An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals

Zijian Zhou; Muhammad Adeel Asghar; Daniyal Nazir; Kamran Siddique; Mohammad Shorfuzzaman; Raja Majid Mehmood

doi:10.1007/s10586-022-03705-0

An AI-empowered affect recognition model for healthcare and emotional well-being using physiological signals

Cluster Comput. 2023;26(2):1253-1266. doi: 10.1007/s10586-022-03705-0. Epub 2022 Nov 3.

Authors

Zijian Zhou¹, Muhammad Adeel Asghar^#², Daniyal Nazir^#³, Kamran Siddique^#^{1

4}, Mohammad Shorfuzzaman^#⁵, Raja Majid Mehmood^#¹

Affiliations

¹ Information and Communication Technology Department, School of Computing and Data Science, Xiamen University Malaysia, Sepang, 43900 Malaysia.
² Department of Computer Science, National University of Modern Languages, Hamayun Road, Rawalpindi, 46300 Punjab Pakistan.
³ Department of Software Engineering, University of Azad Jammu and Kashmir, Muzaffarabad, 13100 Pakistan.
⁴ Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, 99508 USA.
⁵ Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944 Saudi Arabia.

^# Contributed equally.

Abstract

Affective Computing is one of the central studies for achieving advanced human-computer interaction and is a popular research direction in the field of artificial intelligence for smart healthcare frameworks. In recent years, the use of electroencephalograms (EEGs) to analyze human emotional states has become a hot spot in the field of emotion recognition. However, the EEG is a non-stationary, non-linear signal that is sensitive to interference from other physiological signals and external factors. Traditional emotion recognition methods have limitations in complex algorithm structures and low recognition precision. In this article, based on an in-depth analysis of EEG signals, we have studied emotion recognition methods in the following respects. First, in this study, the DEAP dataset and the excitement model were used, and the original signal was filtered with others. The frequency band was selected using a butter filter and then the data was processed in the same range using min-max normalization. Besides, in this study, we performed hybrid experiments on sash windows and overlays to obtain an optimal combination for the calculation of features. We also apply the Discrete Wave Transform (DWT) to extract those functions from the preprocessed EEG data. Finally, a pre-trained k-Nearest Neighbor (kNN) machine learning model was used in the recognition and classification process and different combinations of DWT and kNN parameters were tested and fitted. After 10-fold cross-validation, the precision reached 86.4%. Compared to state-of-the-art research, this method has higher recognition accuracy than conventional recognition methods, while maintaining a simple structure and high speed of operation.

Keywords: Cognitive computing; Discrete wavelet transform; Emotion recognition; Emotional healthcare; Well-being.

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