使用可穿戴传感器和机器学习对帕金森病的步态和姿势症状进行客观评估
Lingyan Ma1,2, Shinuan Lin3,4, Jianing Jin1,2
1Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Frontiers in aging neuroscience
|August 27, 2025
概括
可穿戴式传感器可以客观地预测帕金森病 (PD) 的步态和姿势严重程度. 这种方法提供了高效的,自动化的评估,有助于临床监测和潜在的家庭使用来跟踪症状.
科学领域:
- 神经学
- 生物医学工程
- 数据科学
背景情况:
- 步行和姿势障碍是帕金森病 (PD) 的标志性症状,显著增加了跌倒风险.
- 对这些症状的传统临床评估是主观的,需要大量的时间.
- 需要客观和自动化的方法来准确评估PD症状.
研究的目的:
- 使用可穿戴传感器数据预测帕金森病的步态和姿势症状的严重程度.
- 开发一个客观,高效和自动化的方法来评估与PD相关的步态和姿势障碍.
- 验证用于预测特定MDS-UPDRS III步态和姿势项目的机器学习模型.
主要方法:
- 在标准化的步行评估中从225名帕金森病患者收集了基于传感器的步行参数.
- 训练了极端梯度增强 (XGBoost) 和支持矢量机 (SVM) 模型来预测MDS-UPDRS III步态和姿势项目得分 (#3.9-3.13).
- 在训练 (80%) 和独立测试 (20%) 中使用随机分类,在稳定性检查中使用一次性交叉验证 (LOOCV).
主要成果:
- XGBoost模型表现出优于SVM的性能.
- 模型在5个行走和姿势项目中实现了超过80%的可接受准确性,在3.9-#3.11项目中达到>70%的绝对准确性.
- 关键的步态特征如"有效试验时间"和"Shank-Swing RoM- mean (max) "被确定具有显著的临床相关性.
结论:
- 穿戴式传感器数据可以有效和客观地评估帕金森病的步态和姿势症状.
- 这种方法为传统的临床量表提供了一致和有效的替代方案.
- 这种方法有助于临床决策,并使得在家进行纵向的症状监测.
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