基于粒子群優(yōu)化-支持向量機(jī)方法的下肢肌電信號步態(tài)識別
doi: 10.11999/JEIT141083
基金項(xiàng)目:
浙江省自然科學(xué)基金(Y1101230, LY13F030017)，浙江省科技計(jì)劃(2012C33075, 2013C24016)和國家自然科學(xué)基金(61201302, 61172134)資助課題
Gait Recognition for Lower Extremity Electromyographic Signals Based on PSO-SVM Method
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摘要: 為提高下肢表面肌電信號步態(tài)識別的準(zhǔn)確性和實(shí)時(shí)性，該文提出一種基于粒子群優(yōu)化(PSO)算法優(yōu)化支持向量機(jī)(SVM)的模式識別方法。首先對消噪后的肌電信號提取積分肌電值和方差作為特征樣本，然后利用PSO算法優(yōu)化SVM的懲罰參數(shù)和核函數(shù)參數(shù)，最后利用步態(tài)動作的肌電信號樣本數(shù)據(jù)對構(gòu)造的SVM分類器進(jìn)行訓(xùn)練、測試。實(shí)驗(yàn)結(jié)果表明PSO-SVM分類器對下肢正常行走5個(gè)步態(tài)的識別率，明顯高于未經(jīng)參數(shù)優(yōu)化的SVM分類器，優(yōu)化后平均識別率達(dá)到97.8%，并兼顧了分類的準(zhǔn)確性和自適應(yīng)性。
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關(guān)鍵詞:
- 模式識別 /
- 步態(tài)分析 /
- 肌電信號 /
- 粒子群優(yōu)化 /
- 支持向量機(jī)
Abstract: To improve the lower limb surface ElectroMyoGraphic (EMG) gait recognition accuracy and real time performance, this paper deals with a pattern recognition method for optimizing the Support Vector Machine (SVM) by using the Particle Swarm Optimization (PSO) algorithm. Firstly, the values of Integrated EMG and variance are extracted as the feature samples from the de-noised EMG signals. Then, the SVM parameters of the punishment and the kernel function are optimized by PSO. Finally, the constructed SVM classifiers are trained and tested by using the EMG sample data of the gait movements. The experimental results show that for five normal walking gaits of the lower extremity, the recognition rate of the PSO-SVM classifier is significantly higher than that of the non-parameter-optimized SVM classifier, and the average recognition rate is up to 97.8%, as well as the classification accuracy and self-adaptability are also improved. -
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