Gesture recognition method based on electromyographic topographic map

Abstract

The invention discloses a gesture recognition method based on an electromyographic topographic map. The method comprises the steps of (1) data collection, wherein upper arm muscle surface electromyographic signals of different gestures are collected through array surface electromyographic electrodes; (2) data preprocessing, wherein the collected surface electromyographic signals are preprocessed; (3) generation of the electromyographic topographic map; and (4) deep convolutional neural network model training and gesture recognition for generation of a feature image of the electromyographic topographic map, wherein the electromyographic topographic map is converted into a 64*64 grayscale image first, and then ZCA whitening preprocessing is used to generate the feature image; a corresponding convolutional neural network model structure is designed according to characteristics of the electromyographic topographic map, and a model is constructed; and test set data is input into a trained network model for gesture recognition classification. Through the method, the same gesture can be made to different subjects to generate similar electromyographic topographic maps, and therefore the problem of individual difference of surface electromyographic signals is effectively solved.

Description

technical field [0001] The invention belongs to the field of combining computers and biological signals, and in particular relates to a gesture recognition method based on an electromyographic topographic map. Background technique [0002] The recognition and perception of human motion intention based on physiological signals has become one of the research focuses in the field of human-computer interaction, that is, to digitize the physiological signals of organisms through specific sensing devices, and compare them with signals from other perception or cognitive channels. Integration and fusion can naturally and collaboratively complete various human-computer interaction tasks. [0003] Surface Electromyography (sEMG) is the electrical signal accompanying muscle contraction, and the change in activity can largely reflect the local fatigue, muscle strength level, muscle activation mode, The changes of muscle activity and central control characteristics such as motor unit ex...

AI Technical Summary

Problems solved by technology

At present, the traditional gesture recognition method based on sEMG generally has the following two problems: 1. Due to the individual differences of EMG signals, the trained classification model is often only applicable to the population that provides the training data, and not suitable for other populations; 2. A large number of feature extraction operations are often required

However, it has great shortcomings. First, it does not solve the above two problems, lacks consideration of the individual differences of EMG signals, and still extracts 10 features; second, this method still belongs to the traditional classification method, and based on it Added more image recognition operations

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