Emotion recognition based on customized smart bracelet with built-in accelerometer

• Published in: PeerJ (San Francisco, CA) Vol. 4; p. e2258
• Main Authors: Zhang, Zhan, Song, Yufei, Cui, Liqing, Liu, Xiaoqian, Zhu, Tingshao
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 26.07.2016; PeerJ, Inc; PeerJ Inc
• Subjects: Accelerometer; Classification; Computational Science; Computers; Cybernetics; Data collection; Emotion recognition; Emotions; Experiments; International conferences; Jewelry; Kinesiology; Likert scale; Physiology; Psychiatry and Psychology; Sensors; Smart bracelet; Walking; Wearable smart device; Beijing China; China; Accelerometer; Emotion recognition; Wearable smart device; Smart bracelet
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.2258

Recently, emotion recognition has become a hot topic in human-computer interaction. If computers could understand human emotions, they could interact better with their users. This paper proposes a novel method to recognize human emotions (neutral, happy, and angry) using a smart bracelet with built-in accelerometer. In this study, a total of 123 participants were instructed to wear a customized smart bracelet with built-in accelerometer that can track and record their movements. Firstly, participants walked two minutes as normal, which served as walking behaviors in a neutral emotion condition. Participants then watched emotional film clips to elicit emotions (happy and angry). The time interval between watching two clips was more than four hours. After watching film clips, they walked for one minute, which served as walking behaviors in a happy or angry emotion condition. We collected raw data from the bracelet and extracted a few features from raw data. Based on these features, we built classification models for classifying three types of emotions (neutral, happy, and angry). For two-category classification, the classification accuracy can reach 91.3% (neutral vs. angry), 88.5% (neutral vs. happy), and 88.5% (happy vs. angry), respectively; while, for the differentiation among three types of emotions (neutral, happy, and angry), the accuracy can reach 81.2%. Using wearable devices, we found it is possible to recognize human emotions (neutral, happy, and angry) with fair accuracy. Results of this study may be useful to improve the performance of human-computer interaction.