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A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease
Dalarna University, School of Technology and Business Studies, Computer Engineering. Malardalen University, Vasteras 72123, Sweden. (PAULINA)ORCID iD: 0000-0002-2752-3712
Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
Dalarna University, School of Technology and Business Studies, Computer Engineering. (PAULINA)ORCID iD: 0000-0003-0403-338X
Dalarna University, School of Technology and Business Studies, Computer Engineering. (PAULINA)
2013 (English)In: Movement Disorders: Supplement: Abstracts of the Seventeenth International Congress of Parkinson's Disease and Movement Disorders, Movement Disorder Society , 2013, 110-111 p.Conference paper, Poster (Refereed)
Abstract [en]


To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT).


The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced.


A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT.

The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged.


A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments.


The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

Place, publisher, year, edition, pages
Movement Disorder Society , 2013. 110-111 p.
National Category
Engineering and Technology
Research subject
Komplexa system - mikrodataanalys, PAULINA - Uppföljning av Parkinsonsymptom från hemmet
URN: urn:nbn:se:du-13113DOI: 10.1002/mds.25605ISI: 000320940501046OAI: diva2:654004
17th International Congress of Parkinson's Disease and Movement Disorders, June 16-20 2013
Knowledge Foundation
Available from: 2013-10-07 Created: 2013-10-07 Last updated: 2015-06-29Bibliographically approved

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Khan, TahaWestin, JerkerDougherty, Mark
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