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Gesture classification recognition method and application thereof

A classification recognition and gesture technology, applied in the field of data classification, can solve problems such as low recognition accuracy, long training time, and poor robustness, and achieve the effects of improving robustness, improving accuracy, and reducing waste

Pending Publication Date: 2021-08-27
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

[0007] Based on some current gesture classification and recognition algorithms for sEMG signals, the recognition accuracy is low, and there will be problems of overfitting and underfitting, gradient disappearance, poor robustness, and long training time during the training model process. This application provides A gesture classification recognition method and its application

Method used

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  • Gesture classification recognition method and application thereof
  • Gesture classification recognition method and application thereof
  • Gesture classification recognition method and application thereof

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Experimental program
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Embodiment

[0042] The application provides a gesture classification method based on the GRU model of surface electromyography signal acquisition, and the specific implementation steps include the following steps:

[0043] Step 1: Collect the surface EMG signals of different gestures of the subjects

[0044] Use alcohol to wipe the experimental equipment and the skin surface of the test subject to avoid the interference of the noise signal brought by the skin epidermis and oil. On the forearm, real-time collection of muscle signal changes brought about by the gestures of different arms, the experimental device is shown in the figure figure 1 shown. During the experiment, the application collects various gestures. Moreover, in order to reduce the error caused by the subject's muscle fatigue during the collection process, various gestures in this application were repeated 6 times, each gesture lasted for 5 seconds, and each gesture was rested for 5 seconds. The collected sEMG signals are...

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Abstract

The invention belongs to the technical field of data classification, and particularly relates to a gesture classification recognition method and application thereof. However, some current gesture classification recognition algorithms about sEMG signals are low in recognition accuracy, and over-fitting and under-fitting, gradient disappearance, poor robustness and long training time also exist in a model training process. The invention provides a gesture classification recognition method. The method comprises the following steps: acquiring a surface electromyogram signal; performing feature extraction on the surface electromyogram signal to obtain a gesture feature sequence and a gesture type; and inputting the gesture feature sequence and the gesture type into a circulation gate circuit neural network for training to obtain a classification model, and adopting the classification model to realize gesture classification recognition. And the prediction classification accuracy is improved.

Description

technical field [0001] The present application belongs to the technical field of data classification, and in particular relates to a gesture classification and recognition method and its application. Background technique [0002] With the development of science and technology, new methods of human-computer interaction have attracted more and more attention from researchers. As a natural and intuitive means of interaction in life, gesture is a very important perception channel in human-computer interaction. In human-computer interaction, identifying the types of gestures that can interact with computers is of great significance for the current research on the control of prosthetics. [0003] Compared with the way of realizing human-computer interaction through computer vision, current research using surface electromyography (sEMG) can effectively avoid the influence of physical factors such as light. The sEMG signal is an important bioelectrical signal generated with the mo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/28G06N3/045G06F18/24G06F18/253
Inventor 郭伟钰杨永魁陈瑞陈超辛锦瀚王峥
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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