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Going through directional changes: evolving human movement classifiers using an event based encoding


Lones, MA and Alty, J and Duggan-Carter, P and Turner, AJ and Jamieson, DRS and Smith, SL, Going through directional changes: evolving human movement classifiers using an event based encoding, GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, 15-19 July, Berlin, Germany, pp. 1365-1371. ISBN 978-1-4503-4939-0 (2017) [Refereed Conference Paper]

Copyright Statement

Copyright 2017 ACM

DOI: doi:10.1145/3067695.3082490


Directional changes (DC) is an event based encoding for time series data that has become popular in fnancial analysis, particularly within the evolutionary algorithm community. In this paper, we apply DC to a medical analytics problem, using it to identify and summarise the periods of opposing directional trends present within a set of accelerometry time series recordings. The summarised time series data are then used to train classifiers that can discriminate between different kinds of movement. As a case study, we consider the problem of discriminating the movements of Parkinson’s disease patients when they are experiencing a common effect of medication called levodopa-induced dyskinesia. Our results suggest that a DC encoding is competitive against the window-based segmentation and frequency domain encodings that are often used when solving this kind of problem, but offers added benefits in the form of faster training and increased interpretability.


•Computing methodologies → Genetic programming; •Applied computing Health informatics;

Item Details

Item Type:Refereed Conference Paper
Keywords:bioengineering, Parkinson's disease, sensors, diagnostics, genetic programming, directional changes, time series analysis, movement analysis, Parkinson’s disease, dyskinesia
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Neurology and neuromuscular diseases
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Alty, J (Associate Professor Jane Alty)
ID Code:144182
Year Published:2017
Web of Science® Times Cited:1
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2021-04-27
Last Modified:2021-06-09

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