Emotional State Classification using Pulse Rate Variability
Résumé
Humans are continually exposed to emotional stimuli. Gesture, voice intonation, and facial expressions are among the most popular cues that describe our changing emotions. However, the physiological systems that govern our bodily functions are also impacted by the different emotions that we feel. Psychophysiology is a science concerned with the existing relationship between the psychic state of a person and the physiological signals their body emits. In fact, this connection is due to the autonomic nervous system activity, whose sympathetic nerves get sparked when a person is emotionally excited. One particular physiological phenomenon has turned out to be an excellent indicator of the autonomic function. It is the spontaneous fluctuations in the heart rhythms, which can be described by the pulse rate variability (PRV). In this work, the PRV is obtained using remote photoplethysmography. We prove that from a simple RGB camera, it is possible to assess the emotional state of a person by analysing their pulse rate variations. This optimistic finding is supported by surprising results and an accuracy rate of around 60% on the CAS(ME) 2 dataset. This is the first study to propose an emotion classification based on a physiological signal analysis using CAS(ME) 2 .
Mots clés
autonomic function
pulse rate variability
PRV
emotion classification
physiological signal analysis
emotional state classification
emotional stimuli
voice intonation
facial expressions
physiological systems
psychic state
autonomic nervous system activity
sympathetic nerves
RGB camera
CAS(ME)2 dataset
remote photoplethysmography
autonomic nervous system
cardiology
physiological phenomenon
psychology
Skin
Physiology
Resonant frequency
Biomedical monitoring
emotion recognition
image colour analysis
medical signal processing
neurophysiology
photoplethysmography
Feature extraction
Heart rate variability