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Gesture recognition method based on BP neural network

A BP neural network and gesture recognition technology, applied in the field of gesture recognition based on BP neural network, to achieve the effect of good versatility, saving time consumption and saving memory consumption

Inactive Publication Date: 2017-01-04
张文栋
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the limitations of usage scenarios and small devices, traditional human-computer interaction devices, such as keyboards and mice, can no longer meet people's needs

Method used

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  • Gesture recognition method based on BP neural network
  • Gesture recognition method based on BP neural network
  • Gesture recognition method based on BP neural network

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Embodiment 2

[0102] On the basis of the above-described embodiments, the present embodiment possesses 8 acquisition channels with the electromyographic signal acquisition equipment, adopts 4 kinds of methods to carry out normalization processing, and extracts multiple features including the absolute mean value (MAX) and N of the electromyographic signals. Ratio of absolute mean value (R_MAV), root mean square (RMS), root mean square ratio (R_RMS), zero-crossing point (ZC), waveform length (WL) and symbol slope change rate (SSC) 7 features between acquisition channels Take an example to explain in detail.

[0103] 1. Use 9 samples, 8 channels, and 7 gestures. Each sample has 10240 discrete time series for each gesture. Normalize according to the 4 methods mentioned above. Each gesture forms a discrete time series of size 8*92160. Extract two eigenvalues ​​of absolute mean and waveform length to form an eigenvalue matrix, then carry out BP neural network model training, and then use 9 sample...

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Abstract

The invention relates to a gesture recognition method based on a BP neural network. The method includes the steps that electromyographic signals generated by multiple gesture actions of multiple samples are collected; the electromyographic signals are subjected to normalization; multiple feature values are extracted from the normalized electromyographic signals to form a feature value matrix; model training is conducted on the feature value matrix through the BP neural network algorithm to form a BP neural network model; the BP neural network model is saved in an electromyographic signal collection device for gesture recognition. During signal analysis and mode recognition, the improved normalization method effectively eliminates difference between electromyographic signals of different people, mode recognition is conducted through a BP neural network classifier, the gesture recognition rate is increased, and meanwhile the error probability in recognizing is greatly reduced.

Description

technical field [0001] The invention relates to the preprocessing, feature extraction and pattern recognition algorithm design of electromyographic signals in gesture recognition solutions, in particular to a gesture recognition method based on BP neural network. Background technique [0002] With the rapid development of microelectronics technology, sensor technology and computer technology, handheld mobile devices, wearable devices and microcomputers have gradually become popular in people's daily life. However, due to the limitations of usage scenarios and small devices, traditional human-computer interaction devices, such as keyboards and mice, can no longer meet people's needs. A mobile miniaturized gesture recognition device is proposed as a new type of human-computer interaction device. [0003] Since the muscle electrical signal was used in the field of control in 1945, it has experienced nearly a hundred years of development and has been researched and applied in t...

Claims

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Application Information

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IPC IPC(8): G06F3/01G06K9/62G06N3/04G06N3/08
CPCG06F3/015G06N3/084G06F2203/011G06N3/045G06F18/241
Inventor 李献红李玮琛刘汉成
Owner 张文栋
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