Accuracy of Smartphone Video for Contactless Measurement of Hand Tremor Frequency. Issue 1 (28th December 2020)

• Record Type: Journal Article
• Authors: Williams, Stefan; Fang, Hui; Relton, Samuel D.; Wong, David C.; Alam, Taimour; Alty, Jane E.
• Is Part Of: Movement disorders clinical practice. Volume 8:Issue 1(2021)
• Page Start: 69
• Page End: 75
• Publication Date: 2020-12-28
• Subjects: Parkinson's, essential tremor, functional tremor, computer vision, artificial intelligence, optical flow; Movement Disorders; Movement disorders -- Periodicals; Movement disorders; Periodicals; Periodicals; 616
• DOI: 10.1002/mdc3.13119
• Languages: English
• ISSNs: 2330-1619

Abstract

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.