Title
Going through directional changes: evolving human movement classifiers using an event based encoding.
Abstract
Directional changes (DC) is an event based encoding for time series data that has become popular in financial 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.
Year
DOI
Venue
2017
10.1145/3067695.3082490
GECCO (Companion)
Keywords
Field
DocType
Genetic programming, directional changes, time series analysis, movement analysis, Parkinson's disease, dyskinesia
Frequency domain,Interpretability,Time series,Evolutionary algorithm,Computer science,Segmentation,Genetic programming,Artificial intelligence,Analytics,Machine learning,Encoding (memory)
Conference
Citations 
PageRank 
References 
1
0.35
6
Authors
5
Name
Order
Citations
PageRank
Michael A. Lones116820.42
Jane E. Alty2377.58
Jeremy Cosgrove342.81
D. R. Stuart Jamieson421.72
Stephen L Smith5116383.01