Surface electromyogram signal identification method based on LDA algorithm

A technology of myoelectric signal and recognition method, which is applied in the field of pattern recognition and can solve problems such as the decline of recognition rate

Inactive Publication Date: 2013-12-11
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

It can be seen that when there are fewer gesture categories, the recognition rate

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  • Surface electromyogram signal identification method based on LDA algorithm
  • Surface electromyogram signal identification method based on LDA algorithm
  • Surface electromyogram signal identification method based on LDA algorithm

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Embodiment Construction

[0054] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0055] Now, taking the eight types of grasping gestures to be recognized as examples, combined with the technical solution proposed by the present invention, detailed operation steps and specific recognition results are given.

[0056] Step 1. Clean the skin, scrape off the hairs on the epidermis of the selected muscle, wash it with clean water and wipe the skin with a cotton swab dipped in medical alcohol. According to the muscle distribution of the human forearm, select the corresponding muscle. Because the eight types of grasping gestures mainly involve the flexion and extension of the thumb and the remaining four fingers, the flexor hallucis longus and flexor digitorum superficialis were selected as the source of surface electromyographic signal collection;

[0057] Step 2: Collect the surface electromyographic signals of the subject's for...

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Abstract

The invention discloses a surface electromyogram signal identification method based on an LDA algorithm. The surface electromyogram signal identification method is used for identifying up to eight kinds of grabbing gestures. According to the surface electromyogram signal identification method, only two electromyogram electrodes are utilized to collect surface electromyogram signals of corresponding gestures from related muscles of a forearm of a tester at first, then original electromyogram signals are segmented in an overlapping windowing mode, and absolute mean values, variances and 4-order AR coefficients are extracted from various windows to serve as original electromyogram characteristics; the LDA algorithm is utilized to carry out dimensionality reduction on the original electromyogram characteristics, redundant information is removed to the maximum degree, useful information is kept, and characteristics after the dimensionality reduction are obtained; the mean value of dimensionality reduction characteristics of front and back adjacent windows is computed and is inputted to an LDA classifier, and effective identification for the eight kinds of grabbing gestures is achieved. According to the surface electromyogram signal identification method, the electromyogram signal identification rate for the various kinds of gestures is high, the whole signal processing process is simple in computation and low in time consumption, and the requirement for the real-time performance of an electromyogram control system is met.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to the judgment and recognition of surface myoelectric signals in the case of multiple types of grasping gestures, which can be applied to control myoelectric prosthetic hands and other human-computer interaction interfaces. Background technique [0002] Surface electromyography (sEMG) is a bioelectrical signal related to neuromuscular activity. When the motor command is transmitted to the relevant muscle fibers through the nervous system, it will cause a change in the potential on the muscle fiber and the contraction of the muscle fiber. Electrodes are collected. The surface EMG signal contains information such as the mode and strength of muscle contraction. Different body movements correspond to different EMG signals. By analyzing the surface EMG signal, the specific action pattern corresponding to the signal can be identified. Therefore, surface myoelectric signals are widely...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 王念峰陈雨龙张宪民
Owner SOUTH CHINA UNIV OF TECH
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