Abstract
In recent time we have witnessed a major push towards providing an online closed-loop control of upper-limb hand prostheses. Notwithstanding the substantial advances that have been made in developing invasive closed-loop systems, barriers remain in achieving suitable levels of non-invasiveness and a closed-loop non-invasively controlled prosthetic hand with lifelike dexterity is still missing. In this context, this work proposes a low-cost, non-invasive system for prosthetic hands control using surface EMG (sEMG) signals. We verified the system with 10 human participants. Using the proposed system: 1) four hand movements and two force levels (low, high) were successfully classified from sEMG signals; 2) a real-time control of the prosthetic hand was achieved using the decoded sEMG activity with an average online accuracy of 86.25% using solely 10 training trials per posture; 3) measured finger-tip forces were translated into vibro-tactile stimulation of the muscles for feedback and participants were able to differentiate between three different grasped objects (soft, hard and medium objects) with an average success rate of more than 88.46% across all 10 subjects; 4) the detection of muscle fatigue from sEMG was successfully performed. In sum, our results show the potential of using low cost and non-invasive approaches for closed-loop control of upper-limb hand prostheses.
Published Version
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