Abstract
Understanding students’ emotion, especially during the classroom time, can help to improve the positive classroom emotional climate towards promoting academic achievement. Unfortunately, most of the exisiting studies that try to understand the emotion of students have just utilized a questionnaire method to discover the link between the classroom emotional climate and academic achievement. Such methods do not reflect exactly the emotion of students in the real-time mode. There are also other studies that leverage hi-tech technologies (e.g. video camera, sensors, smartphones) to capture data generated by people themselves (e.g. physiological data, facial expression, body postures, human-smartphone interaction) to recognize emotion. Nonetheless, these methods build either a general model for all users or an individual model for a selected user leading to having a weak adaptive ability. In this paper, we introduce Wemotion, a smart-sensing system built by smartphones and wristbands that can not only detect students’ emotion in real-time mode and also evolve to continuously improve the accuracy during the life cycle. The system is evaluated by real data collected from volunteers and compared to several existing methods. The results show that the system works well and satisfies the purpose of our research.
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Le-Quang, BL., Dao, MS., Nazmudeen, M.S.H. (2019). Wemotion: A System to Detect Emotion Using Wristbands and Smartphones. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_8
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