LSTM-Based Emotion Detection Using Physiological Signals: IoT Framework for Healthcare and Distance Learning in COVID-19

Muhammad Awais*, Mohsin Raza, Nishant Singh, Kiran Bashir, Umar Manzoor, Saif Ul Islam, Joel J.P.C. Rodrigues

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)16863-16871
Number of pages9
JournalIEEE Internet of Things Journal
Issue number23
Early online date10 Dec 2020
Publication statusPublished - 1 Dec 2021

Bibliographical note

Funding Information:
This work was supported in part by the Department of Computer Science, Edge Hill University, U.K.; in part by FCT/MCTES through National Funds and when applicable cofunded EU funds under Project UIDB/50008/2020; and in part by Brazilian National Council for Scientific and Technological Development (CNPq) under Grant 309335/2017-5.

Publisher Copyright:
c IEEE 2020. This article is free to access and download, along with rights for full text and data mining, re-use and analysis.


  • Artificial intelligence (AI)
  • coronavirus (Covid-19), human emotion analysis
  • Internet of Things (IoT)
  • long short-term memory (LSTM)
  • wearable physiological signals


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