A Triboelectric Smart Insole for Capturing Complex Stress on Plantar Surface

Diabetic foot ulcers (DFUs) are serious concerns for people with diabetes, and more than 15% of diabetes patients are expected to develop a DFU during their lifetime, which creates a significant economic burden and impacts on patients’ quality of life. However, current clinical assessment of DFU risk suffers from subjectivity and delayed detection, lacking early warning and intervention capabilities, thus limiting the effectiveness of treatment. Studies have demonstrated that quantifying compressive and shear stresses within the gait process offers a superior approach for assessing DFU risk. Inconveniently, current gait analysis devices exhibit limitations in terms of comfort, continuous monitoring, privacy concerns, and long-term stability, with a critical absence of shear stress detection capability. In this paper, we introduce a real-time triboelectric smart insole for gait analysis, featuring a triboelectric smart textile array in high-stress foot regions, and a flexible printed circuit (FPC) for signal transmission. The electrical signals, generated during gait movements, are transmitted via stainless steel yarns to the FPC, which subsequently communicates these signals to the users using low-power consumption bluetooth. The device’s fully flexible design enhances comfort and durability for prolonged wear and patients’ gait monitoring. Furthermore, we have successfully integrated an interlocked hill structure, inspired by mammalian skin, into the triboelectric smart textile. This innovation helps focus and amplify localized stress, enabling rapid acquisition of external stimulus information with high sensitivity (0.05V/kPa), fast response time (less than 35ms, comparable to human skin), and accurate sensing of a wide stress range (1-200kPa). The compressive and shear stress cues obtained from the triboelectric smart textile are key parameters for DFU risk assessment, and assisted rehabilitation monitoring, providing a potential solution for low-cost, energy-efficient, and universally applicable smart healthcare systems.