SensEmo: Enabling Affective Learning through Real-time Emotion Recognition with Smartwatches

TL;DR

SensEmo is a smartwatch-based system that uses physiological signals to recognize student emotions in real-time, aiming to improve learning outcomes by providing feedback to teachers.

Contribution

This paper introduces SensEmo, a novel affective learning system leveraging physiological sensors in smartwatches for real-time emotion recognition and adaptive teaching support.

Findings

88.9% accuracy in emotion recognition

40% improvement in quiz grades

Effective real-world classroom evaluation

Abstract

Recent research has demonstrated the capability of physiological signals to infer both user emotional and attention responses. This presents an opportunity for leveraging widely available physiological sensors in smartwatches, to detect real-time emotional cues in users, such as stress and excitement. In this paper, we introduce SensEmo, a smartwatch-based system designed for affective learning. SensEmo utilizes multiple physiological sensor data, including heart rate and galvanic skin response, to recognize a student's motivation and concentration levels during class. This recognition is facilitated by a personalized emotion recognition model that predicts emotional states based on degrees of valence and arousal. With real-time emotion and attention feedback from students, we design a Markov decision process-based algorithm to enhance student learning effectiveness and experience by by offering suggestions to the teacher regarding teaching content and pacing. We evaluate SensEmo with 22 participants in real-world classroom environments. Evaluation results show that SensEmo recognizes student emotion with an average of 88.9% accuracy. More importantly, SensEmo assists students to achieve better online learning outcomes, e.g., an average of 40.0% higher grades in quizzes, over the traditional learning without student emotional feedback.