Emotional Intelligence and Human-Machine Interaction in Advanced Hardware Systems

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.

• Preface xxiiiPart I: Foundations of Emotional Intelligence in Technology 11 Investigation on Role and Impact of Emotional Intelligence in Industry 5.0 3M. Al Safreen, P. Vishnu Priya and E. Fantin Irudaya Raj1.1 Introduction 31.2 Role of Emotional Intelligence in Industry 5.0 71.3 Integration of EI Technologies in Industry 5.0 71.4 Emotional Intelligence and Its Impact in Industry 5.0 91.5 Challenges in Integrating Emotional Intelligence in Industry 5.0 131.6 Conclusion 172 Role of the Internet of Things in Enhancing Emotional Intelligence 21P. Jeyashri, N. Siddhara and E. Fantin Irudaya Raj2.1 Introduction 222.2 Artificial Intelligence and Machine Learning—An Overview 232.3 IoT Applications in Enhancing AI 252.4 Emotional Intelligence & IoT in Workspace Applications 322.5 Recent Developments and Trends in Emotional Intelligence and the Internet of Things 352.6 Conclusion 383 Emotional Intelligence in AI-Driven Human–Machine Collaboration 43Mohammed Shihan Sheikh, Rathishchandra R. Gatti, Mranila P. and Chandra Singh3.1 Introduction 443.2 EI in AI and Its Significance in Human–Machine Collaboration 453.3 Development of Emotionally Intelligent AI Interfaces and Supporting Technologies 473.4 Enhancing Collaboration, Communication, and Decision Making through EI-Based Systems 513.5 Challenges and Future Research Directions 523.6 Conclusion 534 Brain–Computer Interfaces: Direct Neural Control in Advanced Hardware Systems 57Shravan Kumar, Shravan Pai, Jeevith B.T. and Rathishchandra R. Gatti4.1 Introduction 584.2 Evolution of BCI Hardware Systems 584.3 Advances in BCI Computing 634.4 Clinical Applications and Therapeutic Outcomes 654.5 Direct Neural Control Mechanisms 694.6 Current Challenges and Future Directions 714.7 Future Technological Directions 744.8 Conclusion 74Part II: Technologies Enabling Emotional Intelligence in Machines 795 Engineering Empathy: Building Machines That Feel 81Savidhan Shetty C. S. and Manjunatha Badiger5.1 Introduction 815.2 The Role of IoT in Enhancing Emotional Intelligence in Machines 845.3 Human Emotions Detection through IoT 855.4 Wearable Systems 865.5 Problems You Have to Overcome in the Process of Emotional AI 865.6 Industrial Applications of Emotional AI and IoT 875.7 Underwater Optical Wireless Communication and Emotional AI 885.8 Neuroscience and Emotional AI: Understanding the Brain-Emotion Connection 905.9 The Skill to Process Emotions from Many Sources of Data 925.10 Facial Expression Analysis in AI Systems 945.11 Speech Emotion Identification 965.12 Healthcare Applications: Emotional AI for Mental Health and Well-Being 975.13 Smart Homes and Emotion-Aware IoT Environments 985.14 Conclusion 1016 Machine Learning and Predictive Analytics to Enhance Emotional Intelligence Using IoT in Industrial Systems 105Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan6.1 Introduction 1066.2 Literature Review 1106.3 Methodology 1186.4 Experimental Results 1236.5 Conclusion 1307 Smart Robotics with Emotional Intelligence: A Fusion of AI, IoT, and ML 135Dankan Gowda V., Supriya Devi, Sadashiva V. Chakrasali, Kottala Sri Yogi and Mandeep Singh7.1 Introduction 1367.2 Background and Motivation 1377.3 Literature Survey 1407.4 Machine Learning for Emotional Recognition 1447.5 Use Cases and Applications 1457.6 Results and Discussions 1467.7 Conclusion 1538 Data-Driven Emotional Intelligence: AI and IoT Synergy in Human–Machine Collaboration 157Dankan Gowda V., Sadashiva V. Chakrasali, Manoj Kumar S. B., Kottala Sri Yogi and Nidal Al Said8.1 Introduction 1588.2 Literature Survey 1618.3 Key Studies on Emotion Recognition via Facial Expressions, Speech Analysis, and Physiological Signals 1628.4 AI Models for Emotion Detection and Interaction 1638.5 IoT and EI 1648.6 Applications in Human–Machine Collaboration 1658.7 Results and Discussion 1668.8 Conclusion 1739 Integrating AI, IoT, and ML for Seamless Human-Centric Optimization 179Dankan Gowda V., Kavitha B. C., V. Nuthan Prasad, K.D.V. Prasad and Nidal Al Said9.1 Introduction 1809.2 Literature Survey 1829.3 Proposed Integration Framework 1869.4 Results and Discussion 1919.5 Conclusion 198Part III: Emotional Intelligence in Robotics and Cobots 20310 AI-Driven Emotional Intelligence in Next-Generation Robotics 205Babitha Hemanth, Khushi Rai and Harshith K.10.1 Emotion Recognition in Robotics 20610.2 Multimodal Emotion Detection Techniques 20610.3 AI Models for Emotion Recognition 211\10.4 Challenges and Ethical Considerations in Emotion AI and Robotics 21611 Emotionally Intelligent Systems: Human-Centered AI for Next-Gen Robotics 221Smitha Gayathri D., Roopashree C. S., Kumar P. and Santhosh Kumar R.11.1 Introduction 22211.2 Fundamentals of Emotional Intelligence in Machines 22311.3 EI in Robotics 22711.4 Emotion-Based Assistive System for Active HRI 22911.5 Multimodal Emotion Detection for System Personalization in HRI 23311.6 Recursive Emotion Analysis 23611.7 Experimental Results 24011.8 Conclusion 24212 Neurocomputational Models for Emotional Intelligence in Robotics: A Review 247Shravan Kumar, Shraddha P., Deeksha M. and Rathishchandra R. Gatti12.1 Introduction 24812.2 Foundations of EI in Robotics 25112.3 Neurocomputational Approaches to Emotional Intelligence in Robotics 25612.4 Applications and Case Studies 26312.5 Challenges and Open Issues 270Part IV: Algorithms and Models for Emotion Recognition 29113 Machine Learning Algorithms for Emotion Recognition in Advanced Hardware 293Swati Patil, Dankan Gowda V., K.D.V. Prasad, Ved Srinivas and Srinivas D.13.1 Introduction 29413.2 Literature Survey 29613.3 Machine Learning Algorithms for Emotion Recognition 30013.4 Results and Discussions 30513.5 Challenges and Limitations 31013.6 Recent Case Studies 31113.7 Conclusion 31214 A New Hybrid Deep Learning Framework for Emotion Recognition Based on ResNet50 and Contextual Features 317Tanuja Pande, Abhimanyu Dutonde and Anita Yadav14.1 Introduction 31814.2 Methodology 32114.3 Conclusion 33015 Multiclass Depression Detection Using Bidirectional Hybrid Deep Learning Model 333Nikhil E. Karale and Vijay S. Gulhane15.1 Introduction 33315.2 Literature Survey 33515.3 Dataset 33715.4 Methodology 33915.5 Result and Analysis 34115.6 Conclusion and Future Scope 34316 Feature-Evolved Deep Learning for Heart Disease Diagnosis: A Genetic Neural Network Model 345Shwetha N., Aravind Jadhav, Sangeetha N., Roopesh Ramesh, Rangaswamy Y. and Chandra Singh16.1 Introduction 34616.2 Scope of the Work 34716.3 Proposed Methodology 34916.4 Software Implementation Requirements 35516.5 Results and Discussion 35616.6 Conclusion and Future Scope 369Part V: Future Systems and Human-Machine Interfaces 37317 AI-Powered Human-Machine Feedback Systems for Adaptive Interfaces 375Dankan Gowda V., Kavitha B. C., V. Nuthan Prasad, K.D.V. Prasad and Srinivas D.17.1 Introduction 37617.2 Literature Survey 37917.3 Types of Feedback in Human-Machine Systems 38217.4 Challenges in Implementing AI for Adaptive Systems 38517.5 Results and Discussions 38617.6 Future Directions 39217.7 Conclusion 39318 Mental Well-Being of Adolescents: A Comparison of Day School and Boarding School 397Usha Desai, Susha M. and Raghavan K. P.18.1 Introduction 39818.2 Data Collection and Methodology 40018.3 Outcome and Discussion 40318.4 Conclusion 41019 Hippocampus Sclerosis Segmentation by Vanilla U-Net Model Prediction 413Jayanthi Vajiram, Sivakumar S., Nanditha H.G., Chennagiri Rajarao Padma and Usha Desai19.1 Introduction 41419.2 Related Survey 41519.3 Model Implementations 41619.4 Methodology 41719.5 Evaluation Metrics 41819.6 Results 41919.7 Conclusion 42320 Chernoff Bound and Bhattacharyya Bound Feature Ranking Approach for Epilepsy Detection 427Usha Desai, Roshan J. Martis, Dilna Udayan and Susha M.20.1 Introduction 42820.2 Materials and Methodology 43020.3 Results and Discussion 43820.4 Conclusion 44121 Emotional Intelligence Techniques in Humanoid Robotics 445Rathishchandra R. Gatti21.1 Introduction 44621.2 Conceptualizing Emotional Intelligence in Humanoid Robotics 44821.3 Emotion Recognition Techniques: Sensing Human Affect 44921.4 Emotion Synthesis and Expression Techniques: Giving Robots Affective Presence 45621.5 Emotion Modeling and Regulation: Toward Deeper Understanding and Adaptation 46121.6 Multimodal Approaches: Integrating Affective Channels 46321.7 Applications of Emotionally Intelligent Humanoid Robots 46521.8 Challenges and Limitations: Hurdles on the Path to EI 46721.9 Ethical Considerations: The Moral Landscape of Emotional AI 46821.10 Future Directions: Charting the Next Wave of Robotic EI 47021.11 Conclusion 47222 Enhancing Emotional Intelligence in Industrial Systems Using IoT 475Srividya P. and Siddharth A.22.1 Introduction to Emotions 47622.2 Overview on IoT and IIoT in Industrial Systems 47822.3 Emotional Intelligence in Industrial Systems 47922.4 Crucial Aspects of EI in Industrial Systems 48022.5 IoT-Based EI Framework 48122.6 Key Technologies Involved in Real-Time Emotion Detection in Industrial Settings 48322.7 EI and IoT for Enhancing Workplace Productivity and Safety 48522.8 Applications of EI in Industrial Systems 48522.9 Enhancement of Emotional Intelligence in Industrial Systems by IoT 48622.10 Challenges and Considerations 48822.11 Conclusion 48923 Integrating Affective Computing in Robotics: Progress and Challenges 491Spuran Rai, Harshal, Chandra Singh, Deeksha M. and Rathishchandra R. Gatti23.1 Introduction 49223.2 Background and Foundations 49323.3 Technologies Enabling Affective Robotics 49723.4 Applications of Affective Robotics 50023.5 Progress and Milestones in Affective Robotics 50323.6 Challenges in Integrating Affective Computing in Robotics 50523.7 Ethical and Societal Implications 50823.8 Conclusion 50824 Enhancing Career Counseling with the Big Five Personality Traits 511Minakshi Roy, Kalpana Sharma and Rohit Gupta24.1 Introduction 51224.2 Proposed Methodology: Following Steps Shows the Proposed Working Methodology 51324.3 Data Analysis 51524.4 Results and Discussion 51724.5 Conclusion 52225 Developing Emotional Intelligence of Cobots Using Multimodal LLMs 525Dhanyashree Acharya, Shraddha P., Shravan Kumar, Deeksha M., Rathishchandra R. Gatti and Chandra Singh25.1 Background 52525.2 Key Elements of Emotional Intelligence 53125.3 Multimodal Large Language Models (LLMs) for Emotional Intelligence 535References 539Index 541