Stefan Williams1,2, Hui Fang3, Samuel D Relton1, David C Wong4, Taimour Alam2, Jane E Alty2,5. 1. Leeds Institute of Health Science, University of Leeds Leeds UK. 2. Department of Neurology Leeds Teaching Hospitals National Health Service (NHS) Trust Leeds UK. 3. Department of Computer Science Loughborough University Loughborough UK. 4. Division of Informatics, Imaging and Data Science University of Manchester Manchester UK. 5. Wicking Dementia Research and Education Centre University of Tasmania Hobart Tasmania Australia.
Abstract
BACKGROUND: Computer vision can measure movement from video without the time and access limitations of hospital accelerometry/electromyography or the requirement to hold or strap a smartphone accelerometer. OBJECTIVE: To compare computer vision measurement of hand tremor frequency from smartphone video with a gold standard measure accelerometer. METHODS: A total of 37 smartphone videos of hands, at rest and in posture, were recorded from 15 participants with tremor diagnoses (9 Parkinson's disease, 5 essential tremor, 1 functional tremor). Video pixel movement was measured using the computing technique of optical flow, with contemporaneous accelerometer recording. Fast Fourier transform and Bland-Altman analysis were applied. Tremor amplitude was scored by 2 clinicians. RESULTS: Bland-Altman analysis of dominant tremor frequency from smartphone video compared with accelerometer showed excellent agreement: 95% limits of agreement -0.38 Hz to +0.35 Hz. In 36 of 37 videos (97%), there was <0.5 Hz difference between computer vision and accelerometer measurement. There was no significant correlation between the level of agreement and tremor amplitude. CONCLUSION: The study suggests a potential new, contactless point-and-press measure of tremor frequency within standard clinical settings, research studies, or telemedicine.
BACKGROUND: Computer vision can measure movement from video without the time and access limitations of hospital accelerometry/electromyography or the requirement to hold or strap a smartphone accelerometer. OBJECTIVE: To compare computer vision measurement of hand tremor frequency from smartphone video with a gold standard measure accelerometer. METHODS: A total of 37 smartphone videos of hands, at rest and in posture, were recorded from 15 participants with tremor diagnoses (9 Parkinson's disease, 5 essential tremor, 1 functional tremor). Video pixel movement was measured using the computing technique of optical flow, with contemporaneous accelerometer recording. Fast Fourier transform and Bland-Altman analysis were applied. Tremor amplitude was scored by 2 clinicians. RESULTS: Bland-Altman analysis of dominant tremor frequency from smartphone video compared with accelerometer showed excellent agreement: 95% limits of agreement -0.38 Hz to +0.35 Hz. In 36 of 37 videos (97%), there was <0.5 Hz difference between computer vision and accelerometer measurement. There was no significant correlation between the level of agreement and tremor amplitude. CONCLUSION: The study suggests a potential new, contactless point-and-press measure of tremor frequency within standard clinical settings, research studies, or telemedicine.
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