IEEE Trans Neural Syst Rehabil Eng. 2024;32:3828-3836. doi: 10.1109/TNSRE.2024.3477003. Epub 2024 Oct 29.
Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson's disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here.
帕金森病患者可能同时出现震颤和运动迟缓等运动症状；因此，理想的家庭监测系统应能够在不受日常活动背景噪音影响的情况下持续跟踪症状。本研究的目的是展示在自由生活场景中检测症状发作的可行性，提供更高水平的可解释性以辅助人工智能决策。基于参与者在实验室中进行的脚本活动的可穿戴传感器数据以及对这些任务的视频记录的临床医生评分训练的机器学习模型，在监督实验室环境中识别出震颤、运动迟缓以及运动障碍的平衡准确率分别为 83%、75%和 81%，与临床医生评分相比。当将相同的模型评估在未经过脚本的、在自己家中进行的非监督活动数据时，与自我评估的患者日记相比，其在震颤、运动迟缓以及运动障碍检测方面的平衡准确率分别为 63%、63%和 67%，这进一步突出了其局限性。踝部佩戴的传感器在检测运动障碍方面具有优势，但在检测震颤和运动迟缓方面并未显示出额外的益处。
