可穿戴在线结步态检测和线索系统
Jan Slemenšek1, Jelka Geršak1, Božidar Bratina2
1Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia.
Bioengineering (Basel, Switzerland)
|October 25, 2024
概括
这项研究介绍了一种使用机器学习的可穿戴系统,以帮助帕金森病患者结步伐. 该设备提供及时干预,将结事件的持续时间减少45%.
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.
- 康复技术 康复技术 康复技术
背景情况:
- 步态结 (FOG) 是帕金森病 (PD) 的残疾症状,严重影响了运动能力和生活质量.
- 目前针对FOG的干预措施往往缺乏实时,个性化的反,限制了它们的有效性.
- 可穿戴技术为PD患者的持续监测和按需协助提供了一个有希望的途径.
研究的目的:
- 开发和评估一个实时可穿戴系统,用于检测和缓解帕金森病患者的步态结.
- 评估用于FOG检测的基于微控制器的设备上实现的机器学习模型的准确性,效率和延迟.
- 量化系统按需振动刺激对FOG发作持续时间的影响.
主要方法:
- 在微控制器上实现一个卷积神经网络 (CNN) + 循环神经网络 (RNN) + 过去的样本数据预处理 (PS) 机器学习模型.
- 使用可穿戴系统的实时步态模式监控,处理速率为40Hz.
- 在检测到FOG发作时,向患者提供按需的振动刺激,并进行延迟和准确性评估.
主要成果:
- CNN+RNN+PS模型实现了高FOG检测准确度95.1%,平均检测延迟为261毫秒.
- 可穿戴系统在实际环境中表现出强大的性能,运行时延迟最小.
- 接受按需刺激的患者在FOG发作的平均持续时间中显著减少了45%.
结论:
- 开发的实时可穿戴系统显示出在患有FOG的帕金森病患者中改善移动性的巨大潜力.
- 个性化的实时反系统可以有效地减少FOG事件的影响,并提高康复结果.
- 这项技术代表了对运动障碍的辅助设备的有希望的进步,改善了患者的生活质量.
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