University of Tasmania

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

conference contribution
posted on 2023-05-23, 14:59 authored by Lones, MA, Jane AltyJane Alty, Duggan-Carter, P, Turner, AJ, Jamieson, DRS, Smith, SL
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;







Wicking Dementia Research Education Centre


Association for Computing Machinery

Place of publication

New York, NY, United States

Event title

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Event Venue

Berlin, Germany

Date of Event (Start Date)


Date of Event (End Date)


Rights statement

Copyright 2017 ACM

Repository Status

  • Restricted

Socio-economic Objectives

Artificial intelligence