Finding Needles in a Haystack: Recognizing Emotions Just From Your Heart

Emotion plays an important role in human cognition and behavior. How to recognize emotions based on physiological signals has attracted increasing research interests worldwide up to date. Both traditional eastern medicine and modern western medicine have confirmed the existence of relationship between human emotionality and heart activity. However, in practice, emotion recognition only using Electrocardiogram (ECG) signals seems quite challenging, not only due to the severe noise interferences and the serious data variations, but also because of the ambiguous relationship between emotional states and ECG data. Such difficulty can even be compared to finding needles in a haystack. As an innovative endeavor to deal with the issue of only-ECG-based emotion recognition, this paper has proposed a novel solution from the perspective of weak signal classification. The proposed solution extracts the static-dynamic representation under the principle of Yin-Yang balance from the heartbeat data, and then utilizes the set-based collaborative measurement upon the thought of data coopetition to classify these features for recognizing emotions. Experimental results have demonstrated the effectiveness, efficiency and adaptiveness of the solution for uncovering the potential relationship between emotions and ECG. Thus, this proposal has also illuminated a promising research direction for the general problem of weak signal classification.