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Error and Anomaly Detection for Intra-Participant Time-Series Data

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Author
Mullineaux, David
Irwin, Gareth
Date
2017-07-10
Acceptance date
2017-06-01
Type
Article
Publisher
Taylor and Francis
ISSN
2333-5432
Metadata
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Abstract
Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves the statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window based method for detecting trials with outliers in intra-participant time-series data. The test data were the angles and displacements for the strides or cycles (mean 38 cycles) from treadmill running, with outliers detected through two-stages. Stage-1 was a one-dimensional (spatial) outlier detection at each time-point, where any data-value of a scaled median-absolute-deviation away from the median at that time-point led to removing that cycle. Stage-2 was a two-dimensional (spatial-temporal) outlier detection of a moving-window-standard-deviation (mwSD) across cycles and across the moving-window size (size of ± 0, 1, 2 or 3 time-points), where any data-value greater than the scaled mwSD led to removing that cycle. Scaling was performed using the t-statistic for three significance levels of 0.01, 0.001 or 0.0001. Fewer cycles were removed with smaller scaling and smaller window size. Appropriate settings were stage-1 scaling of 0.0001 (mean 3.5 cycles removed) and stage-2 scaling of 0.01 with a moving-window size of 1 (mean 2.6 cycles removed). Settings in the supplied Matlab code should be customised to each data set, and trials with outliers assessed to justify whether to retain or remove those trials. The method provides an effective method to identify trials with outliers in intra-participant time-series data.
Journal/conference proceeding
International Biomechanics;
Citation
Mullineaux, David R. and Irwin, Gareth (2017) 'Error and anomaly detection for intra-participant time-series data', International Biomechanics, 4 (1). pp. 28-35.
URI
http://hdl.handle.net/10369/8518
DOI
http://dx.doi.org/10.1080/23335432.2017.1348913
Description
This article was published open access in International Biomechanics on 10 July 2017 (online), available at http://dx.doi.org/10.1080/23335432.2017.1348913
Rights
https://creativecommons.org/licenses/by/4.0/
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
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  • Sport Research Groups [1088]

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