Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3464-3467. doi: 10.1109/EMBC48229.2022.9871375.
We present a cloud-based multimodal dialogue platform for the remote assessment and monitoring of speech, facial and fine motor function in Parkinson's Disease (PD) at scale, along with a preliminary investigation of the efficacy of the various metrics automatically extracted by the platform. 22 healthy controls and 38 people with Parkinson's Disease (pPD) were instructed to complete four interactive sessions, spaced a week apart, on the platform. Each session involved a battery of tasks designed to elicit speech, facial movements and finger movements. We find that speech, facial kinematic and finger movement dexterity metrics show statistically significant differences between controls and pPD. We further investigate the sensitivity, specificity, reliability and generalisability of these metrics. Our results offer encouraging evidence for the utility of automatically-extracted audiovisual analytics in remote mon-itoring of PD and other movement disorders.
我们提出了一个基于云的多模态对话平台，用于大规模远程评估和监测帕金森病（PD）患者的言语、面部和精细运动功能，并初步研究了该平台自动提取的各种指标的疗效。我们指导 22 名健康对照者和 38 名帕金森病患者（pPD）在该平台上完成四个间隔一周的互动会话。每个会话都涉及一系列旨在引出言语、面部运动和手指运动的任务。我们发现，言语、面部运动学和手指运动灵巧度指标在对照组和 pPD 组之间存在统计学上的显著差异。我们进一步研究了这些指标的敏感性、特异性、可靠性和通用性。我们的结果为自动提取视听分析在 PD 及其他运动障碍的远程监测中的应用提供了令人鼓舞的证据。
