Comput Methods Programs Biomed - Feature extraction of the first difference of EMG time series for EMG pattern recognition.

Tópicos

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Resumo

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2-8%.

Resumo Limpo

paper demonstr util differenc techniqu transform surfac emg signal measur static dynam contract becom stationari techniqu evalu three stationar test consist variat two statist properti ie mean standard deviat revers arrang test result propos techniqu first differ emg time seri becam stationari compar origin measur signal base find perform timedomain featur extract raw transform emg investig via emg classif problem ie eight dynam motion four emg channel data subject result show classif accuraci featur extract transform signal higher featur extract origin signal six differ classifi includ quadrat discrimin analysi averag propos differenc techniqu improv classif accuraci

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