Plantar temperature and pressure reflect the plantar health status of diabetic patients and serve as critical indicators for evaluating disease progression. However, existing plantar monitoring devices are still limited by single-modal signal acquisition, bulky device configurations, and a strong reliance on external power supplies. Herein, we report a thermogalvanic hydrogel-based smart insole integrating self-powered sensing units for self-decoupled plantar monitoring. The insole integrates 16 self-decoupled thermogalvanic hydrogel sensing units, which convert plantar temperature difference and pressure stimuli into decouplable electrical signals via the thermogalvanic effect and the active iontronic piezoresistive effect, respectively. The incorporation of the backend acquisition circuit into the smart monitoring insole enables remote plantar temperature-difference and pressure mapping. Cloud-based machine learning further empowers the insole to assess plantar status in diabetic patients and distinguish disease-associated gait patterns. This work provides a versatile strategy for developing multimodal intelligent wearable platforms for personalized foot health monitoring and disease management.
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Niu et al. (2026) studied this question.
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