A hidden Markov model-based stride segmentation technique applied to equine inertial sensor trunk movement data

Pfau, T; Ferrari, M; Parsons, K J; Wilson, A M

Abstract

Inertial sensors are now sufficiently small and lightweight to be used for the collection of large datasets of both humans and animals. However, processing of these large datasets requires a certain degree of automation to achieve realistic workloads. Hidden Markov models (HMMs) are widely used stochastic pattern recognition tools and enable classification of non-stationary data. Here we apply HMMs to identify and segment into strides, data collected from a trunk-mounted six degrees of freedom inertial sensor in galloping Thoroughbred racehorses. A data set comprising mixed gait sequences from seven horses was subdivided into training, cross-validation and independent test set. Manual gallop stride segmentations were created and used for training as well as for evaluating cross-validation and test set performance. On the test set, 91% of the strides were accurately detected to lie within +/- 40 ms (

Citation

Pfau, T., Ferrari, M., Parsons, K. J., & Wilson, A. M. A hidden Markov model-based stride segmentation technique applied to equine inertial sensor trunk movement data. Journal of Biomechanics, 41(1), 216-220. https://doi.org/10.1016/j.jbiomech.2007.08.004

• Journal Article Type: Other
• Deposit Date: Nov 11, 2014
• Journal: JOURNAL OF BIOMECHANICS
• Print ISSN: 0021-9290
• Publisher: Elsevier
• Volume: 41
• Issue: 1
• Pages: 216-220
• DOI: https://doi.org/10.1016/j.jbiomech.2007.08.004
• Public URL: https://rvc-repository.worktribe.com/output/1430570