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

Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering(2018)

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摘要
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 24 weeks 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\(^\circ \) 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.
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关键词
Sensors,Activity recognition,Smartphone,Accelerometer,Machine learning,Deep learning,Parkinson’s disease,Clinical trial
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