Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction

1st Edition - September 18, 2024
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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Artificial 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.

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

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 Arabia

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 Arabia

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, Malaysia

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Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction

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Abdulhamit Subasi

Saeed Mian Qaisar

Humaira Nisar