Emotion Recognition on the Go: Utilizing Wearable IMUs for Personalized Emotion Recognition

Abstract

In the field of emotion recognition, traditional methods often rely on motion capture technologies to recognize human emotions by analyzing body motion. However, these methods are privacy-intrusive and impractical for everyday use. To address the requirements of privacy and practicality, this paper develops a novel personalized Automatic Emotion Recognition (AER) system utilizing inertial measurement units (IMUs) embedded in common wearable devices. Our approach emphasizes personalization to adapt to cultural and individual variations in emotional expression. To reduce the amount of data that needs to be collected from users, we employ cross-modality transfer approaches. These allow us to generate virtual IMU data from established human motion datasets, such as Motion-X and Mocap, thus enriching our training set without extensive real-world data collection. By integrating this virtual IMU data with real IMU data collected from participants, we have developed a personalized wearable-based AER system that is both less intrusive and more practical for real-world applications.