TY - GEN
T1 - Physiological signal-based emotion recognition system
AU - Hassani, Saif
AU - Bafadel, Ibrahim
AU - Bekhatro, Abdelrahman
AU - Al Blooshi, Ebraheim
AU - Ahmed, Soha
AU - Alahmad, Mahmoud
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The design specification and software implementation of a physiological signal-based user-independent emotion recognition system is proposed. The system will have various valuable applications in medicine, computing, education and security domain. Unfortunately, there are many challenges that must be overcome and addressed and this what make the contribution in this field valuable and much needed. The proposed system should operate as a user-independent system, based on physiological signal databases attained from several subjects. Various physiological signals were obtained and measured such as electrocardiogram (ECG), skin temperature, galvanic skin response (GSR), Electromyography (EMG), heart rate (HR), respiration rate (RR), saturation of oxygen in the blood (SPO2), Systolic blood pressure(SBP), and diastolic blood pressure(DBP). To construct the proposed system, the acquired signals went through preprocessing, feature extraction and statistical analysis. A subset of features was selected to be incorporated in the final system due to several technical limitations and design constraint. Using statistical analysis, the system was able to recognize emotion on three basic emotional states namely: anger, joy, and neutral.
AB - The design specification and software implementation of a physiological signal-based user-independent emotion recognition system is proposed. The system will have various valuable applications in medicine, computing, education and security domain. Unfortunately, there are many challenges that must be overcome and addressed and this what make the contribution in this field valuable and much needed. The proposed system should operate as a user-independent system, based on physiological signal databases attained from several subjects. Various physiological signals were obtained and measured such as electrocardiogram (ECG), skin temperature, galvanic skin response (GSR), Electromyography (EMG), heart rate (HR), respiration rate (RR), saturation of oxygen in the blood (SPO2), Systolic blood pressure(SBP), and diastolic blood pressure(DBP). To construct the proposed system, the acquired signals went through preprocessing, feature extraction and statistical analysis. A subset of features was selected to be incorporated in the final system due to several technical limitations and design constraint. Using statistical analysis, the system was able to recognize emotion on three basic emotional states namely: anger, joy, and neutral.
KW - Autonomous nervous system
KW - Emotion recognition
KW - Physiological signal acquisition
KW - Statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=85050856387&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050856387&partnerID=8YFLogxK
U2 - 10.1109/ICETAS.2017.8277912
DO - 10.1109/ICETAS.2017.8277912
M3 - Conference contribution
AN - SCOPUS:85050856387
T3 - 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
SP - 1
EP - 5
BT - 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2017
Y2 - 29 November 2017 through 1 December 2017
ER -