Wearable and Ultra-Low-Power Fusion of EMG and A-Mode US for Hand-Wrist Kinematic Tracking

Hand gesture recognition based on biosignals has shown strong potential for developing intuitive human-machine interaction strategies that closely mimic natural human behavior. In particular, sensor fusion approaches have gained attention for combining complementary information and overcoming the limitations of individual sensing modalities, thereby enabling more robust and reliable systems. Among them, the fusion of surface electromyography (EMG) and A-mode ultrasound (US) is very promising. However, prior solutions rely on power-hungry platforms unsuitable for multi-day use and are limited to discrete gesture classification. In this work, we present an ultra-lowpower (sub- 50 mW) system for concurrent acquisition of 8 channel EMG and $\mathbf{4}$-channel A-mode US signals, integrating two state-of-the-art platforms into fully wearable, dry-contact armbands. We propose a framework for continuous tracking of $\mathbf{2 3}$ degrees of freedom (DoFs), $\mathbf{2 0}$ for the hand and $\mathbf{3}$ for the wrist, using a kinematic glove for ground-truth labeling. Our method employs lightweight encoder-decoder architectures with multitask learning to simultaneously estimate hand and wrist joint angles. Experimental results under realistic sensor repositioning conditions demonstrate that EMG-US fusion achieves a root mean squared error of $10.6^{\circ} \pm 2.0^{\circ}$, compared to $12.0^{\circ} \pm 1^{\circ}$ for EMG and $13.1^{\circ} \pm 2.6^{\circ}$ for US, and a $\mathbf{R}^{2}$ score of $0.61 \pm 0.1$, with $0.54 \pm 0.03$ for EMG and $0.38 \pm 0.20$ for US.