1. Background:
With the increasing aging of the population, high incidence of chronic diseases, and increasing number of congenital or acquired foot deformities, lower limb dysfunction and abnormal gait problems are becoming increasingly common, posing a serious threat to public health and quality of life. Gait analysis is widely considered as a sensitive biomechanical index to assess lower extremity function, disease progression, and rehabilitation effectiveness. However, existing clinical gait assessments mainly rely on laboratory equipment such as optical motion capture systems and force platforms, which are not only expensive and spatially constrained, but also fail to reflect natural movements in real-world scenarios.
Wearable pressure-sensing insoles offer a new decentralized and continuous approach to gait monitoring, but existing technologies still face three major bottlenecks in clinical applications. First, sensors struggle to simultaneously achieve ultra-low pressure resolution and high load tolerance, making it difficult to cover the entire biomechanical range of the sole of the foot, from subtle postural adjustments to severe impacts. Second, the energy supply relies on traditional batteries, resulting in insufficient battery life and frequent charging, which prevents continuous long-term monitoring. Third, the collected large-scale spatiotemporal pressure data lacks effective intelligent analysis and real-time feedback, limiting its application in disease screening and clinical decision-making. Therefore, the development of a wearable gait monitoring system that integrates high-precision sensing, autonomous power supply, and intelligent diagnosis is of great scientific significance and clinical value.
2. Research progress:
In this study, we report a biomimetic smart insole system that achieves high-resolution plantar pressure sensing, energy self-sufficiency, and artificial intelligence-assisted gait intelligent diagnosis through interdisciplinary collaborative design. Inspired by the hierarchical mechanosensory structure of praying mantis legs, the research team designed a dual microstructured capacitive pressure sensor that combines microstructured PDMS and compressible elastic foam. This achieves an ultra-low detection limit of 0.10 Pa, a wide detection range up to 1.4 MPa, and maintains excellent mechanical stability over 12,000 load cycles, significantly exceeding existing flexible pressure sensors and fully meeting the requirements for insole applications.
Regarding the energy system, the smart insole integrates perovskite solar cells and high energy density lithium-sulfur nanobatteries to create a closed-loop adaptive energy supply system. It operates stably under various indoor and outdoor lighting conditions, achieving an average light charging efficiency of 11.21% and energy storage efficiency of 72.15%, effectively solving the energy bottleneck in long-term continuous operation of wearable devices.
At the data processing level, the system collects the spatiotemporal pressure distribution of the sole through a 16-channel wireless module and embeds artificial intelligence algorithms for real-time analysis. Based on the random forest model, the system can achieve 96.0% accuracy in identifying arch anomalies. Based on one-dimensional convolutional neural network (1D-CNN), 12 pathological gait patterns can be classified with 97.6% accuracy. The accompanying mobile app intuitively displays the dynamic force field distribution through color maps, providing interpretable real-time decision support for clinicians and rehabilitation personnel.
3. Future prospects
By deeply integrating biomimetic high-precision sensing, sustainable energy interfaces, and intelligent machine diagnostics, this study built a clinically validated closed-loop wearable platform, providing a new technological pathway for early screening of lower extremity diseases, personalized rehabilitation training, and remote medical monitoring. This presents broad prospects for transforming intelligent wearable devices into clinical-grade diagnostic tools.
sauce:
Science and Technology Review Publishing
Reference magazines:
Lee, Y. Others. (2025). Inspired by praying mantis legs, the smart insole integrates closed-loop power for advanced wearable gait diagnostics. the study. DOI: 10.34133/research.1063. https://spj.science.org/doi/10.34133/research.1063
