Deep-learning-assisted thermogalvanic hydrogel fiber sensor for self-powered in-nostril respiratory monitoring

Direct and consistent monitoring of respiratory patterns is crucial for disease prognostication. Although the wired clinical respiratory monitoring apparatus can operate accurately, the existing defects are evident, such as the indispensability of an external power supply, low mobility, poor comfort, and limited monitoring timeframes. Here, we present a self-powered in-nostril hydrogel sensor for long-term non-irritant anti-interference respiratory monitoring, which is developed from a dual-network binary-solvent thermogalvanic polyvinyl alcohol hydrogel fiber (d = 500 μm, L=30 mm) with Fe