人工智能辅助自动供电可穿戴双模式传感器与TENG和可伸缩光纤用于神经疾病诊断的神经障碍诊断.
Tianliang Li1, Han Liu1, Guoxu Liu2,3
1School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, Hubei, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 12, 2026
概括
这项研究引入了一种用于神经系统疾病的自动供电可穿戴传感器. 它可以准确区分帕金森病和中风,有助于评估康复进展.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 可穿戴技术可穿戴技术
背景情况:
- 目前用于神经疾病下肢功能障碍的可穿戴传感器受到单模式传感,依赖外部电源和有限的诊断能力的限制.
- 有效管理帕金森病和中风等疾病需要先进的监测和诊断工具.
研究的目的:
- 开发一个无线可穿戴的双模式传感器 (WDMS),将肌肉信号监测和脚部压力传感与能量收集能力相结合.
- 利用嵌入式AI模型进行神经系统疾病的准确诊断和康复进展的定量评估.
- 通过提供自动供电的多模式传感解决方案,克服现有的可穿戴系统的局限性.
主要方法:
- 三种灵活的光学应变传感器 (PFOS) 集成用于肌肉监测,与接触分离模式的 triboelectric 纳米发电机 (CS-TENG) 集成,用于脚部压力传感和能量收集.
- 开发和实施一个卷积神经网络-长期短期记忆 (CNN-LSTM) 模型用于数据分析.
- 临床验证使用60名患有神经障碍的个体的步态数据.
主要成果:
- WDMS成功地集成了肌肉信号监控,脚部压力传感和自动供电操作,消除了外部电源依赖.
- 嵌入式CNN-LSTM模型在区分帕金森病和中风患者方面实现了94.23%的准确性.
- 该系统在治疗干预后对康复进展进行了定量评估.
结论:
- 开发的WDMS提供了一种自给自足和可扩展的解决方案,用于智能诊断支持,用于管理神经系统疾病中下肢功能障碍.
- 这项技术超越了被动监控,实现了基于家庭的转型管理和个性化康复.
- 多式联络传感,人工智能驱动的分析和临床验证的协同组合代表着可穿戴医疗技术的重大进步.
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