A Multimodal Framework for Robustly Distinguishing among Similar Emotions using Wearable Sensors

Detecting the correct emotion is crucial for improved mental health outcomes. While the existing works on emotion recognition focus on detecting the common or primary emotions only, there exists some uncommon or secondary emotions of similar kind (e.g., contempt vs anger) that makes accurate emotion detection challenging. Moreover, there exists limited labeled data on such secondary emotions. We present the first work to accurately discriminate among such similar emotions by generating distinguishable data for the secondary emotions using convenient multimodal wrist sensors. Extensive evaluations show that our novel solution provides around 7-36 % F1-score improvement to existing solutions for similar emotions, and also significantly reduces the burden on providing labeled emotion data.