Computer Vision Neurology Group

Computer Vision Neurology Group

Publications

Accuracy of smartphone video for contactless measurement of hand tremor frequency

Published in Movement Disorders Clinical Practice, 2020

The study suggests a potential new, contactless ‘point and press’ measure of tremor frequency within standard clinical settings or telemedicine

Recommended citation: Willams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. (2020). "Accuracy of smartphone video for contactless measurement of hand tremor frequency." Movement Disorders Clinical Practice. https://onlinelibrary.wiley.com/doi/pdf/10.1002/mdc3.13119

Supervised classification of bradykinesia for Parkinson’s disease from smartphone video

Published in AI in Medicine, 2020

The method described here presents an approach for predicting bradykinesia from videos of fingertapping 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

Recommended citation: Willams S, Relton SD, Fang H, Alty JE, Qahwaji R, Graham CD, Wong DC. "Supervised classification of bradykinesia for Parkinson’s disease from smartphone video." AI in Medicine. https://personalpages.manchester.ac.uk/staff/david.wong/assets/Papers/2019Williams.pdf

The discerning eye of computer vision: Can it measure Parkinson’s finger tap bradykinesia?

Published in Journal of the Clinical Neuroscience, 2020

Eulerian video magnification reveals apparent subclinical tremor in Parkinson’s.

Recommended citation: Willams S, Fang H, Relton SD, Graham CD, Alty JE. "Seeing the unseen: Could Eulerian video magnification aid clinician detection of subclinical Parkinson’s tremor?" Journal of the clinical neuroscience. https://www.jocn-journal.com/article/S0967-5868(20)31525-3/fulltext

The discerning eye of computer vision: Can it measure Parkinson’s finger tap bradykinesia?

Published in Journal of the Neurological Sciences, 2020

New computer vision software, DeepLabCut, can quantify three measures related to Parkinson’s bradykinesia from smartphone videos of finger tapping. Objective ‘contactless’ measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely ‘contactless’. This next generation technology holds potential for Parkinson’s and other neurological disorders with altered movements.

Recommended citation: Willams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, Alty JE. "The discerning eye of computer vision: Can it measure Parkinson’s finger tap bradykinesia?." Journal of the Neurological Sciences. https://www.research.manchester.ac.uk/portal/files/170983862/JNS_D_20_00378_R2_2_1_.pdf

Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia

Published in 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020

Our work visually presents the archetypal types of bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no bradykinesia, and the other showing linear decrement over time

Recommended citation: Zhao Z, Fang H, Williams S, Relton SD, Alty JE., Wong DC. "Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia" 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://eprints.utas.edu.au/35260/1/Clustring%20decrement%20FT%20conf%20paper%20Zhibin%20Zhao%20EMBC2020_final.pdf