Surface Electromyography Based Finger Flexion Recognition

Abstract

Great effort is being taken since the last decade to make control interfaces for machines and robots less complicated and more natural, by taking signals directly from the body. This requires highly accurate signal interpretation. Researches have shown that electromyographic signals obtained from skeletal muscles can give very accurate information regarding limb movements, that can be used in human robot interface. In this paper, a novel approach toward classifying finger movements using surface Electromyography (sEMG) is discussed. Higuchi fractal dimensions and auto-regressive modeling are employed for extracting suitable features from a 6-channel EMG signal acquired indigenously using self-designed data acquisition hardware and classified using Multi Class Support Vector Machine. An average accuracy of 95.2% has been obtained.

Development of finger movement assistive gloves with pneumatic fabric actuators

“…The sampling rate of the EMG signals was set to 2000 Hz, and all the collected EMG signals were root-mean-square processed. The EMG sensors were attached to four healthy male participants following the guidelines in extant research [6,44]. In addition, the number of participants in this study met the one to three participants generally required for EMG measurements [6,44,45], and the one to four participants required for angle and grip strength measurements [46][47][48].…”