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. 2008;41(1):216-20.
doi: 10.1016/j.jbiomech.2007.08.004. Epub 2007 Sep 25.

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

Thilo Pfau  1 , Marta Ferrari, Kevin Parsons, Alan Wilson
Affiliations

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

Thilo Pfau et al. J Biomech. 2008.

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 (< 10% stride time) of the manually segmented stride starts. While the automated system did not miss any of the strides, it identified additional gallop strides at the beginning of the trials. In the light of increasing use of inertial sensors for ambulatory measurements in clinical settings, automated processing techniques will be required for efficient data processing to enable instantaneous decision making from large amounts of data. In this context, automation is essential to gain optimal benefits from the potentially increased statistical power associated with large numbers of strides that can be collected in a relatively short period of time. We propose the use of HMM-based classifiers since they are easy to implement. In the present study, consistent results across cross-validation and test set were achieved with limited training data.

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