LSTM based Emotion Detection using Physiological Signals: IoT framework for Healthcare and Distance Learning in COVID-19

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 coronavirus (Covid-19) outbreak, a remote 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 which 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 which enables health monitoring and distance learning support amidst pandemics In this study, the achieved results are very promising In proposed IoT protocols (TS-MAC and R-MAC) ultra-low latency of 1 millisecond 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 IEEE