1. Background:
With the increasing aging of the population, the high incidence of chronic diseases, and the increasing number of congenital or acquired foot deformities, lower limb dysfunction and abnormal gait problems are becoming more common, posing a significant threat to public health and quality of life. Gait analysis is widely considered a sensitive biomechanical index to assess lower extremity function, disease progression, and rehabilitation efficacy. However, existing clinical gait assessment is mainly based on laboratory equipment such as visual motion capture systems and force platforms, which are not only expensive and spatially limited, but also fail to reflect natural movement in real scenarios.
Pressure-sensing wearable insoles offer a decentralized and continuous new approach to gait monitoring, but existing technologies still face three major barriers to clinical translation: first, sensors struggle to simultaneously achieve ultra-low pressure resolution and high load tolerance, making it difficult to cover the full biomechanical range of the sole. Second, the power supply is based on traditional batteries, resulting in insufficient battery life and frequent charging, which prevents the continuity of long-term monitoring. Third, the large-scale spatiotemporal pressure data collected lacks effective intelligent analysis and real-time feedback, limiting its application in disease control and clinical decision making. Therefore, the development of a wearable gait monitoring system that integrates high-precision detection, autonomous power supply, and intelligent diagnosis is of great scientific importance and clinical value.
2. Research Progress:
This study reports a biomimetic smart insole system that, through multidisciplinary collaborative design, achieves high-resolution plantar pressure sensing, energy self-sufficiency, and intelligent AI-assisted gait diagnosis. Inspired by the hierarchical mechanosensory structure of the mantis leg, the research team designed a dual-microstructure capacitive pressure sensor, combining microstructured PDMS with compressible elastic foam. This achieves an extremely low detection limit of 0.10 Pa, a wide detection range up to 1.4 MPa and maintains excellent mechanical stability over 12,000 loading cycles, significantly outperforming existing flexible pressure sensors and fully meeting the requirements for insole applications.
As for the energy system, the smart insole integrates a perovskite solar cell and a high-energy-density lithium-sulfur nanobattery, building an adaptive closed-loop energy supply system. It can work stably under various indoor and outdoor lighting conditions, with an average light charging efficiency of 11.21% and energy storage efficiency of 72.15%, effectively dealing with energy congestion for long-term continuous operation of mobile devices.
At the data processing level, the system collects plantar spatiotemporal pressure distribution via a 16-channel wireless unit and incorporates artificial intelligence algorithms for real-time analysis. Based on a random forest model, the system can achieve 96.0% accuracy in arc anomaly detection. based on a one-dimensional convolutional neural network (1D-CNN), it can classify 12 pathological gait patterns with 97.6% accuracy. The companion mobile app intuitively presents the dynamic force field distribution via color maps, providing interpretable and real-time decision support for clinicians and rehabilitation personnel.
3. Future prospects
By in-depth integration of high-precision biomimetic sensing, sustainable energy interconnects, and smart mechanical diagnostics, this research has constructed a clinically validated closed-loop portable platform, providing a new technological pathway for early detection of lower extremity diseases, personalized rehabilitation training, and remote medical monitoring. This demonstrates the broad prospects for transforming smart wearables into clinical-grade diagnostic tools.
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