Variance in Classifying Affective State via Electrocardiogram and Photoplethysmography.

Abstract

Advances in wearable technology have significantly increased the sensitivity and accuracy of devices for recording physiological signals. Commercial off-the-shelf wearable devices can gather large quantities of physiological data un-obtrusively. This enables momentary assessments of human physiology, which provide valuable insights into an individual’s health and psychological state. Leveraging these insights provides significant benefits for human-to-computer interaction and personalised healthcare. This work contributes an analysis of variance occurring infeatures representative of affective states extracted from electro cardiograms and photoplethysmography; subsequently identifies the cardiac measures most descriptive of affective states from both signals and provides insights into signal and emotion-specific cardiac measures; finally baseline performance for automated affective state detection from physiological signals is established.

Google Scholar

Recommended citation: Dair, Zachary and Dockray, Samantha and O'Reilly, Ruairi (2022). “Variance in Classifying Affective State via Electrocardiogram and Photoplethysmography..” .