用单个智能手表和随机森林算法对帕金森病患者的坐位阶段进行增强的检测和细分
Etienne Goubault1, Camille Martin2, Christian Duval3,4
1Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST), 505 Boul. de Maisonneuve O, Montréal, QC H3A 3C2, Canada.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
在帕金森病 (PD) 中精确的Sit阶段检测是可以使用脚佩戴的智能手表. 这种方法增强了PD患者在家长期监测移动性的能力.
科学领域:
- 生物医学工程 生物医学工程
- 神经学 神经学
- 可穿戴技术可穿戴技术
背景情况:
- 自动检测Sit阶段对于监测帕金森病 (PD) 移动性至关重要.
- 一个单体穿戴传感器为家庭监控提供了可行的解决方案.
研究的目的:
- 为了提高PD患者Sit阶段检测和细分的准确性.
- 为此目的,使用一个在脚上佩戴的单个智能手表.
主要方法:
- 22名PD患者进行了日常活动,并重复过渡到坐姿.
- 三轴加速度和角速度是使用脚戴式智能手表以50 Hz的频率记录的.
- 随机森林算法被训练来检测和细分Sit阶段,对运动捕获数据进行性能评估.
主要成果:
- 该算法在不同试验持续时间 (3,4分钟和5分钟) 的Sit阶段检测中实现了高精度.
- 灵敏度在71.6%至78.8%之间,特异性在84.8%至93.8%之间,F-分数在75.6%至84.7%之间.
- 手动和自动细分之间的中位时间差异始终很低,不到1秒.
结论:
- 使用脚佩戴的智能手表数据的随机森林算法可以准确地检测和细分PD患者的Sit阶段.
- 这种方法提供了与手动细分相比较的性能,但大大减少了时间和精力.
- 这些发现支持有效的,长期的,基于家庭的移动性和PD症状进展的监测.
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