Methods for Real-time Emotional Gait Data Collection Induced by Smart Glasses in a Non-straight Walking Path

Abstract

Emotion recognition is an attractive research field because of its usefulness. Most methods for detecting and analyzing emotions depend on facial features so the close-up facial information is required. Unfortunately, high-resolution facial information is difficult to be captured from a standard security camera. Unlike facial features, gaits and postures can be obtained noninvasively from a distance. We proposed a method to collect emotional gait data with real-time emotion induction. Two gait datasets consisting of total 72 participants were collected. Each participant walked in circular pattern while watching emotion induction videos shown on Microsoft HoloLens 2 smart glasses. OptiTrack motion capturing system was used to capture the participants\' gaits and postures. Effectiveness of emotion induction was evaluated using self-reported emotion questionnaire. In our second dataset, additional information of each subject such as dominant hand, dominant foot, and dominant brain side was also collected. These data can be used for further analyses. To the best of our knowledge, emotion induction method shows the videos to subjects while walking has never been used in other studies. Our proposed method and dataset have the potential to advance the research field about emotional recognition and analysis, which can be used in real-world applications.