention is to make up for the shortcomings of the existing motion classification methods, such as large amount of calculation, poor generalization, and few classification types, and propose a pedestrian motion classification detection method by only using the angular velocity information when the thighs are in motion.

The technical scheme adopted in the present invention is: a pedestrian motion detection method based on leg posture information, comprising the following steps:

Step 1, fix the 3-axis gyro sensor on the thigh of the subject and transmit the collected angular velocity information to the computer by wireless;

Step 2: Obtain the leg angular velocity information of the pedestrian during the movement process, calculate the leg detection angle, and calculate the zero-crossing interval time;

Step 3, performing positive peak extraction and zero-crossing detection on the calculated leg detection angle measurement value;

Step 4: According to the detection result of the leg detection angle, the motion classification is judged according to the judgment rule and output.

The angular velocity information in step 1 is measured by a measuring sensor device placed on the pedestrian's thigh; the measuring sensor device includes a gyroscope.

Further, in the step 3, the calculated positive peak value is extracted from the leg detection angle measurement value, and the extracted positive peak value is used as the motion determination value and locked until the next positive peak value is detected. At the same time, the zero-crossing detection is carried out on the measured value of the leg detection angle calculated by the solution, and the time of two consecutive zero-crossing points is calculated. If the measured value of the detected angle at a certain moment is greater than the measured value at the previous moment and the next moment, then this measured value is a positive peak value, which is used to determine the motion judgment value; zero-crossing detection means that the detected angle measured value at a specific time Whether there is a point with a value of zero in , it is used to determine whether the pedestrian is in a moving state. The specific time can be selected as 0.1 second, 0.2 second, 0.5 second, 1 second, 2 seconds or 5 seconds.

Further, in the step 4, the discrimination process includes the following steps,

1) Determine whether the motion judgment value is greater than 75 degrees, if yes, go to step 2, otherwise, go to step 3;

2) Determine whether the motion judgment value is greater than 110 degrees, if yes, the output motion is squatting, otherwise, the output motion is sitting;

3) Judging whether the motion judgment value is greater than 15 degrees and the zero-crossing interval is between 0.2 seconds and 1 second, if yes, go to step 4, otherwise, the output motion is standing;

4) Judging whether the motion judgment value is greater than 40 degrees, if yes, the output motion is running, otherwise, the output motion is walking.

Compared with prior art, the beneficial effect of the present invention is:

(1) The present invention classifies and distinguishes according to the angular velocity information of the thigh movement when the human body moves, and the applicability and popularization are relatively high;

(2) The used sensor data information of the present invention is less, and calculation amount is also few, is beneficial to the low cost of equipment;

(3) The present invention can recognize and determine many types of motion, basically covering all motions during indoor navigation;

(4) The present invention can output different output detection values for different motions, which is convenient for integration into the existing method based on dead reckoning (PDR).

Description of drawings

Fig. 1 is the overall flowchart of the present invention;

Fig. 2 is a schematic diagram of the axial and leg arrangement of the 3-axis gyroscope in the present invention;

Fig. 3 is the flow chart of detection and discrimination of the present invention;

Fig. 4 is a schematic diagram of the relationship between leg detection angle and motion;

Detailed ways

The pedestrian motion detection method based on leg posture information of the present invention will be described in detail below in conjunction with the accompanying drawings.

1) The 3-axis gyroscope sensor (2-3) is placed on the thigh (2-2) of the pedestrian (2-1), and its inertial reference coordinate system (2-4) is defined as follows: x-axis (2- 5) Point to the left side of the pedestrian (2-1), the y-axis (2-6) points to the front of the pedestrian (2-1), and the z-axis (2-7) points to the bottom of the pedestrian (2-1). Its installation axis and specific position are shown in Figure 2.

2) Measure the angular velocity information of the thigh when the pedestrian is moving, and transmit it to the computer through wireless bluetooth.

3) Using the received angular velocity information data to solve the leg detection angle, the details are as follows

The initial attitude angle Substitute θ ₀ and γ ₀ into the following formula to find the quaternion at the initial moment and complete the initial alignment.

Then do the quaternion update

Written in matrix form as:

where ω _x , ω _y , and ω _z are the measured angular velocities of the pedestrian's leg gyroscope in the x, y, and z axes, respectively, in the carrier coordinate system. Set the sampling period T, read the data of the gyroscope at regular intervals, obtain the updated quaternion through the above formula, and then obtain the updated direction cosine matrix. The quaternion is updated using the first-order Runge-Kutta method, and the angular velocity ω _x , ω _y , ω _z obtained after sensor data fusion is used to update the quaternion.

After the quaternion is updated in each cycle, the quaternion is normalized.

After the quaternion is normalized by the formula, the solution matrix can be obtained according to the relationship between the solution matrix and the quaternion.

in:

To get the leg detection angle:

4) According to the characteristics that pedestrians are not easy to quickly switch the motion state during the motion process (such as quickly switching from sitting to running), in order to avoid misjudgment, set the output delay of motion detection to 2 seconds (3-3), that is, 2 seconds The measured value (3-4) of the leg detection angle (3-1) after internal calculation is stored in the buffer area, and the positive peak value extraction (3-5) and zero-crossing detection (3-5) and zero-crossing detection ( 3-12), then clear the buffer area, and repeat this process until there is no detection angle input;

5) The positive peak value extracted in 2 seconds is used as the motion determination value (3-14) and locked until the next positive peak value (3-13) is detected, and the time of two consecutive zero-crossing points is calculated at the same time. Then make the following judgments:

1) Determine whether the motion judgment value (3-14) is greater than 75 degrees (3-16), if it is (3-18), go to step 2, otherwise (3-17), go to step 3;

2) Determine whether the motion judgment value (3-14) is greater than 110 degrees (3-22), if yes (3-27), the output motion is squatting (3-32), otherwise (3-26), the output motion is sitting ( 3-31);

3) Determine whether the motion judgment value (3-14) is greater than 15 degrees (3-19) and the zero-crossing interval is between 0.2 seconds and 1 second (3-9), if yes (3-25), go to step 4 , otherwise (3-24), the output motion is standing (3-28);

4) Determine whether the motion judgment value (3-14) is greater than 40 degrees (3-21), if it is (3-25), the output motion is running (3-30), otherwise (3-24), the output motion is walking ( 3-29).

If the judgment result is the same motion state within these 2 seconds, it will be output directly. If the judgment result is more than two motion states (that is, at the critical moment of motion switching), then the motion state at the previous moment will be output. The detection and discrimination flow chart is shown in Figure 3.

By the above five steps, the design and invention of this pedestrian motion detection method based on leg posture information can be completed.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements without departing from the spirit and scope of the technical solutions of the present invention shall be covered by the claims of the present invention.

Claims (1)

1. A pedestrian motion detection method based on leg posture information is characterized in that: the method comprises the following steps:

step 1, fixing a 3-axis gyroscope sensor on the thigh of a measured person and transmitting acquired angular velocity information to a computer in a wireless manner;

step 2, acquiring leg angular velocity information of the pedestrian in the motion process and calculating leg detection angles;

wherein, the initial attitude angle is firstlyθ₀、γ₀Substituting the formula (1) into the initial alignment to obtain the quaternion at the initial moment and finish the initial alignment;

and then updating quaternion:

written in matrix form as:

in the formula of omega_x,ω_y,ω_zThe angular velocity measurement values of x, y and z axes of the pedestrian leg gyroscope under a carrier coordinate system are respectively measured; setting a sampling period T, reading data of the gyroscope at intervals, obtaining an updated quaternion through the formula, and further obtaining an updated direction cosine matrix; the quaternion is updated by a first-order Runge-Kutta method and by utilizing the angular velocity omega obtained after the data of the sensor are fused_x,ω_y,ω_zUpdating the quaternion;

after updating the quaternion in each period, carrying out normalization processing on the quaternion:

after quaternion is processed by formula normalization, the calculation matrix can be obtained by calculation according to the relation between the calculation matrix and quaternion

Wherein:

thereby obtaining a leg detection angle:

step 3, performing positive peak value extraction and zero-crossing detection on the calculated leg detection angle measurement value, and calculating zero-crossing interval time;

wherein, the measured value of the leg detection angle calculated by solution is subjected to positive peak value extraction; in order to avoid misjudgment, setting the output delay of motion detection to be 2 seconds, namely storing leg detection angle measurement values calculated within 2 seconds into a cache region, carrying out positive peak value extraction and zero-crossing detection on data within 2 seconds in the cache region, then emptying the cache region, and repeatedly executing the process until no detection angle is input any more; taking the extracted positive peak value as a motion judgment value and locking the motion judgment value until the next positive peak value is detected;

performing zero-crossing detection on the calculated leg detection angle measurement value, and calculating two continuous zero-crossing time;

step 4, performing motion classification judgment according to the detection result of the leg detection angle and a judgment rule and outputting the judgment result; wherein,

1) judging whether the motion judgment value is larger than 75 degrees, if so, entering the step 2), and otherwise, entering the step 3);

2) judging whether the motion judgment value is greater than 110 degrees, if so, outputting the motion as squatting, and otherwise, outputting the motion as sitting;

3) judging whether the motion judgment value is larger than 15 degrees or not and the zero-crossing interval time is between 0.2 and 1 second, if so, entering the step 4), and if not, outputting the motion as standing;

4) judging whether the motion judgment value is greater than 40 degrees, if so, outputting the motion as running, otherwise, outputting the motion as walking;

if the judgment result is the same motion state within the 2 seconds, the motion state is directly output, and if the judgment result is more than two motion states, the motion state at the previous moment is output.