AI-Driven Human-Machine Interaction for Biomedical Engineering
- 1st Edition - May 1, 2026
- Latest edition
- Editors: Kapil Gupta, Varun Bajaj, Deepak Kumar Jain, Raul Villamarin Rodriguez, Hemachandran Kannan
- Language: English
"AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies" offers a comprehensive examination of the intricate relations… Read more
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"AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies" offers a comprehensive examination of the intricate relationship between humans and machines, particularly through the transformative lens of artificial intelligence (AI). As AI technologies rapidly evolve, understanding their implications for human-machine interaction (HMI) has become essential across various domains, especially healthcare. This book addresses the pressing need for insights into AI-driven methodologies, providing scholars, practitioners, and learners with foundational knowledge and practical applications that enhance collaboration between human cognition and machine capabilities. Structured into well-defined chapters, the book begins with an introduction to AI-driven HMI, laying the groundwork for understanding its significance in sustainable healthcare and beyond. Subsequent chapters explore critical topics such as machine learning principles, advanced biomedical data classification methods, and the role of AI in telemedicine. Readers will delve into cutting-edge techniques, from deep learning to non-invasive computer vision, and examine the implications of these technologies across industries. Each chapter equips readers with actionable insights and highlights emerging trends, ethical considerations, and the future of AI in HMI, ensuring a well-rounded perspective on this dynamic field. "AI-Driven Human-Machine Interaction for Biomedical Engineering: Concepts, Applications, and Methodologies" is an invaluable resource for researchers, academics, and students in the fields of Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, and Healthcare Technology. By bridging theoretical foundations with practical applications, this book empowers its readers to effectively harness AI technologies, driving innovation and improving outcomes in healthcare and various sectors
• Offers practical insights into AI-driven methodologies for enhanced human-machine collaboration in healthcare and beyond
• Provides foundational knowledge of machine learning principles applicable across diverse industries
• Equips readers with cutting-edge techniques for biomedical data classification and analysis
• Addresses ethical considerations and emerging trends in AI applications for informed decision-making
• Facilitates innovation by bridging theoretical concepts with real-world applications in human-machine interaction
• Provides foundational knowledge of machine learning principles applicable across diverse industries
• Equips readers with cutting-edge techniques for biomedical data classification and analysis
• Addresses ethical considerations and emerging trends in AI applications for informed decision-making
• Facilitates innovation by bridging theoretical concepts with real-world applications in human-machine interaction
Researchers, academics, and students in the fields of Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, and Healthcare Technology.
1. Introduction to AI-driven Human-Machine Interaction
1.1 Overview of AI in Human-Machine Interaction (HMI)
1.2 Evolution of AI-driven HMI Methodologies
1.3 Overview of Biomedical data
1.4 Role of AI in Sustainable Healthcare 4.0
1.5 Challenges of medical professionals in interpreting the data
1.6 Overview of machine learning, algorithms, design process, and applications and benefits
1.7 Current insights
2. Basics, Constraints, and Future Potential of Machine Learning in HMI
2.1 Introduction to computer-assisted data analysis
2.2 Principles of Human-Machine Interaction
2.3 Integration of AI in HMI: Concepts and Challenges
2.4 Review of current research
2.5 Design and optimization considerations
2.6 The limitations of ML techniques
2.7 Artificial neural networks
2.8 Support vector machines and their biomedical applications
2.9 Challenges such as data security, patient confidentiality
2.10 How to test the effectiveness, suitability, and reliability of machine learning systems?
2.11 How to implement machine learning within organizations?
3. Cutting-edge Methods for Biomedical Data Classification based on Machine Learning
3.1 Introduction
3.2 Data Pre-processing via Feature Selection
3.3 Features Involved in Classification
3.4 Steps for Classification Model Building
3.5 Methods for the Feature Selection Process
3.6 Machine Learning Approaches for Classification of Data
3.7 Important Considerations for Implementing Deep Learning Models for Biomedical Data
3.8 Methodology for Deep Learning in the Context of Computational Biology
3.9 Tools and Pipelines Implementing Machine Learning
4. AI in Telemedicine
4.1 Introduction to telemedicine and its benefits
4.2 The role of AI in telemedicine
4.3 Applications of AI in Telemedicine
4.4 Challenges and future directions of AI-enabled telemedicine
4.5 Future directions
4.6 Conclusion
5. Applications Across Industries
5.1 Healthcare: AI in Patient Interaction and Diagnosis
5.2 Industry 4.0: Smart Manufacturing and Robotics
5.3 Education: Personalized Learning Environments
5.4 Entertainment: Gaming and Virtual Reality
5.5 Future directions
6. Two-stage Verifications for Multi-Instance Feature Selection: A Machine Learning-Based Approach
6.1 Overview of medical image types
6.2 Materials and methods
6.3 Data collection
6.4 Multi-stage feature selection method
6.5 Early breast cancer detection (EBCD) framework
6.6 Current insights
6.7 Future directions
7. A practical EMG-based Intelligent human-computer interface
7.1 Introduction to EMG-Based Interfaces
7.2 Fundamentals of EMG Technology
7.3 System Design and Architecture
7.4 Signal Processing and Feature Extraction
7.5 Machine Learning for EMG Interpretation
7.6 Applications of EMG-Based Interfaces
7.7 Challenges and Solutions
8. Computer Vision for Human-Computer Interaction Using Non-invasive Technology
8.1 Introduction to Computer Vision in HCI
8.2 Basic Principles of Computer Vision
8.3 Importance of Non-invasive Technologies
8.4 Historical Context and Evolution
8.5 Applications of Computer Vision in HCI
8.6 Challenges and Future Directions
9. Human-computer interaction principles for cardiac feedback
9.1 Introduction to Cardiac Feedback and Human-Computer Interaction (HCI)
9.2 Data Acquisition and Processing for Cardiac Feedback
9.3 Visualization and Interaction Design for Cardiac Feedback
9.4 User Experience Considerations for Cardiac Feedback
9.5 Applications of Human-Computer Interaction with Cardiac Feedback
9.6 Ethical Considerations and Privacy Concerns
9.7 Future Directions and Emerging Technologies
10. The Future of AI-Driven HMI
10.1 Introduction: The Rise of AI and its Impact on Human-Machine Interaction (HMI)
10.2 Core Technologies Shaping AI-Driven HMI
10.3 Envisioning Future HMI Applications with AI
10.4 Challenges and Considerations for AI-Driven HMI
10.5 Human-Cantered Design Principles for AI-powered HMI
10.6 Ethical considerations and potential challenges to navigate in the future
11. Biomechanics computation for medical image interpretation
11.1 Overview
11.2 Patient-specific computational biomechanics model derived from medical images
11.3 Segmentation and geometry extraction from medical images
11.4 Creation of finite element meshes
11.5 Image as a meshless discretization model for computational biomechanics
11.6 Image analysis is informed by biomechanics: a computational biomechanics model serves as a tool for image registration
11.7 Formulating problems for biomechanics-based image registration
11.8 Discussion
12. Large-scale demographic imaging biomarkers based on machine learning
12.1 Dimensionality reduction of neuroimaging data using unsupervised pattern learning Methods
12.2 Supervised imaging biomarkers based on classification for the diagnosis of illness
12.3 Predicting brain age via multivariate pattern regression
12.4 Deep learning for analysis of neuroimaging
13. Support vector machine in the processing of medical images
13.1 Introduction
13.2 Feature selection and ensembling
13.3 Detection and localization
13.4 Image-based prediction
13.5 Feature interpretation
14. Computer-aided interventional therapy
14.1 Information flow in interventional medicine with computer integration
14.2 Intraoperative systems for HMI
14.3 Intraoperative imaging methods
14.4 Discussion
15. HMI in healthcare imaging and medical treatments
15.1 HMI for diagnosis queries by employing medical imaging methods
15.2 HMI to assist in organizing, directing, and carrying out necessary actions (computerized operations)
15.3 HMI: design and evaluation
15.4 Machine inputs and human outputs
15.5 Movement and selection events in image-based and workspace-based interactions
1.1 Overview of AI in Human-Machine Interaction (HMI)
1.2 Evolution of AI-driven HMI Methodologies
1.3 Overview of Biomedical data
1.4 Role of AI in Sustainable Healthcare 4.0
1.5 Challenges of medical professionals in interpreting the data
1.6 Overview of machine learning, algorithms, design process, and applications and benefits
1.7 Current insights
2. Basics, Constraints, and Future Potential of Machine Learning in HMI
2.1 Introduction to computer-assisted data analysis
2.2 Principles of Human-Machine Interaction
2.3 Integration of AI in HMI: Concepts and Challenges
2.4 Review of current research
2.5 Design and optimization considerations
2.6 The limitations of ML techniques
2.7 Artificial neural networks
2.8 Support vector machines and their biomedical applications
2.9 Challenges such as data security, patient confidentiality
2.10 How to test the effectiveness, suitability, and reliability of machine learning systems?
2.11 How to implement machine learning within organizations?
3. Cutting-edge Methods for Biomedical Data Classification based on Machine Learning
3.1 Introduction
3.2 Data Pre-processing via Feature Selection
3.3 Features Involved in Classification
3.4 Steps for Classification Model Building
3.5 Methods for the Feature Selection Process
3.6 Machine Learning Approaches for Classification of Data
3.7 Important Considerations for Implementing Deep Learning Models for Biomedical Data
3.8 Methodology for Deep Learning in the Context of Computational Biology
3.9 Tools and Pipelines Implementing Machine Learning
4. AI in Telemedicine
4.1 Introduction to telemedicine and its benefits
4.2 The role of AI in telemedicine
4.3 Applications of AI in Telemedicine
4.4 Challenges and future directions of AI-enabled telemedicine
4.5 Future directions
4.6 Conclusion
5. Applications Across Industries
5.1 Healthcare: AI in Patient Interaction and Diagnosis
5.2 Industry 4.0: Smart Manufacturing and Robotics
5.3 Education: Personalized Learning Environments
5.4 Entertainment: Gaming and Virtual Reality
5.5 Future directions
6. Two-stage Verifications for Multi-Instance Feature Selection: A Machine Learning-Based Approach
6.1 Overview of medical image types
6.2 Materials and methods
6.3 Data collection
6.4 Multi-stage feature selection method
6.5 Early breast cancer detection (EBCD) framework
6.6 Current insights
6.7 Future directions
7. A practical EMG-based Intelligent human-computer interface
7.1 Introduction to EMG-Based Interfaces
7.2 Fundamentals of EMG Technology
7.3 System Design and Architecture
7.4 Signal Processing and Feature Extraction
7.5 Machine Learning for EMG Interpretation
7.6 Applications of EMG-Based Interfaces
7.7 Challenges and Solutions
8. Computer Vision for Human-Computer Interaction Using Non-invasive Technology
8.1 Introduction to Computer Vision in HCI
8.2 Basic Principles of Computer Vision
8.3 Importance of Non-invasive Technologies
8.4 Historical Context and Evolution
8.5 Applications of Computer Vision in HCI
8.6 Challenges and Future Directions
9. Human-computer interaction principles for cardiac feedback
9.1 Introduction to Cardiac Feedback and Human-Computer Interaction (HCI)
9.2 Data Acquisition and Processing for Cardiac Feedback
9.3 Visualization and Interaction Design for Cardiac Feedback
9.4 User Experience Considerations for Cardiac Feedback
9.5 Applications of Human-Computer Interaction with Cardiac Feedback
9.6 Ethical Considerations and Privacy Concerns
9.7 Future Directions and Emerging Technologies
10. The Future of AI-Driven HMI
10.1 Introduction: The Rise of AI and its Impact on Human-Machine Interaction (HMI)
10.2 Core Technologies Shaping AI-Driven HMI
10.3 Envisioning Future HMI Applications with AI
10.4 Challenges and Considerations for AI-Driven HMI
10.5 Human-Cantered Design Principles for AI-powered HMI
10.6 Ethical considerations and potential challenges to navigate in the future
11. Biomechanics computation for medical image interpretation
11.1 Overview
11.2 Patient-specific computational biomechanics model derived from medical images
11.3 Segmentation and geometry extraction from medical images
11.4 Creation of finite element meshes
11.5 Image as a meshless discretization model for computational biomechanics
11.6 Image analysis is informed by biomechanics: a computational biomechanics model serves as a tool for image registration
11.7 Formulating problems for biomechanics-based image registration
11.8 Discussion
12. Large-scale demographic imaging biomarkers based on machine learning
12.1 Dimensionality reduction of neuroimaging data using unsupervised pattern learning Methods
12.2 Supervised imaging biomarkers based on classification for the diagnosis of illness
12.3 Predicting brain age via multivariate pattern regression
12.4 Deep learning for analysis of neuroimaging
13. Support vector machine in the processing of medical images
13.1 Introduction
13.2 Feature selection and ensembling
13.3 Detection and localization
13.4 Image-based prediction
13.5 Feature interpretation
14. Computer-aided interventional therapy
14.1 Information flow in interventional medicine with computer integration
14.2 Intraoperative systems for HMI
14.3 Intraoperative imaging methods
14.4 Discussion
15. HMI in healthcare imaging and medical treatments
15.1 HMI for diagnosis queries by employing medical imaging methods
15.2 HMI to assist in organizing, directing, and carrying out necessary actions (computerized operations)
15.3 HMI: design and evaluation
15.4 Machine inputs and human outputs
15.5 Movement and selection events in image-based and workspace-based interactions
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
KG
Kapil Gupta
Dr. Kapil Gupta earned his Ph.D. from the Indian Institute of Information Technology, Design and Manufacturing (IIITDM), Jabalpur, India. He served as an Assistant Professor in Electronics and Communication Engineering at Oriental College of Technology, Bhopal, from 2013 to 2020. He holds a B.E. with Honors in Electronics and Communication Engineering and an M.Tech. in Nano Technology. His research interests encompass signal processing in biomedical applications, time-frequency analysis, artificial intelligence, and cardiovascular systems. Dr. Gupta has published extensively in reputed journals and serves as a reviewer for IEEE and Elsevier. He has organized numerous national and international conferences and has been involved in various technical committees.
Affiliations and expertise
Assistant Professor (Senior Scale), School of Computer Science, University of Petroleum and Energy Studies, Dehradun, IndiaVB
Varun Bajaj
Dr. Varun Bajaj is an Associate Professor in Electronics and Communication Engineering at Maulana Azad National Institute of Technology Bhopal, India, starting January 2024. Previously, he served at the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur from 2014 to 2024, initially as an Assistant Professor and later as an Associate Professor. He earned his Ph.D. in Electrical Engineering from IIT Indore in 2014, following an M.Tech. in Microelectronics and VLSI Design in 2009, and a B.E. in Electronics and Communication Engineering in 2006. Dr. Bajaj holds various editorial roles, including Associate Editor for the IEEE Sensor Journal and Subject Editor-in-Chief for IET Electronics Letters. A Senior Member of IEEE since 2020, he actively reviews for numerous journals and has delivered over 50 expert talks. He has received multiple awards for his research and has been recognized among the top 2% of researchers globally by Stanford University from 2020 to 2023.
Affiliations and expertise
Assistant Professor, Indian Institute of Technology, Design, and Manufacturing, Bhopal, IndiaDJ
Deepak Kumar Jain
Dr. Deepak Kumar Jain is an Associate Professor at the School of Control Science and Engineering at Dalian University of Technology, China, since July 2023. Previously, he was an Assistant Professor at Chongqing University of Posts and Telecommunications. He earned his Bachelor of Engineering in 2010 and a Master of Technology in 2012 from Indian institutions, followed by a Ph.D. at the University of Chinese Academy of Sciences in 2018, supported by the CAS-TWAS Presidential Fellowship. Recognized as a "Foreign Expert" by the Shandong Taian Administration, Dr. Jain has also served as an Adjunct Professor at Symbiosis International University in India. He has approximately fifty publications with a citation count of 3000 and has contributed to numerous peer-reviewed journals. A senior member of IEEE, he is involved in several editorial roles and conferences, including delivering keynote talks internationally. His research focuses on deep learning, machine learning, pattern recognition, and computer vision.
Affiliations and expertise
Associate Professor, School of Control Science and Engineering, Dalian University of Technology, ChinaRR
Raul Villamarin Rodriguez
Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University, where he holds the Steven Pinker Professorship in Cognitive Psychology and the Classavo Chair in Integrative Research and Digital Learning. He is also an Adjunct Professor at Universidad del Externado in Colombia and serves on the International Advisory Boards of IBS Ranepa in Russia and the University of Pécs Faculty of Business and Economics. Dr. Rodriguez represents India on the PRME i5 Expert Pedagogy Group and holds a Ph.D. in Artificial Intelligence and Robotics Process Automation in Human Resources. His expertise includes machine learning, deep learning, natural language processing, and quantum artificial intelligence. He is a registered expert in these fields with the European Commission and was nominated for the Forbes 30 Under 30 Europe 2020 list. Dr. Rodriguez has co-authored two reference books, published over 70 research papers, and is a regular contributor to various magazines on analytics and emerging technologies. He also serves as a journal reviewer and associate editor for several publications, including IEEE.
Affiliations and expertise
Professor and Vice President, Woxsen University, IndiaHK
Hemachandran Kannan
Dr. Hemachandran Kannan is a Professor in the Department of Artificial Intelligence & Business Analytics at Woxsen University, India, where he holds the Zita Zoltay Paprika Chair in Decision Sciences and Business Economics, as well as the Course5i Chair in Business Analytics and Machine Learning. With 15 years of teaching and 5 years of research experience, he is a dedicated educator skilled in AI and Business Analytics. After earning a Ph.D. in Embedded Systems, Dr. Kannan shifted his focus to interdisciplinary research. He has served as a resource person at numerous national and international conferences and has lectured on AI and Business Analytics topics. Recognized as Best Faculty at Woxsen University (2021-2022) and Ashoka Institute of Engineering & Technology (2019-2020), he has expertise in Natural Language Processing, Computer Vision, and autonomous systems.
Affiliations and expertise
Department of Artificial Intelligence & Business Analytics, Woxsen University, India