A machine learning-enabled planar interdigitated thermogalvanic hydrogel for synergistic thermal and strain sensing

Thermal and strain recognition is a promising modality for wearable electronics and human–machine interfaces, taking advantage of its natural, efficient, and user-distinctive characteristics. However, current sensing materials are typically limited by structural rigidity, functional constraints, and significant external power dependency, hindering their practical applications. This work proposes a unique strain–thermal sensing mechanism based on a planar interdigitated thermogalvanic hydrogel (PITH), integrating MXene interdigitated electrodes with a polyacrylamide (PAAm)– Fe(CN)₆^3−/4− hydrogel. Operating under a temperature gradient, this self-powered device responds to hand-writing motions, thereby enabling real-time capture of subtle writing dynamics. Distinct from resistive strain-based designs, the PITH employs a strain–thermal coupling mechanism, where mechanical strain modulates the hydrogel's redox kinetics, thereby influencing thermoelectric voltage output and establishing a direct strain–voltage correlation. By applying a random forest algorithm to process dynamic voltage signals, discriminative features are extracted, achieving high recognition accuracy (99.09% for letters, 97.85% for digits) under entirely passive operation. This study presents a structurally integrated, fully self-powered, and signal-programmable flexible handwriting recognition device, which is important for developing next-generation low-power wearable HMI systems.