Gesture recognition method based on multichannel electromyographic signal correlation

An electromyographic signal and gesture recognition technology, applied in character and pattern recognition, electrical digital data processing, input/output process of data processing, etc. The effect of precision and easy operation

Inactive Publication Date: 2019-11-01
BEIHANG UNIV +1
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Problems solved by technology

[0007] Aiming at the problem that the current method cannot accurately recognize long gesture signals, the present invention provi

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  • Gesture recognition method based on multichannel electromyographic signal correlation
  • Gesture recognition method based on multichannel electromyographic signal correlation
  • Gesture recognition method based on multichannel electromyographic signal correlation

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Embodiment Construction

[0034] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be further described in detail and in-depth below in conjunction with the accompanying drawings.

[0035] A gesture recognition method based on multi-channel myoelectric signal correlation proposed by the present invention is mainly used for detecting and recognizing predefined gestures. The present invention uses LSTM (Long Short-Term Memory, long-term short-term memory network) unit to explain the nonlinear relationship between electromyographic signals and gestures, and experiments show that the model can accurately identify the gestures of hand movements. The method mainly includes: (1) EMG signal denoising processing: use empirical mode decomposition to decompose the collected EMG signal into several eigenmode functions, and distinguish the noise from the effective one by the statistical characteristics of the autocorrelation functi...

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Abstract

The invention provides a gesture recognition method based on multichannel electromyographic signal correlation. The gesture recognition method comprises the following steps: firstly, de-noising electromyographic signals acquired by each channel; detecting a movable section according to the signal amplitude intensity; then, performing structured processing on the active section signal; processing the signal into a format with time correlation by superposing a plurality of continuous time windows; and finally, realizing a hybrid neural network CRNet based on the CNN + RNN neural network, and establishing a classifier for gesture recognition, wherein the input of the classifier is a signal subjected to structured processing, and the output of the classifier is a gesture classification probability, and the trained classifier is utilized to perform gesture recognition. For the gesture recognition method, only a plurality of myoelectricity sensors are used for collecting original signals while extra complex equipment is not needed, so that operation is convenient, and environmental adaptability is good. According to the gesture recognition method, the noise in the signal can be effectively removed, and the used classifier reduces the computing resources and improves the recognition efficiency, and the gesture recognition method is more suitable for engineering application.

Description

technical field [0001] The invention relates to a gesture detection and recognition method based on multi-channel myoelectric sensor signals, belonging to the technical field of sensor signal processing and recognition. Background technique [0002] In recent years, the widespread application of embedded smart devices has promoted the development of human-computer interaction technology. Among them, gesture recognition plays an important role in human-computer interaction, and the work of controlling smart devices (such as drones, robots, etc.) through gestures has attracted more and more attention from relevant researchers. Gestures can express a wealth of information. There are as many as 30,000 gestures in various countries around the world, which brings great convenience to the communication between people and between people and machines. Especially for hearing-impaired people and silent interactions (such as tactical gesture commands), it highlights the unique advantag...

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Application Information

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IPC IPC(8): G06K9/00G06F3/01
CPCG06F3/015G06F2203/011G06F2218/06G06F2218/08G06F2218/12
Inventor 李辉勇武迪牛建伟谷飞
Owner BEIHANG UNIV
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