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. 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, while also examining 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. This is an invaluable resource for researchers, academics, and students in the fields of Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, and Healthcare Technology.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Chapter 1 Basics, constraints, and future potential of machine learning in human–machine interaction - p. 1 8. Chapter 2 Human-computer interaction for cardiac feedback: A physics-guided deep learning approach - p. 15 9. Chapter 3 Classification of steady state visual evoked potentials in brain-computer interface systems using Python - p. 35 10. Chapter4 Contactless vital sign monitoring through intelligent human–machine interaction: A systematic review of image- and video-based techniques - p. 59 11. Chapter 5 Artificial intelligence-driven scalable software architecture for enhancing human–machine interaction in biomedical data ecosystems - p. 117 12. Chapter 6 AI-driven depression detection using EEG signal determinants and MTO - p. 135 13. Chapter 7 Electroencephalography-driven machine learning for recognition of emergency numbers for crisis readiness: A translational study - p. 149 14. Chapter 8 Suppression of artifacts from electroencephalogram signals using quadratic relative error based least mean squares algorithm - p. 173 15. Chapter 9 Two-stage verification for multiinstance feature selection in dental anomaly detection: A convolutional neural network–gated recurrent unit approach with explainable artificial intelligence and optimization techniques - p. 203 16. Chapter 10 Performance evaluation of vision transformers for AI-driven diagnosis of bleeding detection in wireless endoscopy bleeding - p. 227 17. Chapter 11 Human machine interface in healthcare imaging and medical treatments - p. 243 18. Chapter12 State-of-the-art methods for diagnosis of acute leukemia: A survey - p. 263 19. Chapter 13 Optimized feature subsets for characterizing fetal QRS complexes in noninvasive fetal electrocardiography recordings - p. 281 20. Chapter 14 Multimodal artificial intelligence in human-computer interaction: Transforming medical feedback delivery - p. 301 21. Chapter 15 Computer vision for AI-driven human–machine interaction and enhanced diagnosis - p. 325 22. Chapter 16 Human–machine interaction in AI-assisted medical diagnosis: Challenges and future directions - p. 347 23. Index - p. 361
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. 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, while also examining 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. This is an invaluable resource for researchers, academics, and students in the fields of Biomedical Engineering, Computer Science, Data Science, Artificial Intelligence, and Healthcare Technology.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Chapter 1 Basics, constraints, and future potential of machine learning in human–machine interaction - p. 1 8. Chapter 2 Human-computer interaction for cardiac feedback: A physics-guided deep learning approach - p. 15 9. Chapter 3 Classification of steady state visual evoked potentials in brain-computer interface systems using Python - p. 35 10. Chapter4 Contactless vital sign monitoring through intelligent human–machine interaction: A systematic review of image- and video-based techniques - p. 59 11. Chapter 5 Artificial intelligence-driven scalable software architecture for enhancing human–machine interaction in biomedical data ecosystems - p. 117 12. Chapter 6 AI-driven depression detection using EEG signal determinants and MTO - p. 135 13. Chapter 7 Electroencephalography-driven machine learning for recognition of emergency numbers for crisis readiness: A translational study - p. 149 14. Chapter 8 Suppression of artifacts from electroencephalogram signals using quadratic relative error based least mean squares algorithm - p. 173 15. Chapter 9 Two-stage verification for multiinstance feature selection in dental anomaly detection: A convolutional neural network–gated recurrent unit approach with explainable artificial intelligence and optimization techniques - p. 203 16. Chapter 10 Performance evaluation of vision transformers for AI-driven diagnosis of bleeding detection in wireless endoscopy bleeding - p. 227 17. Chapter 11 Human machine interface in healthcare imaging and medical treatments - p. 243 18. Chapter12 State-of-the-art methods for diagnosis of acute leukemia: A survey - p. 263 19. Chapter 13 Optimized feature subsets for characterizing fetal QRS complexes in noninvasive fetal electrocardiography recordings - p. 281 20. Chapter 14 Multimodal artificial intelligence in human-computer interaction: Transforming medical feedback delivery - p. 301 21. Chapter 15 Computer vision for AI-driven human–machine interaction and enhanced diagnosis - p. 325 22. Chapter 16 Human–machine interaction in AI-assisted medical diagnosis: Challenges and future directions - p. 347 23. Index - p. 361
