Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos

Wong, David C. and Relton, Samuel D. and Fang, Hui and Qhawaji, Rami and Graham, Christopher D. and Alty, Jane and Williams, Stefan; (2019) Supervised classification of bradykinesia for Parkinson's disease diagnosis from smartphone videos. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). Institute of Electrical and Electronics Engineers Inc., ESP, pp. 32-37. ISBN 9781728122861 (https://doi.org/10.1109/CBMS.2019.00017)

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Abstract

Slowness of movement, known as bradykinesia, is an important early symptom of Parkinson's disease. This symptom is currently assessed subjectively by clinical experts. However, expert assessment has been shown to be subject to inter-rater variability. We propose a low-cost, contactless system using smarthphone videos to automatically determine the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predicted the presence of bradykinesia with an estimated test accuracy of 0.79 and the presence of Parkinson's disease with estimated test accuracy 0.63. Even on a small set of pilot data this accuracy is comparable to that recorded by blinded human experts.

ORCID iDs

Wong, David C., Relton, Samuel D., Fang, Hui, Qhawaji, Rami, Graham, Christopher D. ORCID logoORCID: https://orcid.org/0000-0001-8456-9154, Alty, Jane and Williams, Stefan;
CORE (COnnecting REpositories)