ArticleIEEE internet of things journal2021
LSTM-Based Emotion Detection Using Physiological Signals: IoT Framework for Healthcare and Distance Learning in COVID-19.
Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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26 citing papers in PubMed, 1 synthesis or guideline pooled it, 158 citations in OpenAlex.
- Artificial Intelligence for the Analysis of Biometric Data from Wearables in Education: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- An IoT-based smart emotion recognition system by using internal body parameters.Scientific reports · 2026Article
- Emotion recognition with multiple physiological parameters based on ensemble learning.Scientific reports · 2025Article
- Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory.PloS one · 2025Article
- Convolutional-LSTM approach for temporal catch hotspots (CATCH): an AI-driven model for spatiotemporal forecasting of fisheries catch probability densities.Biology methods & protocols · 2025Article
- IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm.Medical & biological engineering & computing · 2024Article
- Analysis of social metrics on scientific production in the field of emotion-aware education through artificial intelligence.Frontiers in artificial intelligence · 2024Article
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- Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers.Diagnostics (Basel, Switzerland) · 2023Article
- MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.Information sciences · 2023Article
- IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Trends in using deep learning algorithms in biomedical prediction systems.Frontiers in neuroscience · 2023Article
- Fusion of Emotional Thinking and Mental Health of Students in Vocal Music Teaching.Occupational therapy international · 2023Article
- Medical image analysis using deep learning algorithms.Frontiers in public health · 2023Review
- An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal.Diagnostics (Basel, Switzerland) · 2022Article
- Physical Activity Monitoring and Classification Using Machine Learning Techniques.Life (Basel, Switzerland) · 2022Article
- A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals.Sensors (Basel, Switzerland) · 2022Article
- Article
- A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19.Annals of medicine and surgery (2012) · 2022Review
- A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings.Biosensors · 2022Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
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 ([Formula: see text]-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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.