2134/37746
David C. Wong
David C.
Wong
Samuel D. Relton
Samuel D.
Relton
Hui Fang
Hui
Fang
Rami Qhawaji
Rami
Qhawaji
Christopher D. Graham
Christopher D.
Graham
Jane Alty
Jane
Alty
Stefan Williams
Stefan
Williams
Supervised classification of bradykinesia for Parkinson’s disease diagnosis from smartphone videos
Loughborough University
2019
Classification
Parkinson’s
Bradykinesia
Video
Computer vision
Diagnosis
Support vector machine
Information and Computing Sciences not elsewhere classified
2019-05-14 14:26:33
Conference contribution
https://repository.lboro.ac.uk/articles/conference_contribution/Supervised_classification_of_bradykinesia_for_Parkinson_s_disease_diagnosis_from_smartphone_videos/9401510
Slowness of movement, known as bradykinesia,
in 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 smartphone videos to automatically determine
the presence of bradykinesia. Using 70 videos recorded in a pilot study, we predict the presence of bradykinesia with an
estimated test accuracy of 0.79 and the presence of Parkinson’s disease diagnosis 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.