A preliminary evaluation of a computer vision-based system to detect effects of aromatherapy during high school classes via movement analysis

Abstract

In this paper we present a pilot study on non-intrusive visual observation and estimation of affective parameter using recorded videos (RGB). We aim at estimating student engagement analyzing upper-body movement comparing two different classroom settings: with the introduction of aromatherapy during the class vs standard lesson. Following previous studies on how aromatherapy can alter movement behaviour, we chose Lavender essential oil. We used computer vision techniques for pose estimation and developed software modules for the extraction of movement features from media data. Data show significant increases in overall velocity and acceleration when the participants are exposed to the aromatherapy condition. Significant decreases in neck flexion angle has been also observed, that shows students had a straighter head posture (i.e. sitting up straighter). No significant differences were observed for the overall kinetic energy of the joints and spinal extension.