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Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no bradykinesia and participants with severe bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance— 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.

Item Type:	Refereed Conference Paper
Keywords:	computer vision, artificial intelligence, Parkinson's
Research Division:	Biomedical and Clinical Sciences
Research Group:	Medical biotechnology
Research Field:	Medical biotechnology not elsewhere classified
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Clinical health not elsewhere classified
UTAS Author:	Alty, J (Associate Professor Jane Alty)
ID Code:	140951
Year Published:	2020
Deposited By:	Wicking Dementia Research and Education Centre
Deposited On:	2020-09-16
Last Modified:	2022-08-26
Downloads:	13 View Download Statistics