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

Keyphrases

• Memory-based 100%
• Distance Learning 100%
• Physiological Signals 100%
• Emotion Detection 100%
• Long Short-term Memory 100%
• COVID-19 100%
• Healthcare Learning 100%
• Internet of Things Framework 100%
• Healthcare 66%
• Emotion Recognition 66%
• R-MAC 66%
• Increased Reliability 33%
• High Performance 33%
• Physical Health 33%
• Mental Health 33%
• Student Engagement 33%
• Health Monitoring 33%
• Interdependency 33%
• Learning Support 33%
• Biological Phenomena 33%
• Measure of Emotions 33%
• General Well-being 33%
• Wireless Communication 33%
• Indirect Measurement 33%
• Human Emotions 33%
• Deep Learning 33%
• Internet of Things 33%
• Proposed Work 33%
• Strongly Coupled 33%
• COVID-19 Outbreak 33%
• Real-time Communication 33%
• Coronavirus COVID-19 33%
• Real-time Recognition 33%
• Physiological Phenomena 33%
• Distance Learning Students 33%
• Internet of Things Protocols 33%
• Emotion Communication 33%
• Ultra-reliable Low-latency Communication (URLLC) 33%
• Deep Learning Scheme 33%

Computer Science

• Internet-Of-Things 100%
• Physiological Signal 100%
• Distance Learning 100%
• Emotion Detection 100%
• Long Short-Term Memory Network 100%
• Artificial Intelligence 40%
• Deep Learning Method 40%
• Data Processing 20%
• Wireless Communication 20%
• Student Engagement 20%
• Interdependency 20%
• Indirect Measure 20%
• Learning Scheme 20%
• Distance Learning Student 20%

Psychology

• Short-Term Memory 100%
• Internet-Of-Things 100%
• Artificial Intelligence 40%
• Mental Health 20%