A narrative review on the characterisation of automated human emotion detection systems using biomedical sensors and machine intelligence

被引:0
作者
Dutta S. [1 ]
Mishra B.K. [1 ]
Mitra A. [2 ]
Chakraborty A. [3 ]
机构
[1] Department of Computer Science and Engineering, GIET University, Odisha
[2] Department of Computer Science and Engineering, Amity University, Kolkata
[3] Department of Computer Science and Engineering, University of Engineering and Management, Kolkata
关键词
ECG; EEG; Electrocardiography; Electroencephalography; Electromyographic signal; EMG; Emotion perception; Galvanic skin response; GSR; Human emotions; Photoplethysmogram; PPG; Respiration rate; RR; Skin temperature measurements; SKT; Valence-Arousal plane;
D O I
10.1504/IJRIS.2023.136360
中图分类号
学科分类号
摘要
In our day-To-day life, emotion plays an essential role in decision-making and human interaction. For many years, psychologists have been trying to develop many emotional models to explain the human emotional or affective states. Automated emotion recognition is a popular research problem increasingly utilised in marketing, education, health sector, and human-robot interaction. There are different ways of emotion recognition, namely uni-modal and multi-modal solutions. However, it depends upon the purpose for which it is to be used. The primary focus of this paper is to provide a detailed review of the existing literature in this domain with the help of three verticals. Initially, the standard databases used for elicitation of human emotions are discussed in brief. Next, the different sensing approaches used to gather physiological signals are discussed. Finally, a thorough review of the state-of-The-Art is given, with reference to the emotional states from the circumplex model of valence-Arousal plane. © 2023 Inderscience Publishers. All rights reserved.
引用
收藏
页码:266 / 276
页数:10
相关论文
共 45 条
  • [1] Bradley M.M., Lang P.J., Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings, (1999)
  • [2] Chang C-Y., Chang C-W., Zheng J-Y., Chung P-C., Physiological emotion analysis using support vector regression, Neurocomputing, 122, 1, pp. 79-87, (2013)
  • [3] Citron F.M.M., Gray M.A., Critchley H.D., Weekes B.S., Ferstl E.C., Emotional valence and arousal affect reading in an interactive way: neuroimaging evidence for an approach-withdrawal framework, Neuropsychologia, 56, 1, pp. 79-89, (2014)
  • [4] Csikszentmihalyi M., Larson R., Flow and the Foundations of Positive Psychology, 10, (2014)
  • [5] Dissanayake T., Rajapaksha Y., Ragel R., Nawinne I., An ensemble learning approach for electrocardiogram sensor based human emotion recognition, Sensors, 19, 20, (2019)
  • [6] Dominguez-Jimenez J.A., Campo-Landines K.C., Martinez-Santos J.C., Delahoz E.J., Contreras-Ortiz S.H., A machine learning model for emotion recognition from physiological signals, Biomedical Signal Processing and Control, 55, 1, (2020)
  • [7] Doma V., Pirouz M., A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals, Journal of Big Data, 7, 1, pp. 1-21, (2020)
  • [8] Dutta S., Dash S., Padhy N., Analysis of human emotion-based data using miot technique, Medical Internet of Things, pp. 199-203, (2021)
  • [9] Dutta S., Mitra A., Padhy N., Khan G., Review on sensors for emotion recognition, Data Engineering and Communication Technology, pp. 571-579, (2021)
  • [10] Dutta S., Mishra B.K., Mitra A., Chakraborty A., An analysis of emotion recognition based on GSR signal, ECS Transactions, 107, 1, (2022)