<p>Posture recognition technology plays a crucial role in health monitoring, motion analysis, and other related fields. However, traditional recognition devices are limited by a lack of comfort, stability, and environmental adaptability, which significantly restrict their performance and range of applications. In response, this study proposes a self-powered, strain-driven, intelligent posture recognition method based on a thermoelectric hydrogel. This approach enables high-precision posture recognition through simultaneous acquisition of dynamic strain signals from multiple joints. The proposed poly(vinyl alcohol) (PVA)/starch bi-network hydrogel, synthesized in a binary H₂O/glycerol solvent system, exhibited outstanding flexibility and tensile strength, allowing it to conform closely to joint movements and ensure wearing comfort. Simultaneously, the hydrogel generated stable electrical signals <i>via</i> the redox reaction of [Fe(CN)₆]^3−/4−, supporting continuous monitoring and data collection. Moreover, its self-powered nature allows effective joint strain signal acquisition, even in open environments. Using a machine learning algorithm that incorporates signal processing and analysis, the proposed method achieved a posture recognition accuracy of 96.82%. This study presents a novel technological route for posture recognition in wearable devices, demonstrating its strong application potential in real-time feedback for sports rehabilitation and performance analysis of athletes.</p>