使用可穿戴传感器和机器学习识别帕金森病的运动进展
Charalampos Sotirakis1, Zi Su1, Maksymilian A Brzezicki1
1NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
NPJ Parkinson's disease
|October 7, 2023
概括
可穿戴式传感器和机器学习可以比标准秤更准确地跟踪帕金森病的运动症状. 这项技术为患有运动障碍的患者提供了更好的诊断和预后能力.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 可穿戴设备可以监测神经系统疾病的运动症状.
- 动力学数据和机器学习可以识别运动障碍和症状严重程度.
研究的目的:
- 为了确定可穿戴传感器数据和机器学习是否可以估计临床评级尺度.
- 评估帕金森病中运动症状进展的纵向监测.
主要方法:
- 74名帕金森病患者在3个月间隔进行了7次实验室检查.
- 六个惯性测量单元传感器记录了行走和姿势摇摆数据.
- 包括Random Forest在内的七个机器学习算法被用于估计运动障碍学会-统一帕金森病评级表第三部分 (MDS-UPDRS-III).
主要成果:
- 在集团层面,29个特征显示出随着时间的推移显著的进展.
- 随机森林模型为MDS-UPDRS-III.III提供了最准确的估计.
- 该模型在15个月内检测到显著的运动症状进展,而MDS-UPDRS-III没有.
结论:
- 与传统秤相比,可穿戴式传感器和机器学习可以更好地跟踪帕金森病的运动症状进展.
- 这些方法可以补充临床尺度,以提高诊断和预后准确性.
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