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However, achieving stable signal acquisition under complex deformation remains a critical challenge for existing sensing technologies. Here, we report a fully stretchable triboelectric\u2013piezoelectric hybrid nanogenerator (S\u2010TPHNG) that enables deformation\u2010adaptive and dual\u2010modal self\u2010powered sensing under realistic biomechanical conditions. A compliant triboelectric nanocomposite interface is engineered by incorporating tin disulfide hierarchical nanostructures (SnS\n                    <jats:sub>2<\/jats:sub>\n                    HNSs) as interfacial polarization regulators, enabling stable harvesting of contact\u2010 and strain\u2010induced 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\u2010TPHNG enables contact\u2013separation sensing for human\u2013machine interaction applications and stretching\u2010mode sensing for muscle monitoring. When benchmarked against electromyography (EMG) measurements, the system achieves accurate, temporally resolved, and EMG\u2010correlated biomechanical signal interpretation under dynamic conditions, supported by a deep learning\u2010based decoding framework. This work establishes a deformation\u2010adaptive, mechanism\u2010informed self\u2010powered sensing strategy, integrating interfacial electromechanics, hybrid sensing, and artificial intelligence (AI)\u2010assisted signal decoding into a wearable biomechanical sensing platform.\n                  <\/jats:p>","DOI":"10.1002\/smll.75415","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T12:56:25Z","timestamp":1787835385000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deformation\u2010Adaptive Hybrid Triboelectric\u2013Piezoelectric Nanogenerator for Wearable Muscle Monitoring and Human\u2013Machine Interaction"],"prefix":"10.1002","volume":"22","author":[{"given":"Shih\u2010Min","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Biomedical Sciences and Engineering National Central University  Taoyuan 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