Biomimetic Self-Powered Smart Insole with AI-Enhanced Mechanodiagnosis for Continuous Gait Monitoring

Continuous gait analysis is essential for early detection and management of neuromuscular disorders, yet current wearable technologies face limitations in sensing capacity, energy autonomy, and real-time diagnostic capabilities, restricting their clinical adoption. Here, we present a biomimetic smart insole that synergizes nature-inspired sensing, self-sustaining energy harvesting, and artificial intelligence (AI) to enable continuous, clinically actionable gait monitoring. Mimicking the mechanosensory architecture of mantis legs, our dual-microstructure capacitive sensor achieves a sensitivity of 0.602 kPa ^⁻ ¹, a detection limit of 0.10 Pa, and a broad sensing range (0.10 Pa–1.40 MPa) with exceptional durability (>12,000 cycles), outperforming state-of-the-art wearable sensors. A custom-designed flexible circuit wirelessly streams 16-channel pressure data to a companion APP, providing real-time visualization of dynamic force fields through chromatic mapping. The system’s energy autonomy is ensured by a hybrid perovskite solar cell/lithium-sulfur battery, enabling continuous operation across diverse environments. An embedded AI framework combines a random forest classifier (96% accuracy in foot arch abnormality detection) with a convolutional neural network (97.6% accuracy in classifying 12 pathological gait patterns), translating raw sensor data into clinical insights. This platform bridges the gap between wearable sensing and precision diagnostics, offering transformative potential for early disease detection, personalized rehabilitation, and telemedicine, and thus establishing a paradigm for next-generation intelligent wearables in global healthcare.