A novel emotion recognition framework using common off-the-shelf wearable biosensors and a preprocessing algorithm demonstrated the practical prediction of wearers' emotional states.
The study presents a novel framework and preprocessing algorithm for predicting human emotional states using physiological data from common wearable biosensors.
The present research proposes a novel emotion recognition framework for the computer prediction of human emotions using common wearable biosensors. Emotional perception promotes specific patterns of biological responses in the human body, and this can be sensed and used to predict emotions using only biomedical measurements. Based on theoretical and empirical psychophysiological research, the foundation of autonomic specificity facilitates the establishment of a strong background for recognising human emotions using machine learning on physiological patterning. However, a systematic way of choosing the physiological data covering the elicited emotional responses for recognising the target emotions is not obvious. The current study demonstrates through experimental measurements the coverage of emotion recognition using common off-the-shelf wearable biosensors based on the synchronisation between audiovisual stimuli and the corresponding physiological responses. The work forms the basis of validating the hypothesis for emotional state recognition in the literature and presents coverage of the use of common wearable biosensors coupled with a novel preprocessing algorithm to demonstrate the practical prediction of the emotional states of wearers.
Hui et al. (Sat,) conducted a other in Emotion recognition. Emotion recognition framework using wearable biosensors was evaluated on Prediction of emotional states. A novel emotion recognition framework using common off-the-shelf wearable biosensors and a preprocessing algorithm demonstrated the practical prediction of wearers' emotional states.