Open Access Repository

Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos

Williams, S, Relton, SD, Fang, H, Alty, J ORCID: 0000-0002-5456-8676, Qahwaji, R, Graham, CD and Wong, DC 2020 , 'Supervised classification of bradykinesia in Parkinson’s disease from smartphone videos' , Artificial Intelligence in Medicine, vol. 110 , pp. 1-9 , doi: 10.1016/j.artmed.2020.101966.

Full text not available from this repository.


BackgroundSlowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.AimWe propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.MethodsWe collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis.ResultsA Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.ConclusionThe method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

Item Type: Article
Authors/Creators:Williams, S and Relton, SD and Fang, H and Alty, J and Qahwaji, R and Graham, CD and Wong, DC
Keywords: computer vision, artificial intelligence, Parkinson's, bradykinesia, video, diagnosis, support vector machine
Journal or Publication Title: Artificial Intelligence in Medicine
Publisher: Elsevier Science Bv
ISSN: 0933-3657
DOI / ID Number: 10.1016/j.artmed.2020.101966
Copyright Information:

© 2020 Elsevier B.V. All rights reserved

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page