Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction
- 1st Edition - September 18, 2024
- Imprint: Academic Press
- Editors: Abdulhamit Subasi, Saeed Mian Qaisar, Humaira Nisar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 5 0 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 1 5 1 - 7
Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modali… Read more
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Request a sales quoteArtificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.
Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field.
Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field.
- Covers advances in the multimodal signal processing and artificial intelligence assistive HMIs
- Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective HMI (Human Machine Interaction) system
- Presents different aspects of the multimodal signals, from the sensing to analysis using hardware/software, and making use of machine/ensemble/deep learning in the intended problem-solving
Researchers working across the biosciences in the fields of Bioinformatics, Computer Science, Biomedical and Computer Engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Series preface
- Preface
- Acknowledgments
- Chapter 1 Introduction to human-machine interaction
- Abstract
- 1.1 Introduction
- 1.2 HMI in the workplace
- 1.3 HMI in various fields
- 1.4 The human component of HMI
- 1.5 Conclusion
- References
- Chapter 2 Artificial intelligence techniques for human-machine interaction
- Abstract
- 2.1 Introduction
- 2.2 Artificial intelligence techniques for HMI
- 2.3 Conclusions and future directions
- References
- Chapter 3 Feature extraction techniques for human-computer interaction
- Abstract
- 3.1 Introduction
- 3.2 The spectral analysis
- 3.3 The time-frequency analysis
- 3.4 Common spatial patterns (CSP)
- 3.5 Conclusion
- References
- Chapter 4 An overview of techniques and best practices to create intuitive and user-friendly human-machine interfaces
- Abstract
- 4.1 Introduction to human-machine interaction
- 4.2 Technological foundations for intuitive interfaces
- 4.3 Multimodal signal processing and interface integration
- 4.4 Best practices in interface design
- 4.5 Future trajectory of intuitive interfaces
- 4.6 Ethical considerations in interface influence
- 4.7 HMI technologies in different industries
- 4.8 Conclusion
- References
- Chapter 5 An overview of electroencephalogram based human-computer interface
- Abstract
- 5.1 Introduction
- 5.2 Fundamentals of EEG
- 5.3 EEG, electrode placement systems, and configurations
- 5.4 EEG data acquisition
- 5.5 EEG-based HCI components
- 5.6 Innovations and challenges in EEG-based HCI
- 5.7 Future directions and emerging trends
- 5.8 Conclusion
- References
- Chapter 6 Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and Cymatics Display
- Abstract
- 6.1 Introduction
- 6.2 Materials and methods
- 6.3 System implementation and results
- 6.4 Conclusion
- References
- Chapter 7 EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers
- Abstract
- Acknowledgment
- 7.1 Introduction
- 7.2 Literature review
- 7.3 Materials and methods
- 7.4 Results and discussion
- 7.5 Conclusion
- References
- Chapter 8 Understanding dyslexia and the potential of artificial intelligence in detecting neurocognitive impairment in dyslexia
- Abstract
- 8.1 Introduction to dyslexia and artificial intelligence
- 8.2 A brief introduction: Learning process of normal children vs dyslexic children
- 8.3 Electroencephalography
- 8.4 ERP in dyslexia
- 8.5 The future of dyslexia: AI assistance in learning for dyslexia
- 8.6 The future of dyslexia: ML for early diagnosis
- 8.7 Conclusion
- References
- Chapter 9 Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG images
- Abstract
- 9.1 Introduction
- 9.2 Related work
- 9.3 Materials and methods
- 9.4 Results and discussion
- 9.5 Conclusions
- References
- Chapter 10 EEG-based stress identification using oscillatory mode decomposition and artificial neural network
- Abstract
- Acknowledgment
- 10.1 Introduction
- 10.2 Materials and adopted methodology
- 10.3 Results and discussion
- 10.4 Conclusion
- References
- Chapter 11 EEG signal processing with deep learning for alcoholism detection
- Abstract
- 11.1 Introduction
- 11.2 Materials and methods in EEG signal processing
- 11.3 Deep neural networks for EEG analysis
- 11.4 Discussion
- 11.5 Conclusions and future of deep learning in alcoholism detection
- References
- Chapter 12 ECG-based emotion recognition using CWT and deep learning
- Abstract
- 12.1 Introduction
- 12.2 Literature review
- 12.3 Artificial intelligence-based emotion recognition
- 12.4 Results and discussion
- 12.5 Conclusion
- References
- Chapter 13 EOG-based human-machine interaction using artificial intelligence
- Abstract
- 13.1 Introduction
- 13.2 EOG signal acquisition
- 13.3 Feature processing
- 13.4 EOG signal processing
- 13.5 EOG-HMI applications
- 13.6 Discussion
- 13.7 Conclusion
- References
- Chapter 14 Surface EMG-based gesture recognition using wavelet transform and ensemble learning
- Abstract
- 14.1 Introduction
- 14.2 Materials and methods
- 14.3 Results and discussion
- 14.4 Multimodal signal processing
- 14.5 Conclusion
- References
- Chapter 15 EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methods
- Abstract
- 15.1 Introduction
- 15.2 Literature review
- 15.3 Materials and methods
- 15.4 Results and discussion
- 15.5 Discussion
- 15.6 Conclusion
- References
- Chapter 16 EEG-based emotion recognition using AR burg and ensemble machine learning models
- Abstract
- 16.1 Introduction
- 16.2 Literature review
- 16.3 Materials and methods
- 16.4 Results and discussion
- 16.5 Discussion
- 16.6 Conclusion
- References
- Chapter 17 Immersive virtual reality and augmented reality in human-machine interaction
- Abstract
- 17.1 Introduction to AR and VR in human-machine interaction
- 17.2 Historical development of AR and VR
- 17.3 Technologies enabling AR and VR
- 17.4 Human perception, AR, and VR
- 17.5 Field of applications
- 17.6 Challenges and future prospects
- 17.7 Conclusion
- References
- Chapter 18 Mental workload levels of multiple sclerosis patients in the virtual reality environment
- Abstract
- 18.1 Introduction
- 18.2 Multiple sclerosis
- 18.3 Electroencephalogram
- 18.4 Mental workload
- 18.5 The role of virtual reality technology in mental workload
- 18.6 Research methodology
- 18.7 Results
- 18.8 Conclusion and future studies
- References
- Chapter 19 Vision-based action recognition for the human-machine interaction
- Abstract
- 19.1 Introduction
- 19.2 Fundamentals of vision-based action recognition
- 19.3 Human-machine interaction
- 19.4 Role of vision in HMI
- 19.5 Action recognition techniques
- 19.6 Applications of vision-based action recognition
- 19.7 Challenges and future directions
- 19.8 Conclusion
- References
- Chapter 20 Security and privacy in human-machine interaction for healthcare
- Abstract
- 20.1 Introduction
- 20.2 Authentication methods in human-machine interaction (HMI)
- 20.3 Security measures for human-machine interaction
- 20.4 Applications in the healthcare sector
- 20.5 Benefits and challenges of human-machine interaction (HMI) in healthcare
- 20.6 Importance of security and privacy in healthcare
- 20.7 Data collection and consent
- 20.8 Threats in human-machine interaction for healthcare
- 20.9 Best practices for security and privacy in healthcare
- 20.10 Industry standards and guidelines
- 20.11 Training and awareness programs
- 20.12 Advancements in security technologies
- 20.13 Conclusion
- References
- Index
- Edition: 1
- Published: September 18, 2024
- Imprint: Academic Press
- No. of pages: 424
- Language: English
- Paperback ISBN: 9780443291500
- eBook ISBN: 9780443291517
AS
Abdulhamit Subasi
Abdulhamit Subasi is a highly specialized expert in the fields of Artificial Intelligence, Machine Learning, and Biomedical Signal and Image Processing. His extensive expertise in applying machine learning across diverse domains is evident in his numerous contributions, including the authorship of multiple book chapters, as well as the publication of a substantial body of research in esteemed journals and conferences. His career has spanned various prestigious institutions, including the Georgia Institute of Technology in Georgia, USA, where he served as a dedicated researcher. In recognition of his outstanding research contributions, Subasi received the prestigious Queen Effat Award for Excellence in Research in May 2018. His academic journey includes a tenure as a Professor of computer science at Effat University in Jeddah, Saudi Arabia, from 2015 to 2020. Since 2020, he has assumed the role of Professor of medical physics at the Faculty of Medicine, University of Turku in Turku, Finland
Affiliations and expertise
Institute of Biomedicine, Faculty of Medicine, University of Turku, Turku, Finland Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi ArabiaSQ
Saeed Mian Qaisar
Dr. Qaisar currently holds the position of Research & Innovation Department Head for the South-East Region at CESI LINEACT, located in France. In recognition of his teaching and learning excellence, he was honored with the Queen Effat Award in May 2016. Dr. Qaisar's accomplishments encompass two granted patents, as well as an extensive portfolio of published works spanning journal articles, book chapters, and conference papers. Furthermore, Dr. Qaisar contributes to the academic community as an editor for various international journals and is actively involved in the technical and review committees of several international journals and conferences. His current areas of research focus include signal processing, circuits and systems, artificial intelligence, event-driven systems, biomedical and bioinformatics applications, smart grid technology, energy storage, and sampling theory.
Affiliations and expertise
CESI LINEACT, Lyon, France College of Engineering, Effat University, Jeddah, Saudi ArabiaHN
Humaira Nisar
Humaira Nisar has a B.S (Honours) in Electrical Engineering from the University of Engineering and Technology, Lahore, Pakistan, M.S in Nuclear Engineering from Quaid-i-Azam University, Islamabad, Pakistan, another M.S in Mechatronics, and Ph.D. in Information and Mechatronics from Gwangju Institute of Science and Technology, Gwangju, South Korea. She has more than twenty years of research experience. Currently, she is working as a Full Professor in the Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar, Malaysia. Her research interests include signal and image processing, biomedical imaging, neuro-signal processing and analysis, Brain-Computer Interface, and Neurofeedback. She has published hundreds international journal and conference papers. She is a senior member of IEEE
Affiliations and expertise
Universiti Tunku, MalaysiaRead Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction on ScienceDirect