Stress Prediction from Head Motion

Hitoshi Kusano, Yuji Horiguchi, Yukino Baba, Hisashi Kashima

Abstract: The measurement of cognitive stress has huge potential for advertising optimization (e.g., neuromarketing), optimization of recommendation systems, and applications in the fields of human-computer interaction and affective computing. Many studies have addressed stress prediction based on machine learning from the features measured by sensors attached to a subject's body. Meanwhile, as virtual reality (VR) and augmented reality (AR) have increased in popularity, head motion data from users watching VR/AR contents have become ubiquitous. In addition, stress prediction from head motion can be valuable because it does not rely on skin condition (e.g., sweat, tattoo, and cosmetics) and is not detrimental to usability. However, the effectiveness of stress prediction based on head motion data is not well understood. In this study, we propose a method to predict stress from head motion and verify the performance of this method in multiple test situations.