3.8 Proceedings Paper

Large-Scale Continuous Mobility Monitoring of Parkinson's Disease Patients Using Smartphones

期刊

出版社

SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-98551-0_2

关键词

Sensors; Activity recognition; Smartphone; Accelerometer; Machine learning; Deep learning; Parkinson's disease; Clinical trial

向作者/读者索取更多资源

Smartphone-based assessments have been considered a potential solution for continuously monitoring gait and mobility in mild to moderate Parkinson's disease (PD) patients. Forty-four PD patients from cohorts 4 to 6 of the Multiple Ascending Dose (MAD) study of PRX002/RG7935 and thirty-five age-and gender-matched healthy individuals (i.e. healthy controls - HC) in a separate study performed smartphone-based assessments for up to 24weeks and up to 6 weeks, respectively. The assessments included active gait tests, where all participants were asked to walk for 30 s with at least one 180. turn, and passive monitoring, in which subjects carried the smartphone in a pocket or fanny pack as part of their daily routine. In total, over 6,600 active gait tests and over 30,000 h of passive monitoring data were collected. A mobility analysis indicates that patients with PD are less mobile than HCs, as manifested in time spent in gait-related activities, number of turns and sit-to-stand transitions, and power per step. It supports the potential use of smartphones for continuous mobility monitoring in future clinical practice and drug development.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据