Wearable High-Density sEMG Processing with Class Activation Maps with an Embedded Temporal Convolutional Network

High-density surface electromyography (HD-sEMG) holds promise for advancing intuitive and non-invasive human-machine interfaces (HMIs) due to its higher spatial resolution compared to sparse sEMG. We address the challenge of integrating the HD-sEMG's data volume with embedded computing to enable fully wearable control systems. We target HD-sEMG data from the wearable EMaGer 4 × 16 sensors system, with a Temporal Convolutional Network (TCN) that processes all channels and yields (94.7 ± 1.2)% accuracy on 6 classes. Moreover, we analyze the TCN's 4 × 16 Class Activation Maps with an approach inspired by computer vision, showing that channel relevance has a rowwise behavior not shown before. We deploy our TCN on the parallel ultra-low-power GAP9 MCU, consuming only 28.7 mW, 13.7 uJ per inference, and 475 us inference computation time, compatible with the constraints of real-time processing. Our work advances the SoA in the integration of HD-sEMG and embedded computing for wearable HMIs by combining wearable HD acquisition and embedded inference.