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Author  |
A. Lanata; A. Guidi; G. Valenza; P. Baragli; E. P. Scilingo | ||||
| Title | Quantitative heartbeat coupling measures in human-horse interaction | Type | Conference Article | ||
| Year | 2016 | Publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | Abbreviated Journal | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (E |
| Volume | Issue | Pages | 2696-2699 | ||
| Keywords | electrocardiography; medical signal processing; signal classification; time series; Dtw; Hrv; Mpc; Msc; complex biological systems; dynamic time warping; grooming; heart rate variability time series; heartbeat dynamics; human-horse dynamic interaction; magnitude squared coherence; magnitude-phase coupling; mean phase coherence; nearest mean classifier; quantitative heartbeat coupling; real human-animal interaction; time duration; visual-olfactory interaction; Coherence; Couplings; Electrocardiography; Heart rate variability; Horses; Protocols; Time series analysis | ||||
| 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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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (E | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1557-170x | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Equine Behaviour @ team @ | Serial | 6175 | ||
| Permanent link to this record | |||||
| Author |
Lanata, A.; Guidi, A.; Valenza, G.; Baragli, P.; Scilingo, E. P. | ||||
| Title | The Role of Nonlinear Coupling in Human-Horse Interaction: a Preliminary Study | Type | Conference Article | ||
| Year | 2017 | Publication | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | Abbreviated Journal | EMBC |
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| 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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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Equine Behaviour @ team @ | Serial | 6176 | ||
| Permanent link to this record | |||||