Dynamic Bonding for In-Situ Welding of Multilayer Elastomers Enables High-Performance Wearable Electronics for Machine Learning-Assisted Active Rehabilitation.

Patients with hand dysfunction require joint rehabilitation for functional restoration, and wearable electronics can provide physical signals to assess and guide the process. However, most wearable electronics are susceptible to failure under large deformations owing to instability in the layered structure, thereby weakening signal reliability. Herein, an in-situ self-welding strategy that uses dynamic hydrogen bonds at interfaces to integrate conductive elastomer layers into highly robust electronics is proposed. This strategy enables the interlocking of functional layers with different microstructures, achieving high interfacial toughness (e.g., ≈700 J m for micropyramid layer with the smallest welding areas) and preventing structural failure. The welded electronics exhibit excellent pressure-sensing performance, including high sensitivity, a wide sensing range, and excellent long-term stability, surpassing those of the unwelded electronics. This enables a reliable collection of comprehensive pressure signals during joint rehabilitation, which is beneficial for assessing the rehabilitation levels of a patient. Furthermore, a machine learning-assisted system using t-distributed stochastic neighbor embedding and artificial neural network models to facilitate home-based active rehabilitation is established, which reduces the need for frequent hospital visits. This system analyzes and quantifies rehabilitation levels in a timely manner, allowing patients to adjust training programs autonomously, thereby accelerating the rehabilitation process.