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由液体金属嵌入式海绵三电传感器阵列启用的智能步行分析系统.

Hongwei Liao1, Wandi Chen2, Yun Ye2,3

  • 1School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China.

ACS applied materials & interfaces
|October 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一个可穿戴的步态监测系统,使用灵活的纳米发电机和人工智能. 该系统实现了早期疾病检测和健康评估的高准确性.

关键词:
深度学习是一种深度学习.步态识别系统可以识别步态.液体金属海绵是一种液体金属海绵.带电纳米发电机的 triboelectric 的使用.可穿戴式健康监测设备

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科学领域:

  • 生物医学工程 生物医学工程
  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能

背景情况:

  • 步态动态是健康评估和疾病预测的关键生物标志物.
  • 现有的步态监测方法往往缺乏舒适性和长期可用性.

研究的目的:

  • 开发一个智能,舒适和长期的步态监测系统.
  • 整合灵活的 triboelectric 纳米发电机与人工智能进行步态分析.

主要方法:

  • 一个灵活的PDMS/液体金属海绵 triboelectric纳米发电机 (PLMFT) 阵列被开发出来.
  • 构建了一个带有五个PLMFT传感单元的内类型监控系统.
  • 卷积神经网络 (CNN) 用于分析步态信号.

主要成果:

  • PLMFT装置表现出卓越的灵活性,高电力输出和3000个周期的稳定性.
  • 内系统提供了透气性和可穿戴舒适性.
  • 在CNN的分析中,步态识别准确度达到98.95%.

结论:

  • 拟议的系统为可穿戴健康监测提供了高精度,轻量级的解决方案.
  • 这项技术在探测步行异常,评估运动功能和预测疾病方面具有重大潜力.