Electromyographic signal gesture recognition method based on hidden markov model

Abstract

The invention discloses an electromyographic signal gesture recognition method based on a hidden markov model. The method comprises the following steps of: executing smoothing filtering for electromyographic signals; extracting a multi-feature feature set for each window data through a sliding window, and executing normalization and feature dimension reduction of minimum redundancy maximum correlation criterion for feature vectors; designing three classes of hidden markov model classifiers, and optimizing parameters of the hidden markov model classifiers; obtaining classifier models through training with hidden markov classifier model parameters and training data; inputting test data into the models trained well, and according to likelihood output by each class of hidden markov model, determining that the class corresponding to the maximum likelihood is the recognized class. According to the method provided by the invention, three classes of common hidden markov model classifiers are recognized based on a new feature set. By application of a classification method based on the hidden markov model, different gestures of the same testee can be recognized accurately, and gestures of different testees can be relatively recognized accurately.

Description

technical field [0001] The invention belongs to the field of combining computers and biological signals, and specifically recognizes gestures corresponding to electromyographic signals based on a hidden Markov model. Background technique [0002] Friendly human-computer interaction interface has become one of the research hotspots in the field of information technology. In order for computers to better judge and understand human intentions, "integration of life, muscle and electricity" is one of the important development trends of human-computer interaction in the future. one. Surface electromyography (sEMG) signal is a one-dimensional voltage time series signal obtained by guiding, amplifying, displaying and recording the bioelectrical changes of the neuromuscular system during voluntary and involuntary activities. It has important academic value and application significance for sports science research, human-computer interaction, clinical and basic research of rehabilitat...

AI Technical Summary

Problems solved by technology

The Hidden Markov Model is very good at modeling time series data, and is very suitable as a classifier for EMG signal gesture recognition, but no Hidden Markov Model is used in the existing inventions to recognize EMG signal gestures

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