Channel selection method for surface electromyography signal based on LDA algorithm

A technology of myoelectric signal and channel, applied in the field of pattern recognition

Inactive Publication Date: 2019-12-06
SOUTH CHINA UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

In the research and application of pattern recognition of EMG signals, there are not many studi

Method used

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  • Channel selection method for surface electromyography signal based on LDA algorithm
  • Channel selection method for surface electromyography signal based on LDA algorithm
  • Channel selection method for surface electromyography signal based on LDA algorithm

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Embodiment

[0039] Such as figure 1 As shown, the channel selection method of the surface electromyographic signal based on the LDA algorithm comprises the following steps:

[0040] Step S1. Use alcohol to clean the six parts of tibialis anterior muscle, peroneus longus muscle, soleus muscle, outer gastrocnemius muscle, inner gastrocnemius muscle on the inner side of the calf, and inner gastrocnemius muscle on the back side of the calf, and fix them on the six parts Surface electromyography electrodes, the above 6 parts are 6 to-be-selected myoelectric signal channels.

[0041]Step S2, the subject sits on the chair, the thigh and calf are supported accordingly; only the ankle joint is moved, and the five action modes of relaxation, dorsiflexion, plantarflexion, valgus and varus are performed, and the surface electromyography sensor is used to The myoelectric electrode collects the myoelectric signal data of each action mode of each myoelectric signal channel and saves it to the computer ...

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Abstract

The invention discloses a channel selection method for surface electromyography signal based on an LDA algorithm. The method comprises the steps of selecting different combinations from six alternative electromyographic signal channels including tibialis anterior muscle, peroneus longus and soleus, outer gastrocnemius muscle, inner gastrocnemius muscle of medial leg and inner gastrocnemius muscleof posterior leg, for quantitative electromyographic signal channel combinations; calculating the average recognition rate of five action patterns of ankle including relaxation, dorsal flexion, plantar flexion, eversion and enstrophe, for each channel combination by using an accuracy calculation algorithm based on the LDA algorithm; and obtaining an optimal channel combination through comparing the average recognition rates of all the channel combination. The optimal electromyographic signal channel combination is determined through recognition rate calculation based on the LDA algorithm. Compared to a method of determining a channel combination by experience, the channel selection method disclosed by the invention is more scientific and accurate, and can recognize the five action patternsof ankle with a high recognition rate by using less electromyographic signal channels.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to a channel selection method for surface electromyographic signals based on an LDA algorithm. It can be used for selection of myoelectric electrode positions in ankle action pattern recognition or selection of myoelectric electrode positions for other action modes. Background technique [0002] Surface electromyography (SEMG), referred to as surface electromyography or electromyography, is a bioelectric signal generated during muscle activity collected by surface electrodes. When the ankle is about to make corresponding movements, the muscles of the calf will generate action potentials to induce muscle contraction, and these signals can be detected by the surface electrodes on the skin surface through the conduction of the subcutaneous tissue. The surface electromyographic signal contains the information of the corresponding action, and the detection of the surface electromyogra...

Claims

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Application Information

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IPC IPC(8): A61B5/0488A61B5/11A61B5/00G06K9/62G06K9/00
CPCA61B5/1118A61B5/72A61B5/316A61B5/389G06F2218/08G06F18/214
Inventor 王念峰张新浩张宪民
Owner SOUTH CHINA UNIV OF TECH
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