CN104913772B - A kind of pedestrian movement's detection method based on leg posture information - Google Patents
A kind of pedestrian movement's detection method based on leg posture information Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
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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
Technical Field
The invention belongs to the field of personal motion detection, and particularly relates to a motion classification method based on 3-axis gyroscope sensor signals, which is used for acquiring angular velocity information of thighs when pedestrians move and providing data signals according to the acquired angular velocity information.
Background
Nowadays, with the rapid increase of data services and multimedia services, people's demands for positioning and navigation are increasing, and especially in complex indoor environments, such as airport halls, exhibition halls, warehouses, supermarkets, libraries, underground parking lots, mines and other environments, it is often necessary to determine the indoor position information of the mobile terminal or its holder. The conventional navigation system usually requires an external auxiliary signal for positioning, but is limited by positioning time, positioning accuracy, and complex indoor environment, and the positioning cost is often high.
With the rapid development of the MEMS technology, the size and the cost of the MEMS technology are continuously reduced, so the MEMS technology is widely applied to the field of civil navigation, and because a navigation system applying the MEMS technology can realize positioning in indoor and other complex environments without external auxiliary signals, a series of indoor pedestrian navigation positioning algorithms based on Pedestrian Dead Reckoning (PDR) are born, and if the movement of pedestrians can be accurately classified, an effective solution is provided for improving the positioning precision of the PDR algorithm navigation system.
Compared with the related patent application in the same field, the invention has obvious creativity and advantages. For example, the application numbers are: 201010134122.1, the patent name is "Classification method for Walking based on 3-axis acceleration sensor signal", it adopts and installs the acceleration information when the 3-axis accelerometer sensor that is located at the ankle measures the pedestrian's motion, through detecting the acceleration threshold value under different motion states and realizing the classification to different motion, but this kind of method can't distinguish effectively and sit, stand, squat etc. the motion of sitting, standing, squatting etc. moreover to walk slowly, climb slowly and move slowly the motion of acceleration change unobvious less effective discrimination. For another example, application nos.: 201010248929.8 entitled personal positioning method and device based on exercise measurement information, which discloses an exercise type determination method, which needs to model the exercises such as walking and running and calibrate the model parameters according to the needs of different users. The method covers too little classification categories, only comprises walking and running, and is poor in applicability because different users need to be calibrated again. Finally, a motion classification method is introduced in a document of weight lesson feature-a novel feature for single tri-axial accelerometer based activity recognition, and an author regards motion classification as a pattern recognition problem, extracts time domain features and frequency domain features of acceleration data, and classifies motions by using a method of a Support Vector Machine (SVM). This method is high in calculation amount and is not favorable for low cost of the equipment.
Disclosure of Invention
The invention aims to make up the defects of large calculation amount, poor popularization and few classification types of the existing motion classification method, and provides a pedestrian motion classification detection method only by utilizing angular velocity information during thigh motion.
The technical scheme adopted by the invention is as follows: a pedestrian motion detection method based on leg posture information 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 a pedestrian in the motion process, resolving leg detection angles, and calculating zero-crossing interval time;
step 3, performing positive peak value extraction and zero-crossing detection on the calculated leg detection angle measurement value;
and 4, performing motion classification judgment according to the detection result of the leg detection angle and a judgment rule and outputting the result.
Measuring the angular velocity information in the step 1 through a measuring sensor device arranged on the thigh of the pedestrian; the measurement sensing device includes a gyroscope.
Further, in step 3, the calculated leg detection angle measurement value is subjected to positive peak value extraction, and the extracted positive peak value is locked as a motion determination value until the next positive peak value is detected. And meanwhile, zero-crossing detection is carried out on the calculated leg detection angle measurement value, and two continuous zero-crossing time is calculated. If the measured value of the detection angle at a certain moment is larger than the measured values at the previous moment and the later moment, the measured value is a positive peak value and is used for determining a motion judgment value; the zero-crossing detection is to detect whether a point with a zero value exists in the angle measurement value within a specific time, and is used for determining whether the pedestrian is in a traveling state. The specific time may be selected to be 0.1 second, 0.2 second, 0.5 second, 1 second, 2 seconds, or 5 seconds.
Further, the discrimination process in the above-mentioned step 4 includes the steps of,
1) judging whether the motion judgment value is larger than 75 degrees, if so, entering the step 2, 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) and judging whether the motion judgment value is greater than 40 degrees, if so, outputting the motion as running, and otherwise, outputting the motion as walking.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention carries out classification and judgment according to the angular velocity information of thigh movement when the human body moves, and has higher applicability and popularization;
(2) the sensor data information used by the invention is less, the calculation amount is less, and the low cost of the equipment is facilitated;
(3) the invention can identify and judge more types of motion and basically covers all the motion during indoor navigation;
(4) the invention can output different output detection values for different motions, and is convenient to integrate into the existing dead reckoning-based method (PDR).
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a schematic axial and leg arrangement of a 3-axis gyroscope of the present invention;
FIG. 3 is a flow chart of the detection and discrimination of the present invention;
FIG. 4 is a schematic diagram showing the relationship between leg detection angle and motion;
Detailed Description
The pedestrian motion detection method based on the leg posture information of the present invention will be described in detail below with reference to the accompanying drawings.
One) to place a 3-axis gyroscope sensor (2-3) on the thigh (2-2) of a pedestrian (2-1), the inertial reference frame (2-4) is defined as follows: the x-axis (2-5) points 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 lower side of the pedestrian (2-1). The axial and specific location of the arrangement is shown in fig. 2.
And secondly) measuring the angular velocity information of the thigh part when the pedestrian moves, and transmitting the information to a computer in a wireless Bluetooth mode.
Thirdly) the calculation of the leg detection angle using the received angular velocity information data is specifically as follows
Firstly, the initial attitude angle isθ0、γ0Substituting the formula to obtain the quaternion at the initial time to complete the initial alignment.
Then updating quaternion
Written in matrix form as:
in the formula of omegax，ωy，ωzThe angular velocity measurement values of the x, y and z axes of the pedestrian leg gyroscope under the carrier coordinate system are respectively. And 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 fusedx，ωy，ωzUpdatingA quaternion.
And after updating the quaternion in each period, performing 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:
fourthly) according to the characteristic that the pedestrian is not easy to rapidly switch the motion state in the motion process (such as rapid switching from sitting to running), in order to avoid misjudgment, the output time delay of motion detection is set to be 2 seconds (3-3), namely, the measured value (3-4) of the leg detection angle (3-1) calculated within 2 seconds is stored in a buffer area, the positive peak value extraction (3-5) and the zero-crossing detection (3-12) are carried out on the data within 2 seconds in the buffer area, then the buffer area is emptied, and the process is repeatedly executed until the detection angle is not input;
and fifthly), taking the positive peak value extracted in 2 seconds as a motion judgment value (3-14) and locking the positive peak value until the next positive peak value (3-13) is detected, and simultaneously calculating two continuous zero-crossing time. Then, the following judgment is made:
1) judging whether the motion judgment value (3-14) is greater than 75 degrees (3-16), if so, judging to be (3-18), entering the step 2, otherwise, judging to be (3-17), and entering the step 3;
2) judging whether the movement judgment value (3-14) is greater than 110 degrees (3-22) or not, if so, judging the movement judgment value is (3-27), and if not, judging the output movement is squatting (3-32), otherwise, judging the output movement is sitting (3-31);
3) judging whether the motion judgment value (3-14) is greater than 15 degrees (3-19) or not and the zero-crossing interval time is between 0.2 second and 1 second (3-9), if so, judging to be (3-25), entering the step 4, and if not, judging to be (3-24), outputting the motion to be standing (3-28);
4) and judging whether the motion judgment value (3-14) is greater than 40 degrees (3-21) or not, if so, judging the motion judgment value (3-25), and if not, judging the output motion to be running (3-30), otherwise, judging the output motion to be running (3-24), and judging the output motion to be running (3-29).
If the judgment result is the same motion state in the 2 seconds, the motion state is directly output, and if the judgment result is more than two motion states (namely, at the critical moment of motion switching), the motion state at the previous moment is output. The detection discrimination flowchart is shown in fig. 3.
The design and invention of the pedestrian motion detection method based on the leg posture information can be completed through the five steps.
Finally, it should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should 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 omegax,ωy,ωzThe 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 fusedx,ωy,ωzUpdating 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.
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| CN114440883B (en) * | 2022-04-08 | 2022-06-17 | 南京师范大学 | Pedestrian positioning method based on foot and leg micro-inertia measurement unit |
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| US8055469B2 (en) * | 2006-03-03 | 2011-11-08 | Garmin Switzerland Gmbh | Method and apparatus for determining the attachment position of a motion sensing apparatus |
| CN101894252B (en) * | 2010-03-29 | 2012-12-05 | 天津大学 | Walking movement classification method based on triaxial acceleration transducer signals |
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