TY - GEN
T1 - A Comparative Study of Arousal and Valence Dimensional Variations for Emotion Recognition Using Peripheral Physiological Signals Acquired from Wearable Sensors
AU - Alskafi, Feryal A.
AU - Khandoker, Ahsan H.
AU - Jelinek, Herbert F.
N1 - Funding Information:
This work was supported by a KU-KAIST joint grant (8474000221;KKJRC-2019-Health2 (Khandoker)) from Khalifa University Abu Dhabi, UAE.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Wearable sensors have made an impact on healthcare and medicine by enabling out-of-clinic health monitoring and prediction of pathological events. Further advancements made in the analysis of multimodal signals have been in emotion recognition which utilizes peripheral physiological signals captured by sensors in wearable devices. There is no universally accepted emotion model, though multidimensional methods are often used, the most popular of which is the two-dimensional Russell's model based on arousal and valence. Arousal and valence values are discrete, usually being either binary with low and high labels along each dimension creating four quadrants or 3-valued with low, neutral, and high labels. In day-to-day life, the neutral emotion class is the most dominant leaving emotion datasets with the inherent problem of class imbalance. In this study, we show how the choice of values in the two-dimensional model affects the emotion recognition using multiple machine learning algorithms. Binary classification resulted in an accuracy of 87.2% for arousal and up to 89.5% for valence. Maximal 3-class classification accuracy was 80.9% for arousal and 81.1% for valence. For the joined classification of arousal and valence, the four-quadrant model reached 87.8%, while the nine-class model had an accuracy of 75.8%. This study can be used as a basis for further research into feature extraction for better overall classification performance.
AB - Wearable sensors have made an impact on healthcare and medicine by enabling out-of-clinic health monitoring and prediction of pathological events. Further advancements made in the analysis of multimodal signals have been in emotion recognition which utilizes peripheral physiological signals captured by sensors in wearable devices. There is no universally accepted emotion model, though multidimensional methods are often used, the most popular of which is the two-dimensional Russell's model based on arousal and valence. Arousal and valence values are discrete, usually being either binary with low and high labels along each dimension creating four quadrants or 3-valued with low, neutral, and high labels. In day-to-day life, the neutral emotion class is the most dominant leaving emotion datasets with the inherent problem of class imbalance. In this study, we show how the choice of values in the two-dimensional model affects the emotion recognition using multiple machine learning algorithms. Binary classification resulted in an accuracy of 87.2% for arousal and up to 89.5% for valence. Maximal 3-class classification accuracy was 80.9% for arousal and 81.1% for valence. For the joined classification of arousal and valence, the four-quadrant model reached 87.8%, while the nine-class model had an accuracy of 75.8%. This study can be used as a basis for further research into feature extraction for better overall classification performance.
UR - http://www.scopus.com/inward/record.url?scp=85122549344&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9630759
DO - 10.1109/EMBC46164.2021.9630759
M3 - Conference contribution
C2 - 34891480
AN - SCOPUS:85122549344
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1104
EP - 1107
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -