A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion
نویسندگان
چکیده
Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method mode identification by fusing the electromyography data with two types from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From finite state machine perspective, was used systematically identify 2 static modes, 7 dynamic 27 among them. addition five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), identified transition walking speeds ramp at inclination angles. Seven combinations sensor fusion were conducted, on experimental 8 able-bodied adult subjects, their classification accuracy prediction time compared. Prediction based gyroscope (angular velocity) predicted earlier higher accuracy. All total average 94.5% fused data. For nearly all transitions, able predict next 300-500ms prior step into that mode.
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Systems and Rehabilitation Engineering
سال: 2021
ISSN: ['1534-4320', '1558-0210']
DOI: https://doi.org/10.1109/tnsre.2021.3087135