Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST), 505 Boul. de Maisonneuve O, Montréal, QC H3A 3C2, Canada.
Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montréal, QC H2X 1Y4, Canada.
Sensors (Basel). 2024 Aug 24;24(17):5486. doi: 10.3390/s24175486.
The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson's disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use.
to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle.
Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL.
Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up.
The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments.
自动检测日常生活活动（ADL）对于改善帕金森病（PD）症状的长期家庭监测至关重要。虽然大多数用于 ADL 检测的身体佩戴式传感器算法都是使用涵盖全身运动学的实验室研究系统开发的，但现在迫切需要使用仍然可商用且经济实惠的单个身体佩戴式传感器来实现 ADL 检测。
使用佩戴在脚踝上的智能手表检测和分割 PD 患者的行走、转身、坐下和站立活动。
22 名 PD 患者在模拟的自由生活环境中进行 3 分钟试验前，先进行 3 次计时起立行走（TUG）任务，然后再进行清洁 ADL。使用智能手表以三维方式记录右侧或左侧脚踝的加速度和角速度。TUG 任务用于开发行走、转身、坐下和站立的检测算法，而在自由生活环境中的 3 分钟试验用于测试和验证这些算法。根据 ADL 的手动分割计算灵敏度、特异性和 F 分数。
行走的灵敏度、特异性和 F 分数分别为 96.5%、94.7%和 96.0%；转身的灵敏度、特异性和 F 分数分别为 90.0%、93.6%和 91.7%；坐下的灵敏度、特异性和 F 分数分别为 57.5%、70.5%和 52.3%；站立的灵敏度、特异性和 F 分数分别为 57.5%、72.9%和 54.1%。手动和自动分割之间的时间差异中位数分别为行走 1.31 秒、转身 0.71 秒、坐下 2.75 秒和站立 2.35 秒。
本研究结果表明，基于单个智能手表对 ADL 进行分割以描述 PD 患者的移动能力，可以与手动分割相媲美，同时需要的时间明显更少。虽然行走和转身检测效果良好，但坐下和站立需要进一步研究以开发更好的算法。尽管如此，这些成就增加了在生态环境中实施用于 PD 监测的可穿戴技术的成功机会。
