||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.