Emotion recognition is useful in many applications such as preventing crime or improving customer satisfaction. Most of current methods are performed using facial features, which require close-up face information. Such information is difficult to capture with normal security cameras. The advantage of using gait and posture over conventional biometrics such as facial features is that gaits and postures can be obtained unobtrusively from faraway, even in a noisy environment. This study aims to investigate and analyze the relationship between human emotions and their gaits or postures. We collected a dataset made from the input of 49 participants for our experiments. Subjects were instructed to walk naturally in a circular walking path, while watching emotion-inducing videos on Microsoft HoloLens 2 smart glasses. An OptiTrack motion-capturing system was used for recording the gaits and postures of participants. The angles between body parts and walking straightness were calculated as features for comparison of body-part movements while walking under different emotions. Results of statistical analyses show that the subjects' arm swings are significantly different among emotions. And the arm swings on one side of the body could reveal subjects' emotions more obviously than those on the other side. Our results suggest that the arm movements together with information of arm side and walking straightness can reveal the subjects' current emotions while walking. That is, emotions of humans are unconsciously expressed by their arm swings, especially by the left arm, when they are walking in a non-straight walking path. We found that arm swings in happy emotion are larger than arm swings in sad emotion. To the best of our knowledge, this study is the first to perform emotion induction by showing emotion-inducing videos to the participants using smart glasses during walking instead of showing videos before walking. This induction method is expected to be more consistent and more realistic than conventional methods. Our study will be useful for implementation of emotion recognition applications in real-world scenarios, since our emotion induction method and the walking direction we used are designed to mimic the real-time emotions of humans as they walk in a non-straight walking direction.

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.