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Electromyogram pattern recognition (EMG-PR) based control for upper-limb prostheses conventionally focuses on the classification of signals acquired in a controlled laboratory setting. In such a setting, relatively stable and high performances are often reported because subjects could consistently perform muscle contractions corresponding to a targeted limb motion. Meanwhile the clinical implementation...
In this study, we evaluated classification performance of electromyography (EMG) four time-domain features and autoregressive model features and their combination in identifying 11 classes of arm and hand movements in both able-bodied subjects and amputees. Our results showed that using three time-domain features could achieve similar classification accuracy as using four features. Using AR model...
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