Deformation-Adaptive Hybrid Triboelectric-Piezoelectric Nanogenerator for Wearable Muscle Monitoring and Human-Machine Interaction.

Wearable systems for real-time and long-term musculoskeletal monitoring are increasingly required as shoulder-neck disorders and upper-arm injuries become more prevalent in aging and sedentary populations. However, achieving stable signal acquisition under complex deformation remains a critical challenge for existing sensing technologies. Here, we report a fully stretchable triboelectric-piezoelectric hybrid nanogenerator (S-TPHNG) that enables deformation-adaptive and dual-modal self-powered sensing under realistic biomechanical conditions. A compliant triboelectric nanocomposite interface is engineered by incorporating tin disulfide hierarchical nanostructures (SnS2 HNSs) as interfacial polarization regulators, enabling stable harvesting of contact- and strain-induced mechanical stimuli. Kelvin probe force microscopy (KPFM) and density functional theory (DFT) calculations suggest that interfacial polarization modulation and charge redistribution contribute to enhanced electrical performance. Leveraging these insights, the S-TPHNG enables contact-separation sensing for human-machine interaction applications and stretching-mode sensing for muscle monitoring. When benchmarked against electromyography (EMG) measurements, the system achieves accurate, temporally resolved, and EMG-correlated biomechanical signal interpretation under dynamic conditions, supported by a deep learning-based decoding framework. This work establishes a deformation-adaptive, mechanism-informed self-powered sensing strategy, integrating interfacial electromechanics, hybrid sensing, and artificial intelligence (AI)-assisted signal decoding into a wearable biomechanical sensing platform.