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Submit a ProposalEmotional Intelligence and Human-Machine Interaction in Advanced Hardware Systems
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Unlock the future of human-machine collaboration with this essential guide to designing, optimizing, and deploying the next generation of emotionally intelligent hardware systems built for the Industry 5.0 landscape.
Description
In today’s industrial landscape, there is a newly discovered necessity for frameworks that perform seamlessly with the ability to collaborate with other devices. This book explores the integration of emotional intelligence and human-machine interaction within the framework of Industry 5.0, emphasizing the role of advanced hardware systems and their design and optimization. It highlights how emotionally intelligent systems can foster seamless collaboration between humans and machines, leveraging next-generation hardware technologies like IoT-enabled devices, robotics, wearable systems, and AI-driven hardware platforms. The volume covers topics including adaptive hardware for personalized human-machine interaction, sensor-based emotional recognition, the design and optimization of intelligent hardware systems, and ethical considerations in human-machine collaboration. This work underscores the importance of creating efficient, reliable, and emotionally responsive systems through strategic hardware design and iterative optimization processes. Combining interdisciplinary insights into emotional intelligence, human-centered design, hardware innovation, and optimization strategies, this essential guide offers practical applications and theoretical frameworks to transform industries.
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In today’s industrial landscape, there is a newly discovered necessity for frameworks that perform seamlessly with the ability to collaborate with other devices. This book explores the integration of emotional intelligence and human-machine interaction within the framework of Industry 5.0, emphasizing the role of advanced hardware systems and their design and optimization. It highlights how emotionally intelligent systems can foster seamless collaboration between humans and machines, leveraging next-generation hardware technologies like IoT-enabled devices, robotics, wearable systems, and AI-driven hardware platforms. The volume covers topics including adaptive hardware for personalized human-machine interaction, sensor-based emotional recognition, the design and optimization of intelligent hardware systems, and ethical considerations in human-machine collaboration. This work underscores the importance of creating efficient, reliable, and emotionally responsive systems through strategic hardware design and iterative optimization processes. Combining interdisciplinary insights into emotional intelligence, human-centered design, hardware innovation, and optimization strategies, this essential guide offers practical applications and theoretical frameworks to transform industries.
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Author / Editor Details
Chandra Singh, PhD is an Assistant Professor in the Department of Electronics and Communications Engineering at Nitte University. He has published more than eight books, more than 30 papers in international journals, and eight patents. His research interests include optical communication, networking, and wireless communication.
Chandra Singh, PhD is an Assistant Professor in the Department of Electronics and Communications Engineering at Nitte University. He has published more than eight books, more than 30 papers in international journals, and eight patents. His research interests include optical communication, networking, and wireless communication.
Rathishchandra R. Gatti, PhD is a Professor and Head of the Department of Mechanical Robots and Engineering at Nitte University. He has published more than seven books, more than 30 papers in international journals, and 15 patents. His research focuses on physical and medical AI and robotics.
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Table of Contents
Preface
Part I: Foundations of Emotional Intelligence in Technology
1. Investigation on Role and Impact of Emotional Intelligence in Industry 5.0
M. Al Safreen, P. Vishnu Priya and E. Fantin Irudaya Raj
1.1 Introduction
1.1.1 Industry 5.0
1.1.2 Emotional Intelligence
1.2 Role of Emotional Intelligence in Industry 5.0
1.3 Integration of EI Technologies in Industry 5.0
1.3.1 Artificial Intelligence
1.3.2 Machine Learning
1.3.3 Robotics
1.4 Emotional Intelligence and Its Impact in Industry 5.0
1.4.1 Human–Robot Collaboration
1.4.2 Workplace Well-Being
1.4.3 Leadership and Decision Making
1.4.4 Creativity and Innovation
1.4.5 Customer Relations and Experience
1.4.6 Conflict Resolution and Adaptability
1.4.7 Summary
1.5 Challenges in Integrating Emotional Intelligence in Industry 5.0
1.5.1 Technical–Human Balance
1.5.2 Human Emotions and Intelligence
1.5.3 Ethical Considerations and Bias
1.5.4 Human Factor and Adoption
1.5.5 Technical Limitation
1.5.6 Organizational and Cultural Aspects
1.5.7 Collaboration of EI in AI
1.5.8 Interdisciplinary Collaboration
1.6 Conclusion
References
2. Role of the Internet of Things in Enhancing Emotional Intelligence
P. Jeyashri, N. Siddhara and E. Fantin Irudaya Raj
2.1 Introduction
2.2 Artificial Intelligence and Machine Learning—An Overview
2.3 IoT Applications in Enhancing AI
2.3.1 Healthcare Devices
2.3.2 Smart Home
2.3.3 Virtual Reality & Augmented Reality
2.4 Emotional Intelligence & IoT in Workspace Applications
2.4.1 Role of IoT in the Workplace
2.4.2 Role of Emotional Intelligence in the Workplace
2.4.3 Integration of EI and IoT in Workspace Applications
2.5 Recent Developments and Trends in Emotional Intelligence and the Internet of Things
2.5.1 Emotional Intelligence Development
2.5.1.1 Integration with Artificial Intelligence
2.5.1.2 Workplace Applications
2.5.1.3 Mental Health Technologies
2.5.1.4 Educational Curricula
2.5.2 Internet of Things Innovations
2.5.2.1 Smart Wearable
2.5.2.2 Home Automation
2.5.2.3 Healthcare Innovations
2.5.2.4 Data Privacy and Ethics
2.5.3 Convergence of EI and IoT
2.5.3.1 Emotion-Aware Systems
2.5.3.2 Enhanced User Engagement
2.5.3.3 Smart Environments
2.5.4 Future Directions
2.5.5 Summary
2.6 Conclusion
References
3. Emotional Intelligence in AI-Driven Human–Machine Collaboration
Mohammed Shihan Sheikh, Rathishchandra R. Gatti, Mranila P. and Chandra Singh
3.1 Introduction
3.2 EI in AI and Its Significance in Human–Machine Collaboration
3.2.1 Foundations of EI in AI
3.2.2 Applications of EI in AI-Driven Human–Machine Collaboration
3.2.3 Challenges and Ethical Considerations in AI-Based EI
3.3 Development of Emotionally Intelligent AI Interfaces and Supporting Technologies
3.3.1 Natural Language Processing for Emotional Understanding
3.3.2 Facial Expression Recognition in AI
3.3.3 Physiological Sensors for Emotion Detection
3.4 Enhancing Collaboration, Communication, and Decision Making through EI-Based Systems
3.5 Challenges and Future Research Directions
3.5.1 Accuracy in Emotion Detection
3.5.2 Ethical and Privacy Concerns
3.5.3 Bias in Emotion Recognition Models
3.6 Conclusion
References
4. Brain–Computer Interfaces: Direct Neural Control in Advanced Hardware Systems
Shravan Kumar, Shravan Pai, Jeevith B.T. and Rathishchandra R. Gatti
4.1 Introduction
4.2 Evolution of BCI Hardware Systems
4.2.1 Advanced Electrode Technologies
4.2.2 System-on-Chip Integration
4.2.3 Wireless Neural Interfaces
4.2.4 Minimally Invasive Neural Interfaces
4.3 Advances in BCI Computing
4.3.1 Real-Time Neural Signal Processing
4.3.2 Neuromorphic Computing Integration
4.3.3 Machine Learning and Deep Neural Networks
4.3.4 Data Compression and Transmission
4.4 Clinical Applications and Therapeutic Outcomes
4.4.1 Stroke Rehabilitation
4.4.2 Neuroprosthetic Control
4.4.3 Communication Interfaces for ALS
4.4.4 Epilepsy Monitoring and Control
4.4.5 Attention and Cognitive Disorders
4.5 Direct Neural Control Mechanisms
4.5.1 Motor Cortex Decoding
4.5.2 Adaptive Learning Algorithms
4.5.3 Closed-Loop Neural Stimulation
4.5.4 Multi-Modal Integration
4.6 Current Challenges and Future Directions
4.6.1 Signal Quality and Longevity
4.6.2 Computational Efficiency
4.6.3 Regulatory and Ethical Considerations
4.7 Future Technological Directions
4.8 Conclusion
References
Part II: Technologies Enabling Emotional Intelligence in Machines
5. Engineering Empathy: Building Machines That Feel
Savidhan Shetty C. S. and Manjunatha Badiger
5.1 Introduction
5.2 The Role of IoT in Enhancing Emotional Intelligence in Machines
5.3 Human Emotions Detection through IoT
5.4 Wearable Systems
5.5 Problems You Have to Overcome in the Process of Emotional AI
5.6 Industrial Applications of Emotional AI and IoT
5.6.1 Customer Experience and Sentiment Analysis
5.6.2 Industrial Training and Simulation
5.7 Underwater Optical Wireless Communication and Emotional AI
5.7.1 Emotional AI for Diver Monitoring and Safety
5.7.2 Enhancing Human–AUV Interaction in Underwater Environments
5.7.3 Emotion-Aware Marine Life Monitoring
5.7.4 Challenges and Future Prospects
5.8 Neuroscience and Emotional AI: Understanding the Brain-Emotion Connection
5.8.1 The Neurological Basis of Emotions
5.8.2 Brain Waves and Emotion Detection
5.8.3 Neurotransmitters Role in Emotions
5.8.4 Models Based on Principles of Brain Functions
5.9 The Skill to Process Emotions from Many Sources of Data
5.9.1 Behavioral Cues
5.9.1.1 Analyzing the Movement of a Person’s Face
5.9.1.2 Text-Based Sentiment Analysis
5.9.2 Physiological Cues in Emotion Recognition
5.9.2.1 EEG for Brain Activity Monitoring
5.9.2.2 Heart Rate Variability and Emotion Recognition
5.9.2.3 Galvanic Skin Response (GSR) and Emotional Arousal
5.9.3 Multimodal Fusion Strategies
5.10 Facial Expression Analysis in AI Systems
5.11 Speech Emotion Identification
5.12 Healthcare Applications: Emotional AI for Mental Health and Well-Being
5.13 Smart Homes and Emotion-Aware IoT Environments
5.14 Conclusion
References
6. Machine Learning and Predictive Analytics to Enhance Emotional Intelligence Using IoT in Industrial Systems
Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.4 Experimental Results
6.5 Conclusion
References
7. Smart Robotics with Emotional Intelligence: A Fusion of AI, IoT, and ML
Dankan Gowda V., Supriya Devi, Sadashiva V. Chakrasali, Kottala Sri Yogi and Mandeep Singh
7.1 Introduction
7.2 Background and Motivation
7.3 Literature Survey
7.3.1 AI and Robotics
7.3.2 Emotional Intelligence in Robots
7.4 Machine Learning for Emotional Recognition
7.4.1 IoT and Robotics
7.5 Use Cases and Applications
7.6 Results and Discussions
7.6.1 Key Achievements in Emotionally Intelligent Robotics
7.6.2 Integration of AI, IoT, and ML
7.6.3 Challenges in Implementation
7.6.4 Future of Smart Robots with Emotional Intelligence
7.7 Conclusion
References
8. Data-Driven Emotional Intelligence: AI and IoT Synergy in Human–Machine Collaboration
Dankan Gowda V., Sadashiva V. Chakrasali, Manoj Kumar S. B., Kottala Sri Yogi and Nidal Al Said
8.1 Introduction
8.1.1 Overview of Emotional Intelligence
8.1.2 The Need for Data-Driven EI
8.1.3 Synergy of AI and IoT in EI
8.2 Literature Survey
8.2.1 EI Models
8.2.2 AI’s Role in EI
8.3 Key Studies on Emotion Recognition via Facial Expressions, Speech Analysis, and Physiological Signals
8.4 AI Models for Emotion Detection and Interaction
8.5 IoT and EI
8.6 Applications in Human–Machine Collaboration
8.7 Results and Discussion
8.7.1 Technological Synergy of AI and IoT for EI
8.7.2 Impact on Human–Machine Interaction
8.7.3 Challenges and Limitations
8.7.4 Opportunities for Future Development
8.8 Conclusion
References
9. Integrating AI, IoT, and ML for Seamless Human-Centric Optimization
Dankan Gowda V., Kavitha B. C., V. Nuthan Prasad, K.D.V. Prasad and Nidal Al Said
9.1 Introduction
9.1.1 Human-Centric Optimization
9.1.2 Objective
9.2 Literature Survey
9.2.1 AI in Human-Centric Optimization
9.2.2 IoT’s Role in Data Collection and Optimization
9.2.3 ML for Predictive Analysis
9.2.4 Case Studies
9.2.5 Challenges in Integration
9.3 Proposed Integration Framework
9.3.1 AI, IoT, and ML Synergy
9.3.2 Architecture of the Integrated System
9.3.3 Examples of Real-World Applications
9.4 Results and Discussion
9.4.1 Performance Metrics
9.4.2 Impact on User Experience
9.4.3 Evaluation of Case Studies
9.4.4 Comparative Analysis
9.5 Conclusion
References
Part III: Emotional Intelligence in Robotics and Cobots
10. AI-Driven Emotional Intelligence in Next-Generation Robotics
Babitha Hemanth, Khushi Rai and Harshith K.
10.1 Emotion Recognition in Robotics
10.2 Multimodal Emotion Detection Techniques
10.2.1 Facial Expression Analysis
10.2.2 Speech-Based Sentiment Detection
10.3 AI Models for Emotion Recognition
10.3.1 System Architecture for Emotionally Intelligent Robots
10.3.2 Hardware Infrastructure
10.3.3 Software Architecture
10.3.3.1 Core AI Frameworks
10.3.3.2 Robotic Middleware
10.3.3.3 Edge-Cloud Computing Paradigm
10.3.3.4 Cloud Backend
10.3.3.5 Hybrid Coordination
10.4 Challenges and Ethical Considerations in Emotion AI and Robotics
10.4.1 Bias and Accuracy Issues in Emotion AI
10.4.2 Data Privacy in Emotion Recognition
10.4.3 Avoiding Emotional Manipulation in Human–Robot Interaction
References
11. Emotionally Intelligent Systems: Human-Centered AI for Next-Gen Robotics
Smitha Gayathri D., Roopashree C. S., Kumar P. and Santhosh Kumar R.
11.1 Introduction
11.2 Fundamentals of Emotional Intelligence in Machines
11.3 EI in Robotics
11.3.1 Automatic Emotion Recognition (Sense)
11.3.2 Robots’ Capacity to Exhibit Emotions (Act)
11.4 Emotion-Based Assistive System for Active HRI
11.4.1 Prototype of the Robotic System
11.4.2 Auditory Interface
11.4.3 Control Loop
11.5 Multimodal Emotion Detection for System Personalization in HRI
11.6 Recursive Emotion Analysis
11.6.1 Long Short-Term Memory
11.7 Experimental Results
11.7.1 Emotion Sequence Analysis
11.8 Conclusion
Bibliography
12. Neurocomputational Models for Emotional Intelligence in Robotics: A Review
Shravan Kumar, Shraddha P., Deeksha M. and Rathishchandra R. Gatti
12.1 Introduction
12.2 Foundations of EI in Robotics
12.3 Neurocomputational Approaches to Emotional Intelligence in Robotics
12.3.1 Affective Computing and Emotion Recognition
12.3.2 Physiological Signals for Emotion Recognition
12.3.3 Cognitive Architectures for Emotional Intelligence
12.3.4 Reinforcement Learning & Adaptive Emotion Regulation
12.3.5 Neuro-Symbolic AI for Emotion Processing
12.4 Applications and Case Studies
12.4.1 Healthcare Robotics
12.4.2 Customer Service and Social Robotics
12.4.3 Educational AI and Tutoring Systems
12.4.4 Collaborative Robotics in Workspaces
12.5 Challenges and Open Issues
12.5.1 Data Limitations & Ethical Concerns
12.5.2 Interpretability & Trust in Neurocomputational Models
12.5.3 Generalization & Adaptability
12.5.4 Computational Constraints in Real-Time Robotics
Bibliography
Part IV: Algorithms and Models for Emotion Recognition
13. Machine Learning Algorithms for Emotion Recognition in Advanced Hardware
Swati Patil, Dankan Gowda V., K.D.V. Prasad, Ved Srinivas and Srinivas D.
13.1 Introduction
13.2 Literature Survey
13.2.1 Historical Development of Emotion Recognition
13.2.2 The Role of Traditional Machine Learning Methods
13.2.3 Recent Advances in Machine Learning for Emotion Recognition
13.2.4 Integration with Advanced Hardware
13.2.5 Challenges in Emotion Recognition
13.3 Machine Learning Algorithms for Emotion Recognition
13.3.1 Supervised Learning Techniques
13.3.2 Deep Learning Techniques
13.3.3 Unsupervised Learning and Clustering Techniques
13.3.4 Multimodal Emotion Recognition
13.3.5 Real-Time Emotion Recognition Models
13.4 Results and Discussions
13.4.1 Benchmarking Emotion Recognition Models
13.4.2 Performance Evaluation on Advanced Hardware
13.5 Challenges and Limitations
13.6 Recent Case Studies
13.7 Conclusion
References
14. A New Hybrid Deep Learning Framework for Emotion Recognition Based on ResNet50 and Contextual Features
Tanuja Pande, Abhimanyu Dutonde and Anita Yadav
14.1 Introduction
14.1.1 Summary of Literature Review
14.2 Methodology
14.2.1 Dataset Pre-Processing
14.3 Conclusion
References
15. Multiclass Depression Detection Using Bidirectional Hybrid Deep Learning Model
Nikhil E. Karale and Vijay S. Gulhane
15.1 Introduction
15.2 Literature Survey
15.3 Dataset
15.3.1 Dataset Description
15.3.2 Dataset Pre-Processing
15.4 Methodology
15.4.1 Hybrid CNN-BI-LSTM Architectures
15.5 Result and Analysis
15.6 Conclusion and Future Scope
References
16. Feature-Evolved Deep Learning for Heart Disease Diagnosis: A Genetic Neural Network Model
Shwetha N., Aravind Jadhav, Sangeetha N., Roopesh Ramesh, Rangaswamy Y. and Chandra Singh
16.1 Introduction
16.2 Scope of the Work
16.2.1 Literature Survey
16.2.2 Problem Identification
16.3 Proposed Methodology
16.3.1 ANN Classification
16.3.2 Gradient Descent
16.3.3 Genetic Algorithm
16.3.4 Benefits of Genetic Algorithm in Neural Network
16.4 Software Implementation Requirements
16.5 Results and Discussion
16.6 Conclusion and Future Scope
Bibliography
Part V: Future Systems and Human-Machine Interfaces
17. AI-Powered Human-Machine Feedback Systems for Adaptive Interfaces
Dankan Gowda V., Kavitha B. C., V. Nuthan Prasad, K.D.V. Prasad and Srinivas D.
17.1 Introduction
17.1.1 Overview of Human-Machine Feedback Systems
17.1.2 Importance of Adaptive Interfaces
17.1.3 Role of AI in Adaptive Feedback Systems
17.2 Literature Survey
17.2.1 Evolution of Human–Machine Interfaces
17.2.1.1 Early Feedback Systems
17.2.1.2 Advancements in HMI
17.2.2 AI in Adaptive Interfaces
17.2.2.1 AI Algorithms for Adaptation
17.2.2.2 User-Centric Design Approaches
17.3 Types of Feedback in Human-Machine Systems
17.3.1 Visual Feedback
17.3.2 Auditory Feedback
17.3.3 Tactile Feedback
17.3.4 Multimodal Feedback
17.4 Challenges in Implementing AI for Adaptive Systems
17.4.1 Data Collection and Privacy
17.4.2 Contextual Understanding
17.4.3 Scalability and Performance
17.5 Results and Discussions
17.5.1 Key Findings from AI-Powered Adaptive Interfaces
17.5.1.1 User Experience Improvement
17.5.1.2 Real-World Applications
17.5.2 Case Studies
17.5.2.1 Smart Homes
17.5.2.2 Healthcare Systems
17.5.2.3 Gaming Systems
17.5.3 Challenges in Implementation
17.5.3.1 User Trust in Adaptive Systems
17.5.3.2 AI Feedback Accuracy
17.5.3.3 Real-Time Adaptation
17.6 Future Directions
17.6.1 AI Integration with Emerging Technologies
17.6.2 Continuous Learning Systems
17.7 Conclusion
References
18. Mental Well-Being of Adolescents: A Comparison of Day School and Boarding School
Usha Desai, Susha M. and Raghavan K. P.
18.1 Introduction
18.2 Data Collection and Methodology
18.3 Outcome and Discussion
18.3.1 Day School
18.3.1.1 Recommendations for Students
18.3.1.2 Recommendations for Parents
18.3.1.3 Recommendations for Teachers
18.3.2 Boarding School
18.3.2.1 Recommendations for Students
18.3.2.2 Recommendations for Parents
18.3.2.3 Recommendations for Teachers and School Staff
18.4 Conclusion
Acknowledgment
References
19. Hippocampus Sclerosis Segmentation by Vanilla U-Net Model Prediction
Jayanthi Vajiram, Sivakumar S., Nanditha H.G., Chennagiri Rajarao Padma and Usha Desai
19.1 Introduction
19.2 Related Survey
19.3 Model Implementations
19.4 Methodology
19.5 Evaluation Metrics
19.5.1 Hessian Metric and Eigen Values of the Images
19.5.2 Mean Square Error
19.6 Results
19.7 Conclusion
References
20. Chernoff Bound and Bhattacharyya Bound Feature Ranking Approach for Epilepsy Detection
Usha Desai, Roshan J. Martis, Dilna Udayan and Susha M.
20.1 Introduction
20.2 Materials and Methodology
20.2.1 EEG Dataset and Preprocessing
20.2.2 Feature Extraction
20.2.2.1 Wavelet Packet Decomposition
20.2.2.2 Mean
20.2.2.3 Standard Deviation
20.2.2.4 Skewness
20.2.2.5 Entropy
20.2.2.6 Kurtosis
20.2.2.7 Approximate Entropy
20.2.2.8 Sample Entropy
20.2.2.9 Hurst Exponent
20.2.3 Feature Selection and Ranking
20.2.4 Classification
20.3 Results and Discussion
20.4 Conclusion
References
21. Emotional Intelligence Techniques in Humanoid Robotics
Rathishchandra R. Gatti
21.1 Introduction
21.2 Conceptualizing Emotional Intelligence in Humanoid Robotics
21.3 Emotion Recognition Techniques: Sensing Human Affect
21.3.1 Facial Expression Recognition
21.3.2 Vocal Emotion Recognition (VER)
21.3.3 Physiological Signal Analysis
21.3.4 Textual Emotion Analysis (Sentiment Analysis)
21.4 Emotion Synthesis and Expression Techniques: Giving Robots Affective Presence
21.4.1 Facial Expression Synthesis
21.4.2 Vocal Emotion Synthesis
21.4.3 Body Language and Gesture Synthesis
21.5 Emotion Modeling and Regulation: Toward Deeper Understanding and Adaptation
21.5.1 Computational Models of Emotion
21.5.2 Emotion Regulation and Affective Adaptation
21.6 Multimodal Approaches: Integrating Affective Channels
21.7 Applications of Emotionally Intelligent Humanoid Robots
21.8 Challenges and Limitations: Hurdles on the Path to EI
21.9 Ethical Considerations: The Moral Landscape of Emotional AI
21.10 Future Directions: Charting the Next Wave of Robotic EI
21.11 Conclusion
References
Back to Top
Preface
Part I: Foundations of Emotional Intelligence in Technology
1. Investigation on Role and Impact of Emotional Intelligence in Industry 5.0
M. Al Safreen, P. Vishnu Priya and E. Fantin Irudaya Raj
1.1 Introduction
1.1.1 Industry 5.0
1.1.2 Emotional Intelligence
1.2 Role of Emotional Intelligence in Industry 5.0
1.3 Integration of EI Technologies in Industry 5.0
1.3.1 Artificial Intelligence
1.3.2 Machine Learning
1.3.3 Robotics
1.4 Emotional Intelligence and Its Impact in Industry 5.0
1.4.1 Human–Robot Collaboration
1.4.2 Workplace Well-Being
1.4.3 Leadership and Decision Making
1.4.4 Creativity and Innovation
1.4.5 Customer Relations and Experience
1.4.6 Conflict Resolution and Adaptability
1.4.7 Summary
1.5 Challenges in Integrating Emotional Intelligence in Industry 5.0
1.5.1 Technical–Human Balance
1.5.2 Human Emotions and Intelligence
1.5.3 Ethical Considerations and Bias
1.5.4 Human Factor and Adoption
1.5.5 Technical Limitation
1.5.6 Organizational and Cultural Aspects
1.5.7 Collaboration of EI in AI
1.5.8 Interdisciplinary Collaboration
1.6 Conclusion
References
2. Role of the Internet of Things in Enhancing Emotional Intelligence
P. Jeyashri, N. Siddhara and E. Fantin Irudaya Raj
2.1 Introduction
2.2 Artificial Intelligence and Machine Learning—An Overview
2.3 IoT Applications in Enhancing AI
2.3.1 Healthcare Devices
2.3.2 Smart Home
2.3.3 Virtual Reality & Augmented Reality
2.4 Emotional Intelligence & IoT in Workspace Applications
2.4.1 Role of IoT in the Workplace
2.4.2 Role of Emotional Intelligence in the Workplace
2.4.3 Integration of EI and IoT in Workspace Applications
2.5 Recent Developments and Trends in Emotional Intelligence and the Internet of Things
2.5.1 Emotional Intelligence Development
2.5.1.1 Integration with Artificial Intelligence
2.5.1.2 Workplace Applications
2.5.1.3 Mental Health Technologies
2.5.1.4 Educational Curricula
2.5.2 Internet of Things Innovations
2.5.2.1 Smart Wearable
2.5.2.2 Home Automation
2.5.2.3 Healthcare Innovations
2.5.2.4 Data Privacy and Ethics
2.5.3 Convergence of EI and IoT
2.5.3.1 Emotion-Aware Systems
2.5.3.2 Enhanced User Engagement
2.5.3.3 Smart Environments
2.5.4 Future Directions
2.5.5 Summary
2.6 Conclusion
References
3. Emotional Intelligence in AI-Driven Human–Machine Collaboration
Mohammed Shihan Sheikh, Rathishchandra R. Gatti, Mranila P. and Chandra Singh
3.1 Introduction
3.2 EI in AI and Its Significance in Human–Machine Collaboration
3.2.1 Foundations of EI in AI
3.2.2 Applications of EI in AI-Driven Human–Machine Collaboration
3.2.3 Challenges and Ethical Considerations in AI-Based EI
3.3 Development of Emotionally Intelligent AI Interfaces and Supporting Technologies
3.3.1 Natural Language Processing for Emotional Understanding
3.3.2 Facial Expression Recognition in AI
3.3.3 Physiological Sensors for Emotion Detection
3.4 Enhancing Collaboration, Communication, and Decision Making through EI-Based Systems
3.5 Challenges and Future Research Directions
3.5.1 Accuracy in Emotion Detection
3.5.2 Ethical and Privacy Concerns
3.5.3 Bias in Emotion Recognition Models
3.6 Conclusion
References
4. Brain–Computer Interfaces: Direct Neural Control in Advanced Hardware Systems
Shravan Kumar, Shravan Pai, Jeevith B.T. and Rathishchandra R. Gatti
4.1 Introduction
4.2 Evolution of BCI Hardware Systems
4.2.1 Advanced Electrode Technologies
4.2.2 System-on-Chip Integration
4.2.3 Wireless Neural Interfaces
4.2.4 Minimally Invasive Neural Interfaces
4.3 Advances in BCI Computing
4.3.1 Real-Time Neural Signal Processing
4.3.2 Neuromorphic Computing Integration
4.3.3 Machine Learning and Deep Neural Networks
4.3.4 Data Compression and Transmission
4.4 Clinical Applications and Therapeutic Outcomes
4.4.1 Stroke Rehabilitation
4.4.2 Neuroprosthetic Control
4.4.3 Communication Interfaces for ALS
4.4.4 Epilepsy Monitoring and Control
4.4.5 Attention and Cognitive Disorders
4.5 Direct Neural Control Mechanisms
4.5.1 Motor Cortex Decoding
4.5.2 Adaptive Learning Algorithms
4.5.3 Closed-Loop Neural Stimulation
4.5.4 Multi-Modal Integration
4.6 Current Challenges and Future Directions
4.6.1 Signal Quality and Longevity
4.6.2 Computational Efficiency
4.6.3 Regulatory and Ethical Considerations
4.7 Future Technological Directions
4.8 Conclusion
References
Part II: Technologies Enabling Emotional Intelligence in Machines
5. Engineering Empathy: Building Machines That Feel
Savidhan Shetty C. S. and Manjunatha Badiger
5.1 Introduction
5.2 The Role of IoT in Enhancing Emotional Intelligence in Machines
5.3 Human Emotions Detection through IoT
5.4 Wearable Systems
5.5 Problems You Have to Overcome in the Process of Emotional AI
5.6 Industrial Applications of Emotional AI and IoT
5.6.1 Customer Experience and Sentiment Analysis
5.6.2 Industrial Training and Simulation
5.7 Underwater Optical Wireless Communication and Emotional AI
5.7.1 Emotional AI for Diver Monitoring and Safety
5.7.2 Enhancing Human–AUV Interaction in Underwater Environments
5.7.3 Emotion-Aware Marine Life Monitoring
5.7.4 Challenges and Future Prospects
5.8 Neuroscience and Emotional AI: Understanding the Brain-Emotion Connection
5.8.1 The Neurological Basis of Emotions
5.8.2 Brain Waves and Emotion Detection
5.8.3 Neurotransmitters Role in Emotions
5.8.4 Models Based on Principles of Brain Functions
5.9 The Skill to Process Emotions from Many Sources of Data
5.9.1 Behavioral Cues
5.9.1.1 Analyzing the Movement of a Person’s Face
5.9.1.2 Text-Based Sentiment Analysis
5.9.2 Physiological Cues in Emotion Recognition
5.9.2.1 EEG for Brain Activity Monitoring
5.9.2.2 Heart Rate Variability and Emotion Recognition
5.9.2.3 Galvanic Skin Response (GSR) and Emotional Arousal
5.9.3 Multimodal Fusion Strategies
5.10 Facial Expression Analysis in AI Systems
5.11 Speech Emotion Identification
5.12 Healthcare Applications: Emotional AI for Mental Health and Well-Being
5.13 Smart Homes and Emotion-Aware IoT Environments
5.14 Conclusion
References
6. Machine Learning and Predictive Analytics to Enhance Emotional Intelligence Using IoT in Industrial Systems
Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan
6.1 Introduction
6.2 Literature Review
6.3 Methodology
6.4 Experimental Results
6.5 Conclusion
References
7. Smart Robotics with Emotional Intelligence: A Fusion of AI, IoT, and ML
Dankan Gowda V., Supriya Devi, Sadashiva V. Chakrasali, Kottala Sri Yogi and Mandeep Singh
7.1 Introduction
7.2 Background and Motivation
7.3 Literature Survey
7.3.1 AI and Robotics
7.3.2 Emotional Intelligence in Robots
7.4 Machine Learning for Emotional Recognition
7.4.1 IoT and Robotics
7.5 Use Cases and Applications
7.6 Results and Discussions
7.6.1 Key Achievements in Emotionally Intelligent Robotics
7.6.2 Integration of AI, IoT, and ML
7.6.3 Challenges in Implementation
7.6.4 Future of Smart Robots with Emotional Intelligence
7.7 Conclusion
References
8. Data-Driven Emotional Intelligence: AI and IoT Synergy in Human–Machine Collaboration
Dankan Gowda V., Sadashiva V. Chakrasali, Manoj Kumar S. B., Kottala Sri Yogi and Nidal Al Said
8.1 Introduction
8.1.1 Overview of Emotional Intelligence
8.1.2 The Need for Data-Driven EI
8.1.3 Synergy of AI and IoT in EI
8.2 Literature Survey
8.2.1 EI Models
8.2.2 AI’s Role in EI
8.3 Key Studies on Emotion Recognition via Facial Expressions, Speech Analysis, and Physiological Signals
8.4 AI Models for Emotion Detection and Interaction
8.5 IoT and EI
8.6 Applications in Human–Machine Collaboration
8.7 Results and Discussion
8.7.1 Technological Synergy of AI and IoT for EI
8.7.2 Impact on Human–Machine Interaction
8.7.3 Challenges and Limitations
8.7.4 Opportunities for Future Development
8.8 Conclusion
References
9. Integrating AI, IoT, and ML for Seamless Human-Centric Optimization
Dankan Gowda V., Kavitha B. C., V. Nuthan Prasad, K.D.V. Prasad and Nidal Al Said
9.1 Introduction
9.1.1 Human-Centric Optimization
9.1.2 Objective
9.2 Literature Survey
9.2.1 AI in Human-Centric Optimization
9.2.2 IoT’s Role in Data Collection and Optimization
9.2.3 ML for Predictive Analysis
9.2.4 Case Studies
9.2.5 Challenges in Integration
9.3 Proposed Integration Framework
9.3.1 AI, IoT, and ML Synergy
9.3.2 Architecture of the Integrated System
9.3.3 Examples of Real-World Applications
9.4 Results and Discussion
9.4.1 Performance Metrics
9.4.2 Impact on User Experience
9.4.3 Evaluation of Case Studies
9.4.4 Comparative Analysis
9.5 Conclusion
References
Part III: Emotional Intelligence in Robotics and Cobots
10. AI-Driven Emotional Intelligence in Next-Generation Robotics
Babitha Hemanth, Khushi Rai and Harshith K.
10.1 Emotion Recognition in Robotics
10.2 Multimodal Emotion Detection Techniques
10.2.1 Facial Expression Analysis
10.2.2 Speech-Based Sentiment Detection
10.3 AI Models for Emotion Recognition
10.3.1 System Architecture for Emotionally Intelligent Robots
10.3.2 Hardware Infrastructure
10.3.3 Software Architecture
10.3.3.1 Core AI Frameworks
10.3.3.2 Robotic Middleware
10.3.3.3 Edge-Cloud Computing Paradigm
10.3.3.4 Cloud Backend
10.3.3.5 Hybrid Coordination
10.4 Challenges and Ethical Considerations in Emotion AI and Robotics
10.4.1 Bias and Accuracy Issues in Emotion AI
10.4.2 Data Privacy in Emotion Recognition
10.4.3 Avoiding Emotional Manipulation in Human–Robot Interaction
References
11. Emotionally Intelligent Systems: Human-Centered AI for Next-Gen Robotics
Smitha Gayathri D., Roopashree C. S., Kumar P. and Santhosh Kumar R.
11.1 Introduction
11.2 Fundamentals of Emotional Intelligence in Machines
11.3 EI in Robotics
11.3.1 Automatic Emotion Recognition (Sense)
11.3.2 Robots’ Capacity to Exhibit Emotions (Act)
11.4 Emotion-Based Assistive System for Active HRI
11.4.1 Prototype of the Robotic System
11.4.2 Auditory Interface
11.4.3 Control Loop
11.5 Multimodal Emotion Detection for System Personalization in HRI
11.6 Recursive Emotion Analysis
11.6.1 Long Short-Term Memory
11.7 Experimental Results
11.7.1 Emotion Sequence Analysis
11.8 Conclusion
Bibliography
12. Neurocomputational Models for Emotional Intelligence in Robotics: A Review
Shravan Kumar, Shraddha P., Deeksha M. and Rathishchandra R. Gatti
12.1 Introduction
12.2 Foundations of EI in Robotics
12.3 Neurocomputational Approaches to Emotional Intelligence in Robotics
12.3.1 Affective Computing and Emotion Recognition
12.3.2 Physiological Signals for Emotion Recognition
12.3.3 Cognitive Architectures for Emotional Intelligence
12.3.4 Reinforcement Learning & Adaptive Emotion Regulation
12.3.5 Neuro-Symbolic AI for Emotion Processing
12.4 Applications and Case Studies
12.4.1 Healthcare Robotics
12.4.2 Customer Service and Social Robotics
12.4.3 Educational AI and Tutoring Systems
12.4.4 Collaborative Robotics in Workspaces
12.5 Challenges and Open Issues
12.5.1 Data Limitations & Ethical Concerns
12.5.2 Interpretability & Trust in Neurocomputational Models
12.5.3 Generalization & Adaptability
12.5.4 Computational Constraints in Real-Time Robotics
Bibliography
Part IV: Algorithms and Models for Emotion Recognition
13. Machine Learning Algorithms for Emotion Recognition in Advanced Hardware
Swati Patil, Dankan Gowda V., K.D.V. Prasad, Ved Srinivas and Srinivas D.
13.1 Introduction
13.2 Literature Survey
13.2.1 Historical Development of Emotion Recognition
13.2.2 The Role of Traditional Machine Learning Methods
13.2.3 Recent Advances in Machine Learning for Emotion Recognition
13.2.4 Integration with Advanced Hardware
13.2.5 Challenges in Emotion Recognition
13.3 Machine Learning Algorithms for Emotion Recognition
13.3.1 Supervised Learning Techniques
13.3.2 Deep Learning Techniques
13.3.3 Unsupervised Learning and Clustering Techniques
13.3.4 Multimodal Emotion Recognition
13.3.5 Real-Time Emotion Recognition Models
13.4 Results and Discussions
13.4.1 Benchmarking Emotion Recognition Models
13.4.2 Performance Evaluation on Advanced Hardware
13.5 Challenges and Limitations
13.6 Recent Case Studies
13.7 Conclusion
References
14. A New Hybrid Deep Learning Framework for Emotion Recognition Based on ResNet50 and Contextual Features
Tanuja Pande, Abhimanyu Dutonde and Anita Yadav
14.1 Introduction
14.1.1 Summary of Literature Review
14.2 Methodology
14.2.1 Dataset Pre-Processing
14.3 Conclusion
References
15. Multiclass Depression Detection Using Bidirectional Hybrid Deep Learning Model
Nikhil E. Karale and Vijay S. Gulhane
15.1 Introduction
15.2 Literature Survey
15.3 Dataset
15.3.1 Dataset Description
15.3.2 Dataset Pre-Processing
15.4 Methodology
15.4.1 Hybrid CNN-BI-LSTM Architectures
15.5 Result and Analysis
15.6 Conclusion and Future Scope
References
16. Feature-Evolved Deep Learning for Heart Disease Diagnosis: A Genetic Neural Network Model
Shwetha N., Aravind Jadhav, Sangeetha N., Roopesh Ramesh, Rangaswamy Y. and Chandra Singh
16.1 Introduction
16.2 Scope of the Work
16.2.1 Literature Survey
16.2.2 Problem Identification
16.3 Proposed Methodology
16.3.1 ANN Classification
16.3.2 Gradient Descent
16.3.3 Genetic Algorithm
16.3.4 Benefits of Genetic Algorithm in Neural Network
16.4 Software Implementation Requirements
16.5 Results and Discussion
16.6 Conclusion and Future Scope
Bibliography
Part V: Future Systems and Human-Machine Interfaces
17. AI-Powered Human-Machine Feedback Systems for Adaptive Interfaces
Dankan Gowda V., Kavitha B. C., V. Nuthan Prasad, K.D.V. Prasad and Srinivas D.
17.1 Introduction
17.1.1 Overview of Human-Machine Feedback Systems
17.1.2 Importance of Adaptive Interfaces
17.1.3 Role of AI in Adaptive Feedback Systems
17.2 Literature Survey
17.2.1 Evolution of Human–Machine Interfaces
17.2.1.1 Early Feedback Systems
17.2.1.2 Advancements in HMI
17.2.2 AI in Adaptive Interfaces
17.2.2.1 AI Algorithms for Adaptation
17.2.2.2 User-Centric Design Approaches
17.3 Types of Feedback in Human-Machine Systems
17.3.1 Visual Feedback
17.3.2 Auditory Feedback
17.3.3 Tactile Feedback
17.3.4 Multimodal Feedback
17.4 Challenges in Implementing AI for Adaptive Systems
17.4.1 Data Collection and Privacy
17.4.2 Contextual Understanding
17.4.3 Scalability and Performance
17.5 Results and Discussions
17.5.1 Key Findings from AI-Powered Adaptive Interfaces
17.5.1.1 User Experience Improvement
17.5.1.2 Real-World Applications
17.5.2 Case Studies
17.5.2.1 Smart Homes
17.5.2.2 Healthcare Systems
17.5.2.3 Gaming Systems
17.5.3 Challenges in Implementation
17.5.3.1 User Trust in Adaptive Systems
17.5.3.2 AI Feedback Accuracy
17.5.3.3 Real-Time Adaptation
17.6 Future Directions
17.6.1 AI Integration with Emerging Technologies
17.6.2 Continuous Learning Systems
17.7 Conclusion
References
18. Mental Well-Being of Adolescents: A Comparison of Day School and Boarding School
Usha Desai, Susha M. and Raghavan K. P.
18.1 Introduction
18.2 Data Collection and Methodology
18.3 Outcome and Discussion
18.3.1 Day School
18.3.1.1 Recommendations for Students
18.3.1.2 Recommendations for Parents
18.3.1.3 Recommendations for Teachers
18.3.2 Boarding School
18.3.2.1 Recommendations for Students
18.3.2.2 Recommendations for Parents
18.3.2.3 Recommendations for Teachers and School Staff
18.4 Conclusion
Acknowledgment
References
19. Hippocampus Sclerosis Segmentation by Vanilla U-Net Model Prediction
Jayanthi Vajiram, Sivakumar S., Nanditha H.G., Chennagiri Rajarao Padma and Usha Desai
19.1 Introduction
19.2 Related Survey
19.3 Model Implementations
19.4 Methodology
19.5 Evaluation Metrics
19.5.1 Hessian Metric and Eigen Values of the Images
19.5.2 Mean Square Error
19.6 Results
19.7 Conclusion
References
20. Chernoff Bound and Bhattacharyya Bound Feature Ranking Approach for Epilepsy Detection
Usha Desai, Roshan J. Martis, Dilna Udayan and Susha M.
20.1 Introduction
20.2 Materials and Methodology
20.2.1 EEG Dataset and Preprocessing
20.2.2 Feature Extraction
20.2.2.1 Wavelet Packet Decomposition
20.2.2.2 Mean
20.2.2.3 Standard Deviation
20.2.2.4 Skewness
20.2.2.5 Entropy
20.2.2.6 Kurtosis
20.2.2.7 Approximate Entropy
20.2.2.8 Sample Entropy
20.2.2.9 Hurst Exponent
20.2.3 Feature Selection and Ranking
20.2.4 Classification
20.3 Results and Discussion
20.4 Conclusion
References
21. Emotional Intelligence Techniques in Humanoid Robotics
Rathishchandra R. Gatti
21.1 Introduction
21.2 Conceptualizing Emotional Intelligence in Humanoid Robotics
21.3 Emotion Recognition Techniques: Sensing Human Affect
21.3.1 Facial Expression Recognition
21.3.2 Vocal Emotion Recognition (VER)
21.3.3 Physiological Signal Analysis
21.3.4 Textual Emotion Analysis (Sentiment Analysis)
21.4 Emotion Synthesis and Expression Techniques: Giving Robots Affective Presence
21.4.1 Facial Expression Synthesis
21.4.2 Vocal Emotion Synthesis
21.4.3 Body Language and Gesture Synthesis
21.5 Emotion Modeling and Regulation: Toward Deeper Understanding and Adaptation
21.5.1 Computational Models of Emotion
21.5.2 Emotion Regulation and Affective Adaptation
21.6 Multimodal Approaches: Integrating Affective Channels
21.7 Applications of Emotionally Intelligent Humanoid Robots
21.8 Challenges and Limitations: Hurdles on the Path to EI
21.9 Ethical Considerations: The Moral Landscape of Emotional AI
21.10 Future Directions: Charting the Next Wave of Robotic EI
21.11 Conclusion
References
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