LSTM-based emotion detection using physiological signals: IoT framework for healthcare and distance learning in COVID-19

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Awais, Muhammad, Raza, Mohsin, Singh, Nishant, Bashir, Kiran, Manzoor, Umar, Islam, Saif Ul and Rodrigues, Joel J. P. C. (2021) LSTM-based emotion detection using physiological signals: IoT framework for healthcare and distance learning in COVID-19. IEEE Internet of Things Journal, 8 (23). pp. 16863-16871. ISSN 2327-4662

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Abstract

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike the coronavirus (Covid-19) outbreak, a remote Internet of Things (IoT) enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework that enables wireless communication of physiological signals to data processing hub where long short-term memory (LSTM)-based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions that enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In the proposed IoT protocols (TS-MAC and R-MAC), ultralow latency of 1 ms is achieved. R-MAC also offers improved reliability in comparison to state of the art. In addition, the proposed deep learning scheme offers high performance (f-score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support, and general wellbeing.

Item Type:	Article
Uncontrolled Keywords:	artificial intelligence (ai),coronavirus (covid-19), human emotion analysis,internet of things (iot),long short-term memory (lstm),wearable physiological signals,signal processing,information systems,hardware and architecture,computer science applications,computer networks and communications ,/dk/atira/pure/subjectarea/asjc/1700/1711
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Health Technologies Faculty of Science > Research Groups > Cyber Intelligence and Networks
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	16 Jun 2025 10:30
Last Modified:	16 Jun 2025 10:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99512
DOI:	10.1109/JIOT.2020.3044031

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