LAUSR

Electromyographic (EMG) signals sensed at the skin surface on the forearm can be used to accurately infer wrist-hand poses. However this is only possible when the EMG sensors are carefully placed over specific arm muscles. This cannot be guaranteed for wearable devices, which could acquire EMG from anywhere on the forearm. As a result, these devices detect fewer poses, less accurately. In addition, the complexity of the time-frequency analysis used in placed-sensor systems precludes real-time detection using the simple embedded processors on EMG wearables. This paper describes an approach which resolves both these shortcomings. It shows that, when random sensor placement is adopted, wrist-hand movement detection with performance equal the state-of-the-art can be achieved, with only 10% of the computational complexity. This latter property allows the first real-time wrist-hand movement detector using only simple embedded processors; specifically when using on ARM Cortex-A53 processor, execution time is lowered by 90% against the state-of-the-art, with no reduction in detection performance. It is shown how this can be further reduced by 30% by using fewer EMG channels or features, whilst maintaining good detection performance. To the best of the authors’ knowledge, this is the first record of real-time high-performance wrist-hand movement detection for standalone, battery-powered EMG wearables.