用鞋上摄像头和传感器，实时监测17项步态参数，精度超93%。
A Wearable Gait Monitoring System for 17 Gait Parameters Based on Computer Vision
- 用单只鞋上的双目相机追踪另一只鞋的标记，测空间步态参数。
- 结合足跟压力传感器与算法，测时间相关步态参数，准确率超93.61%。
- 系统低漂移、低成本，适合长期采集数据，适配大模型研究。
我们开发了一种鞋上佩戴的步态监测系统，可同时追踪多达17项步态参数，包括步长、步时、步速等。系统采用单只鞋上的双目相机追踪对侧鞋上的标记，实现空间参数估计；同时，在鞋跟处集成压敏电阻（FSR）并搭配自研算法，测量时间类参数。在多参与者测试中，与步态垫对比，所有参数准确率均超过93.61%，长时间行走下漂移仅4.89%。基于该系统采集的数据，使用训练好的Transformer模型进行步态识别，准确率达95.7%。结果表明，该硬件具备长期序列数据采集能力，适合与当前大型语言模型（LLMs）融合应用。系统成本低、操作简便，适用于真实生活场景中的步态测量。
原文摘要 · Abstract (English)
We developed a shoe-mounted gait monitoring system capable of tracking up to 17 gait parameters, including gait length, step time, stride velocity, and others. The system employs a stereo camera mounted on one shoe to track a marker placed on the opposite shoe, enabling the estimation of spatial gait parameters. Additionally, a Force Sensitive Resistor (FSR) affixed to the heel of the shoe, combined with a custom-designed algorithm, is utilized to measure temporal gait parameters. Through testing on multiple participants and comparison with the gait mat, the proposed gait monitoring system exhibited notable performance, with the accuracy of all measured gait parameters exceeding 93.61%. The system also demonstrated a low drift of 4.89% during long-distance walking. A gait identification task conducted on participants using a trained Transformer model achieved 95.7% accuracy on the dataset collected by the proposed system, demonstrating that our hardware has the potential to collect long-sequence gait data suitable for integration with current Large Language Models (LLMs). The system is cost-effective, user-friendly, and well-suited for real-life measurements.
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