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设计了一种基于非接触式摩擦纳米发电机(NC-TENG)的步态识别系统，旨在通过捕捉人体运动中的机械能实现精准的步态分析与分类。实验表明，该传感器的感应电压与运动速度、加速度以及角度均呈现良好的线性关系。当运动角度设定为200°时，输出电压达到峰值为0.75 V;在运动速度从90°/s提升至180°/s时，峰值电压从0.475 V显著提升至1.49 V,验证了NC-TENG用于步态信息采集的可行性。基于该系统对人体大腿的5种步态数据进行采集，并设计了一种结合了定义层和注意力机制的双向长短期记忆网络(BiLSTM)分类算法，实现了对序列数据的有效分类。实验结果显示，该系统输出的电压信号能够清晰表征每种步态的特征波形，实现了这5种步态动作的有效识别，其中蹲起、下楼梯的动作识别率分别高达90.0%和97.5%,展现了该步态识别系统在健康监测和运动科学领域的应用潜力。
Abstract：A gait recognition system based on a non-contact triboelectric nanogenerator(NC-TENG) was designed to achieve accurate gait analysis and classification by capturing the mechanical energy in human movement. Experiments show that the sensor's induced voltage exhibits good linear relationships with the motion speed, acceleration and angle. At a motion angle of 200°, the output voltage reaches its peak of 0.75 V. As the motion speed increases from 90°/s to 180°/s, the peak voltage significantly rises from 0.475 V to 1.49 V, demonstrating the feasibility of the NC-TENG for gait information acquisition. Based on this system, five kinds of gait data of human thighs were collected, and a bidirectional long and short-term memory(BiLSTM) classification algorithm combining the definition layer and attention mechanism was designed to realize the effective classification of the sequence data. Experimental results demonstrate that the voltage signals generated by the system can clearly characterize the feature waveforms of each gait, achieving effective identification of these five gait motions. Notably, the recognition rates for squat-stand and downstairs walking reach 90.0% and 97.5%, respectively, showing the application potential of this gait recognition system in health monitoring and sports science.
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基本信息:
DOI：10.13250/j.cnki.wndz.25100102
中图分类号:TB383.1;TP18;TM31;TP212
引用信息:
[1]顾梦辰,张静怡,曹自平.基于非接触式摩擦纳米发电机的步态识别方法[J].微纳电子技术,2025,62(10):18-27.DOI:10.13250/j.cnki.wndz.25100102.
基金信息:
国家自然科学基金(62371253)