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

Muhammad Awais¹, Mohsin Raza², Nishant Singh²

¹Department of Computer ScienceEdge Hill University Ormskirk L39 4QP U.K.

IEEE Internet of Things Journal

|May 18, 2022

Summary

This summary is machine-generated.

This study introduces an Internet of Things (IoT) framework using AI for real-time emotion recognition from physiological signals. This technology aids remote health monitoring and distance learning, especially during pandemics.

Keywords:

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

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Published on: January 22, 2018

Area of Science:

Biomedical Engineering
Computer Science
Artificial Intelligence

Background:

Human emotions are intrinsically linked to physical and mental health.
Physiological signals offer an indirect but valuable measure of emotional states.
The COVID-19 pandemic highlighted the need for remote healthcare and learning solutions.

Purpose of the Study:

To propose an integrated Internet of Things (IoT) framework for remote emotion recognition.
To leverage Artificial Intelligence (AI), specifically Long Short-Term Memory (LSTM) networks, for analyzing physiological signals.
To enable real-time emotion interpretation for healthcare and distance learning applications.

Main Methods:

Development of an integrated IoT framework for wireless transmission of physiological data.
Implementation of novel IoT protocols (TS-MAC and R-MAC) for efficient data communication.
Application of a Long Short-Term Memory (LSTM) deep learning model for emotion recognition.

Main Results:

Achieved ultra-low latency of 1 ms in proposed IoT protocols (TS-MAC and R-MAC).
Demonstrated improved reliability with the R-MAC protocol compared to existing methods.
Attained a high performance score of 95% for the deep learning-based emotion recognition scheme.

Conclusions:

The proposed framework effectively integrates IoT and AI for real-time emotion recognition.
The system's low latency and high reliability support critical applications like remote health monitoring and distance learning.
The successful integration ensures suitability for enhancing student engagement, emotional support, and overall well-being.