A Dual-Mode, Scalable, Machine-Learning-Enhanced Wearable Sensing System for Synergetic Muscular Activity Monitoring

Abstract

Simultaneously detecting muscular deformation and biopotential signals provides comprehensive insights of the muscle activity. However, the substantial size and weight of detecting equipment result in reduced wearer benefits and comfort. It remains a challenge to establish a flexible and lightweight wearable system for mapping muscular morphological parameters while collecting biopotentials. Herein, a fully integrated dual-mode wearable system for monitoring lower-extremity muscular activity is introduced. The system utilizes an iontronic pressure sensing matrix (16 channels) for precise mapping of force myography (FMG) within a single muscle, while simultaneously capturing the muscular electrophysiological signals using a self-customized electromyography (EMG) sensing module. Experimental results show that the bimodal sensing system is capable of capturing complementary and comprehensive aspects of muscular activity, which reflect activation and architectural changes of the muscle. By leveraging machine learning techniques, the integrated system significantly (p < 0.05) enhances the average gait phase recognition accuracy to 96.35%, and reduces the average ankle joint angle estimation error to 1.44°. This work establishes a foundation for lightweight and bimodal muscular sensing front-ends, which is promising in applications of human–machine interfaces and wearable robotics.