Conformal Phase-Transition Hydrogel Interfaces for High Fidelity Electrophysiological Sensing and Data-Driven Inference

While gelatin-based conductive hydrogels can acquire electrophysiological signals over multiple days, the statistical consistency and analytical utility of these long-term recordings for data-driven interpretation remain inadequately assessed. To address this, we developed a gelatin-quaternary ammonium chitosan (GT-QCS) hydrogel electrode that leverages a rapid, temperature-triggered sol–gel transition. Its fluid precursor conforms to complex skin topographies, forming a strongly adhesive interface within two minutes. The ionically crosslinked network shows high stretchability (B400% strain), tissue-matched modulus (B73 kPa), strong adhesion (544.1 mN cm1 ), breathability (WVTR E 605 g m2 day1 ), and low dehydration (B13% water loss after 30 days). This combination enables stable, week-long acquisition of high-fidelity sEMG, ECG, and EEG signals. The utility of these signals for data-driven analytics was quantitatively validated through a convolutional neural network, which achieved high accuracy in gesture recognition using the long-term sEMG data. Furthermore, the electrode-skin impedance and EEG signal fidelity remained stable over a seven-day period, outperforming standard conductive paste that typically dries within hours. This work demonstrates how phase-transition-enabled hydrogel electrodes can bridge material design with data-driven physiological analysis, offering a general approach for intelligent wearable bioelectronics.