Bioinspired shark skin-based liquid metal triboelectric nanogenerator for self-powered gait analysis and long-term rehabilitation monitoring. (15th December 2022) - Yeh, Cheng; Kao, Fu-Cheng; Wei, Po-Han; Pal, Arnab; Kaswan, Kuldeep; Huang, Yan-Tsz; Parashar, Parag; Yeh, Hsuan-Yu; Wang, Ting-Wei; Tiwari, Naveen; Tsai, Tsung-Ting; Huang, Yu-Fen; et al.
Abstract: Real-time gait monitoring is crucial for neuromuscular diagnosis and rehabilitation of neuromuscular ailments. However, the existing wearable sensors for gait analysis suffer from several drawbacks, such as external power requirement, poor sensitivity, short-term stability, and professional operator requirement, which restrict their applicability outside clinical settings. Here, a next-generation self-powered solid-liquid triboelectric nanogenerator-based flexible wearable sensor is developed. The proposed sensor is composed of a highly resilient liquid metal encapsulated within an innovative bio-mimicked shark skin-like microstructure embedded on the Ecoflex surface. The unique surface morphology imparts hydrophobicity to the solid triboelectric layer, which prevents the liquid metal adhesion during sensing and facilitates highly sensitive real-time monitoring of signals and long-term stability. The as-designed low-cost, highly scalable self-powered sensor, which is also compatible with diverse detection strategies, provides an on-demand user-friendly point-of-care gait detection and rehabilitation monitoring system with significant applications in personalized health care and sports science. Graphical Abstract: Bioinspired shark skin-based liquid metal triboelectric nanogenerator array has been developed and demonstrated for self-powered gait analysis and long-term rehabilitation monitoring. ga1 Highlights: Bioinspired shark skin-based liquid metal triboelectric nanogenerators developed for self-powered gait phase monitoring. The biomimetic shark skin-like structure prevents the adhesion of liquid metal and facilitates long-term stability. The time-ratio-based diagnostic model is capable of distinguishing gait disorders originating from traumatized muscles. The applicability of the proposed sensor array for real-time gait analysis and rehabilitation monitoring is also validated.