Experimental study demonstrates real-time hand motion tracking using a dual-network hydrogel strain sensor, highlighting its potential in machine-learning-assisted rehabilitation.
Wearable rehabilitation monitoring requires soft strain sensors with mechanical robustness, stable electromechanical responses, and intelligent motion recognition capability. Here, we report a poly(vinyl alcohol)/polyacrylamide (PVA/PAM) double−network hydrogel strain sensor for rehabilitation−oriented wearable monitoring. The hydrogel was prepared by ultraviolet ray (UV)−initiated acrylamide polymerization followed by freeze−thaw−induced PVA crystallization, forming a covalent PAM network interpenetrated with a physically crosslinked PVA network. The resulting hydrogel possessed a compact porous structure, improved stretchability, and stable deformation recovery. The optimized sensor exhibited a tensile strength of approximately 0.52 MPa, an elongation at break of approximately 480%, a response time of 0.12 s, and a recovery time of 0.17 s. It generated repeatable resistance signals under cyclic strain, finger bending, wrist motion, and grip training. Furthermore, the sensor enabled morse−code information transmission and support vector machine (SVM)−based recognition of rehabilitation−related hand states, including straight, bend, and clench. This work provides a soft hydrogel sensing platform for real−time rehabilitation−oriented hand motion, while morse−code encoding provides auxiliary assistance and an emergency communication function.
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Liu et al. (2026) studied this question.
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