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Pedestrian gait recognition method based on MEMS inertial sensor

An inertial sensor and gait recognition technology, applied in the field of personal navigation and positioning, can solve problems such as poor practicability, low recognition accuracy, and not so large differences, and achieve the effects of accurate judgment, high recognition accuracy, and strong adaptability

Inactive Publication Date: 2020-09-01
XIDIAN UNIV
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AI Technical Summary

Problems solved by technology

This method does not have high recognition accuracy when using
Sometimes the difference in acceleration and angular velocity in different stances is not so large, so the practicability is poor

Method used

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  • Pedestrian gait recognition method based on MEMS inertial sensor
  • Pedestrian gait recognition method based on MEMS inertial sensor
  • Pedestrian gait recognition method based on MEMS inertial sensor

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Embodiment Construction

[0013] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0014] Such as figure 1 As shown, the indoor gait recognition method based on the MEMS inertial sensor includes filling the difference of the acceleration angular velocity and smoothing the filter. Carry out feature construction, and finally design the experimental model.

[0015] Due to the large difference in acceleration and angular velocity at different moments, the acceleration and angular velocity are selected as the original characteristic data. In order to be able to better identify and classify, it is necessary to perform feature processing on the data. The normal adult's pace is 1.5m / s, so it can be considered that a complete step can be taken within 1 second. Therefore, add a window of 1 second, and then perform fast Fourier transform...

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Abstract

The invention discloses an indoor positioning personnel gait recognition method based on an MEMS inertial sensor. In indoor positioning, accurate gait recognition can provide prior information for anattitude estimation algorithm, so that the positioning precision is improved. Acceleration and angular velocity of a testee under five gaits of walking, running, standing, going upstairs and going downstairs are collected. Secondly, difference filling, smooth filtering and other preprocessing are carried out on the collected data. Thirdly, feature construction is carried out, and the difference offeatures is increased through windowing, fast Fourier transform and a principal component analysis method. And finally, model training is conducted by using a random forest algorithm. Experimental results show that the model trained by adopting the random forest algorithm is high in classification precision because of other classification algorithms such as a support vector machine and a gradientboosting tree. And the average classification precision of the five states is 98.2%.

Description

technical field [0001] The invention relates to the field of personal navigation and positioning, and can be used in places where satellite navigation is not available, such as indoors, underground passages and other places. Background technique [0002] In the pedestrian positioning method based on inertial navigation technology, gait recognition technology has been widely used. A gait recognition model is trained through a large amount of data, so that it can determine which gait the pedestrian is in at the moment, and provide prior information for the next step of the attitude estimation algorithm, thereby improving the positioning accuracy. It is an important factor to determine the positioning accuracy. [0003] Traditional gait recognition algorithms only estimate gait by judging the magnitude of acceleration or angular velocity and the difference of data in different states. The recognition accuracy of this method is not high when used. Sometimes the difference in ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/11A61B5/00
CPCA61B5/112A61B5/7203A61B5/725A61B5/7257A61B5/7267A61B2562/0219
Inventor 仇潮汐刘洪谭晓乐
Owner XIDIAN UNIV
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