Artificial Intelligence And Multimodal Signal Processing In Human Machine Interaction
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Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction
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. - 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
Neural Network Technologies and Brain-Computer Interfaces: Innovations and Applications
Author: Al Ansari, Mohammed Saleh
language: en
Publisher: IGI Global
Release Date: 2025-06-06
Novel neural network models and architectures inspired by the human brain advance learning and adaptability in AI systems. Innovations in neurorobotics empower robots to perceive, interact with, and navigate the environment autonomously through bio-inspired algorithms. As a result, brain-computer interfaces (BCI) technology can be applied to the development of advanced prosthetics, exoskeletons, and assistive devices that restore mobility and functionality. BCI-enabled neurofeedback can be utilized for cognitive training, neurorehabilitation, and treating neurological disorders. Advancements in neural interface technologies, including brain implants and neurostimulation techniques, are imperative for seamless integration with AI systems and robots. Neural Network Technologies and Brain-Computer Interfaces: Innovations and Applications explores the latest advancements and innovations in neural network technologies and brain-computer interfaces (BCIs), highlighting their potential to revolutionize various fields, including artificial intelligence, robotics, healthcare, and virtual reality. It discusses the potential of leveraging neural networks for processing and analyzing brain signals to enhance the accuracy and speed of BCI systems. Covering topics such as BCI prediction accuracy, healthcare access barriers, and neurofinance, this book is an excellent resource for engineers, healthcare practitioners, neuroscientists, computer scientists, researchers, academicians, and more.
Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET)
As industries worldwide adopt advanced technologies and sustainable practices, the role of technical and vocational education and training (TVET) is evolving to meet these new demands. TVET institutions must now integrate artificial intelligence (AI) and sustainability into their programs to produce a workforce equipped with future-ready skills. By incorporating AI tools and sustainable practices into TVET curricula, educators can provide learners with the competencies to thrive in green technologies, smart manufacturing, renewable energy, and other emerging fields. This integration empowers individuals with new skills and contributes to a more sustainable, resilient global economy. Further exploration may bridge the gap between technological advancement and environmental responsibility. Integrating AI and Sustainability in Technical and Vocational Education and Training (TVET) provides a comprehensive guide on how TVET can successfully incorporate technological elements, addressing the frameworks, strategies, best practices, and challenges associated with this transformation. It supports educators in navigating the complexities of integrating AI and sustainability into vocational training. This book covers topics such as cybersecurity, data science, and supply chains, and is a useful resource for business owners, engineers, educators, academicians, researchers, and data scientists.