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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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A. Lanata, A. Guidi, G. Valenza, P. Baragli, & E. P. Scilingo. (2016). Quantitative heartbeat coupling measures in human-horse interaction. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2696–2699). 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (E.
Abstract: Abstract— We present a study focused on a quantitative estimation of a human-horse dynamic interaction. A set of measures based on magnitude and phase coupling between heartbeat dynamics of both humans and horses in three different conditions is reported: no interaction, visual/olfactory interaction and grooming. Specifically, Magnitude Squared Coherence (MSC), Mean Phase Coherence (MPC) and Dynamic Time Warping (DTW) have been used as estimators of the amount of coupling between human and horse through the analysis of their heart rate variability (HRV) time series in a group of eleven human subjects, and one horse. The rationale behind this study is that the interaction of two complex biological systems go towards a coupling process whose dynamical evolution is modulated by the kind and time duration of the interaction itself. We achieved a congruent and consistent
statistical significant difference for all of the three indices. Moreover, a Nearest Mean Classifier was able to recognize the three classes of interaction with an accuracy greater than 70%. Although preliminary, these encouraging results allow a discrimination of three distinct phases in a real human-animal interaction opening to the characterization of the empirically proven relationship between human and horse.
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