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Quantitative heartbeat coupling measures in human-horse interaction.
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.
Publication Date: 2017-03-09 PubMed ID: 28268877DOI: 10.1109/EMBC.2016.7591286Google Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
Summary
This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.
This research article examines the quantifiable interaction between humans and horses, based on physiological parameters such as heart rate variability, under different interaction conditions: no interaction, visual/olfactory interaction, and grooming.
Methodology
In this study, the investigators used cutting-edge statistical and mathematical approaches to quantify the physiological interaction between humans and a horse. They focused on the following parameters:
- Magnitude Squared Coherence (MSC): This measure observes the correlation between the frequencies of the human’s and horse’s heartbeat. It is used to determine whether or not there is a rhythm synchronization.
- Mean Phase Coherence (MPC): Similar to MSC, this method indicates the synchronization between the two heartbeats, but it also takes into account the phase difference between them.
- Dynamic Time Warping (DTW): This algorithm is used to measure the similarity or dissimilarity between two time series, in this case, the heart rate variability time series of the human and the horse.
Findings
The researchers applied these measures to eleven human-horse pairings under three different interaction conditions: no interaction, visual/olfactory interaction, and grooming. The results showed a statistically significant difference in the level of heart rate coupling under each condition. The highest level of coupling was found during the grooming interaction. This indicates a high level of synchronization between the human and horse’s heartbeats during this particular interaction.
Classification of Interactions
A Nearest Mean Classifier was used to differentiate between the three classes of interaction. The classifier was able to correctly identify the type of interaction with an accuracy of over 70%.
Implications
While these findings are preliminary, they offer valuable insights into the physiological impact of human-animal interactions. The study establishes a method to objectively measure and classify the quality of human-horse interactions based on heart rate variability. Such research could potentially contribute to understanding and enhancing the benefits of human-animal interactions in therapeutic settings, e.g. horse-assisted therapy.
Cite This Article
APA
Lanata A, Guidi A, Valenza G, Baragli P, Scilingo EP.
(2017).
Quantitative heartbeat coupling measures in human-horse interaction.
Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2696-2699.
https://doi.org/10.1109/EMBC.2016.7591286 Publication
Researcher Affiliations
MeSH Terms
- Animal Husbandry
- Animals
- Heart Rate
- Horses
- Humans
Citations
This article has been cited 8 times.- Marchand WR. Potential Mechanisms of Action and Outcomes of Equine-Assisted Services for Veterans with a History of Trauma: A Narrative Review of the Literature. Int J Environ Res Public Health 2023 Jul 16;20(14).
- Baldwin AL, Rector BK, Alden AC. Physiological and Behavioral Benefits for People and Horses during Guided Interactions at an Assisted Living Residence. Behav Sci (Basel) 2021 Sep 23;11(10).
- Scopa C, Contalbrigo L, Greco A, Lanatà A, Scilingo EP, Baragli P. Emotional Transfer in Human-Horse Interaction: New Perspectives on Equine Assisted Interventions. Animals (Basel) 2019 Nov 26;9(12).
- Semin GR, Scandurra A, Baragli P, Lanatà A, D'Aniello B. Inter- and Intra-Species Communication of Emotion: Chemosignals as the Neglected Medium. Animals (Basel) 2019 Oct 31;9(11).
- Baragli P, Demuru E, Scopa C, Palagi E. Are horses capable of mirror self-recognition? A pilot study. PLoS One 2017;12(5):e0176717.
- Lavezzo L, Barcos-Munoz F, Benis D, Margaria J, Grandjean D, Scilingo EP, Vollenweider A, Henriot S, Nardelli M, Hüppi P, Filippa M. Cardiorespiratory mechanisms triggered during music perception in preterm infants and adults. iScience 2025 Oct 17;28(10):113524.
- Callara AL, Scopa C, Contalbrigo L, Lanatà A, Scilingo EP, Baragli P, Greco A. Unveiling directional physiological coupling in human-horse interactions. iScience 2024 Sep 20;27(9):110857.
- Holtcamp K, Nicodemus MC, Phillips T, Christiansen D, Rude BJ, Ryan PL, Galarneau K. Does Equine Interaction Facilitate Emotional Safety and Learning for College Students within an Agricultural-Based Classroom?. Eur J Investig Health Psychol Educ 2023 Nov 2;13(11):2460-2477.
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