基于灵活的三电压传感器阵列和深度学习的平面压力检测和步态分析系统
Hanyan Zhou1, Yingying Gui1, Guangqin Gu1
1Key Lab for Special Functional Materials, Ministry of Education, National & Local Joint Engineering Research Center for High-efficiency Display and Lighting Technology, School of Nanoscience and Materials Engineering and Collaborative Innovation Center of Nano Functional Materials and Applications, Henan University, Kaifeng, 475004, China.
一个新的可穿戴的内采用灵活的电压压力传感器,用于自动供电的步态检测. 该系统在步态分析中达到94.23%的准确性,有助于健康评估和疾病诊断.
科学领域:
- 材料科学 材料科学 材料科学
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
背景情况:
- 步态分析对于健康监测和疾病诊断至关重要.
- 现有的系统往往是重的,限制性的,需要外部电源.
- 需要舒适的,自动供电的可穿戴步态检测解决方案.
研究的目的:
- 开发一个柔软,透气,轻量级,自动供电的可穿戴步态检测系统.
- 使用灵活的 triboelectric 压力传感器 (FTPS) 创建一个脚部压力传感器阵列.
- 为了实现健康应用的准确实时步态分析.
主要方法:
- 制造一个柔软,透气的电旋纳米纤维薄膜,具有 triboelectric 特性.
- 将32个FTPS集成到智能内中,用于脚部压力传感.
- 使用长期短期记忆 (LSTM) 人工神经网络进行步态分析.
主要成果:
- 在不同的压力范围内,FTPS表现出高灵敏度 (45.1 mV/kPa和19.4 mV/kPa).
- 智能内是柔软的,可以透气,稳定和自动供电.
- 该LSTM模型实现了94.23%的步态判断精度.
结论:
- 开发了一种可行的解决方案,用于使用自动供电的可穿戴传感器内实时检测步态.
- 这项技术为改善人类健康评估和早期疾病诊断提供了潜力.
- 该系统的舒适性和易用性克服了当前步态分析方法的局限性.
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