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Refining Accelerometer-based Animal Behaviour Classifications with Sequence-Informed Post-Processing
Preprint

Refining Accelerometer-based Animal Behaviour Classifications with Sequence-Informed Post-Processing

Oakleigh Wilson, Hui Yu, David Schoeman, Gabriella R Sparkes and Christofer Clemente
Authorea , Vol.12 May 2026
Atypon
2026
DOI: https://doi.org/10.22541/authorea.15003095/v1
url
https://doi.org/10.22541/authorea.15003095/v1View
Preprint Version Open

Abstract

1. Supervised machine learning has been used to detect fine-scale behaviours from animal-borne accelerometers by dividing the continuous sequences of behaviour into discrete segments and classifying each with a distinct behavioural category. This approach, while widely implemented, discards the sequential information available in the temporal ordering of the behavioural series. 2. ‘Post-processing’ (smoothing and error correction made after the initial classifications) can be used to improve the accuracy of the original predictions by learning from the natural transitions and durations of behaviours. While broadly implemented across other classification domains, this technique has been underutilised for accelerometer-based animal behaviour classification. 3. In this paper, we compare the performance of five different post-processors (modal, duration-based, transition-based, Hidden Markov Model, and a Naive Bayes smoother) against the original predictions from the base classifier across 15 animal accelerometer datasets. 4. Overall, we find Bayesian smoothing to have the best overall performance improvement (average 6.4% increase in F1-score), resulting in substantial improvements to ecological interpretation. Requiring no additional data and very little additional computational effort, this preliminary research suggests promising potential for the widespread and accessible inclusion of post-processing in the animal behaviour classification pipeline.

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