toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Record Links
Author (up) A. Lanata; A. Guidi; G. Valenza; P. Baragli; E. P. Scilingo doi  openurl
  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 Abstractó 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.
 
  Address  
  Corporate Author Thesis  
  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
Select All    Deselect All
 |   | 
Details
   print