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Guidi, A., Lanata, A., Valenza, G., Scilingo, E. P., & Baragli, P. (2017). Validation of smart textile electrodes for electrocardiogram monitoring in free-moving horses. J. Vet. Behav., 17, 19–23.
Abstract: This article focuses on the validation of smart textile electrodes used to acquire electrocardiogram (ECG) signals in horses in a comfortable and robust manner. The performance of smart textile electrodes is compared with standard Ag/AgCl electrodes in terms of the percentage of motion artifacts (MAs, the noise that results from the movement of electrodes against the skin) and signal quality. Seven healthy Standardbred mares were equipped with 2 identical electronic systems for the simultaneous collection of ECGs. One system was equipped with smart textile electrodes, whereas the second was equipped with standard Ag/AgCl electrodes. Each horse was then monitored individually in a stall for 1 hour, without any movement constraints. The ECGs were visually examined by an expert who blindly labeled the ECG segments that had been corrupted by MAs. Finally, the percentage of MAs (MA%) was computed as the number of samples of the corrupted segments over the whole length of the signal. The total MA% was found to be lower for the smart textiles than for the Ag/AgCl electrodes. Consistent results were also obtained by investigating MAs over time. These results suggest that smart textile electrodes are more reliable when recording artifact-free ECGs in horses at rest. Thus, improving the acquisition of important physiological information related to the activity of the autonomic nervous system, such as heart rate variability, could help to provide reliable information on the mood and state of arousal of horses.
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Lanata, A., Guidi, A., Valenza, G., Baragli, P., & Scilingo, E. P. (2017). The Role of Nonlinear Coupling in Human-Horse Interaction: a Preliminary Study. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
Abstract: This study focuses on the analysis of humanhorse
dynamic interaction using cardiovascular information
exclusively. Specifically, the Information Theoretic Learning
(ITL) approach has been applied to a Human-Horse Interaction
paradigm, therefore accounting for the nonlinear information
of the heart-heart interplay between humans and horses.
Heartbeat dynamics was gathered from humans and horses
during three experimental conditions: absence of interaction,
visual-olfactory interaction, and brooming. Cross Information
Potential, Cross Correntropy, and Correntropy Coefficient were
computed to quantitatively estimate nonlinear coupling in a
group of eleven subjects and one horse. Results showed a
statistical significant difference on all of the three interaction
phases. Furthermore, a Support Vector Machine classifier
recognized the three conditions with an accuracy of 90:9%.
These preliminary and encouraging results suggest that ITL
analysis provides viable metrics for the quantitative evaluation
of human-horse interaction.
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