Fig. 4: Multimodal physiological sensing decoupled via a machine learning model.

a Schematic illustration of the AMSG wristband interface for multimodal sensing of body temperature, pulse rate, and sweat secretion, simulated by individually applying temperature, pressure, and Na⁺ electrolyte stimuli to obtain their characteristic signals. b Architecture of the machine learning-based signal decoupling model, featuring three LSTM layers with local attention and three fully connected layers for extracting and decoupling the signal features of each stimulus independently. c Internal structure of the LSTM network. d Comparison of compound signals under multiple simultaneous stimuli and their decoupled components. e Multimodal monitoring of body temperature, pulse rate, and sweat Na⁺ concentration across various activity states (relax, walk, run, sleep, fever), benchmarked against commercial devices. f User interface displaying the real-time waveforms of multimodal signals and corresponding monitoring outputs.