Neural Rehab
Deep Learning in Healthcare Revitalization
1st Edition
Published on 30. July 2025
Book
Hardback
204 pages
978-1-032-83998-1 (ISBN)
Description
In recent years, the integration of cutting-edge technology with healthcare has led to groundbreaking advancements. Deep learning, in particular, has emerged as a revolutionary force, poised to transform the field of neural rehabilitation. This book sets the stage for the exploration of deep learning's significant impact on the revitalization of healthcare through neural rehabilitation.
Neural Rehab: Deep Learning in Healthcare Revitalization offers a detailed exploration of the intersection between deep learning and neural rehabilitation, shedding light on a critical area in healthcare. Through real-life examples and case studies, readers will gain a practical understanding of how deep learning is utilized in everyday healthcare settings to improve patient outcomes. The focus is on how deep learning algorithms are being used to personalize rehabilitation plans, demonstrating how technology can tailor interventions to meet the unique needs and progress of each individual. The book covers various aspects of neural rehabilitation, such as stroke recovery, brain injuries, and neurological disorders, providing readers with a comprehensive understanding of the subject matter. A key emphasis is placed on the patient-centric approach, showcasing how deep learning contributes to enhanced patient experiences, improved recovery, and an overall better quality of life.
This book is essential reading for healthcare professionals, researchers, and anyone interested in the intersection of deep learning and neural rehabilitation.
Neural Rehab: Deep Learning in Healthcare Revitalization offers a detailed exploration of the intersection between deep learning and neural rehabilitation, shedding light on a critical area in healthcare. Through real-life examples and case studies, readers will gain a practical understanding of how deep learning is utilized in everyday healthcare settings to improve patient outcomes. The focus is on how deep learning algorithms are being used to personalize rehabilitation plans, demonstrating how technology can tailor interventions to meet the unique needs and progress of each individual. The book covers various aspects of neural rehabilitation, such as stroke recovery, brain injuries, and neurological disorders, providing readers with a comprehensive understanding of the subject matter. A key emphasis is placed on the patient-centric approach, showcasing how deep learning contributes to enhanced patient experiences, improved recovery, and an overall better quality of life.
This book is essential reading for healthcare professionals, researchers, and anyone interested in the intersection of deep learning and neural rehabilitation.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional Reference
Illustrations
47 s/w Abbildungen, 47 s/w Zeichnungen
47 Line drawings, black and white; 47 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 17 mm
Weight
499 gr
ISBN-13
978-1-032-83998-1 (9781032839981)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
07/2025
CRC Press
€73.99
Available for download
E-Book
07/2025
CRC Press
€73.99
Available for download
Persons
Sima Das is an Assistant Professor at Bengal College of Engineering and Technology, Durgapur, India, previously served at Camellia Institute of Technology and Management, Hooghly. She earned her M. Tech in Computer Science and Engineering in 2020 from Maulana Abul Kalam Azad University of Technology. Currently pursuing her Ph.D. at NIT Rourkela, her expertise spans Artificial Intelligence, Machine Learning, Deep Learning, IoT, Cybersecurity, Smart Healthcare, and Agriculture. Recognized with the 2021 Research Excellence Award, Sima is an Associate Member of the Institute of Engineers and a Professional Member of IEEE. She is a prolific author, contributing to books, chapters, papers, patents, and journals while actively engaging as an Editor and Reviewer for international publications.
Parijat Bhowmick is an Assistant Professor, who specializes in Robust Control, Negative-Imaginary Systems, and Cooperative Control of Multi-agent Systems. He holds a Ph.D. from IIT Kharagpur and conducted post-doctoral research at the University of Manchester, UK. His research contributions include adaptive control schemes for vehicle platoons, distributed formation tracking, and secondary control for microgrids. Dr. Bhowmick has authored numerous papers in prestigious journals, notably IEEE Transactions, and received recognition for his work, including the Research Excellence Award. His expertise extends to Vibration Control, Smart Grids, and applications in Robotics. He actively contributes to advancing control theory and its practical applications.
Farshad Arvin is an Associate Professor at Durham University, UK, earning his BSc in Computer Engineering in 2004, an MSc in Computer Systems Engineering in 2010, and a PhD in Computer Science in 2015. Formerly a Lecturer and Senior Lecturer in Robotics at the University of Manchester, he joined Durham in 2022. His extensive research spans Swarm Robotics, Autonomous Multi-agent Systems, and Bio-hybrid Systems. Farshad founded the Swarm and Computation Intelligence Laboratory in 2018, overseeing 3 Post-Doctoral Research Associates and 8 PGR students.
Arpan Adhikary, an Assistant Professor at Haldia Institute of Technology, previously served at Bengal College of Engineering and Technology, Durgapur, India. He earned his M. Tech in Computer Science and Engineering in 2022 from Maulana Abul Kalam Azad University of Technology. His expertise spans Artificial Intelligence, Machine Learning, Deep Learning, IoT, Cybersecurity, and Smart Healthcare. He is a prolific author, contributing to books, chapters, and papers while actively engaging as a Reviewer for international publications.
Parijat Bhowmick is an Assistant Professor, who specializes in Robust Control, Negative-Imaginary Systems, and Cooperative Control of Multi-agent Systems. He holds a Ph.D. from IIT Kharagpur and conducted post-doctoral research at the University of Manchester, UK. His research contributions include adaptive control schemes for vehicle platoons, distributed formation tracking, and secondary control for microgrids. Dr. Bhowmick has authored numerous papers in prestigious journals, notably IEEE Transactions, and received recognition for his work, including the Research Excellence Award. His expertise extends to Vibration Control, Smart Grids, and applications in Robotics. He actively contributes to advancing control theory and its practical applications.
Farshad Arvin is an Associate Professor at Durham University, UK, earning his BSc in Computer Engineering in 2004, an MSc in Computer Systems Engineering in 2010, and a PhD in Computer Science in 2015. Formerly a Lecturer and Senior Lecturer in Robotics at the University of Manchester, he joined Durham in 2022. His extensive research spans Swarm Robotics, Autonomous Multi-agent Systems, and Bio-hybrid Systems. Farshad founded the Swarm and Computation Intelligence Laboratory in 2018, overseeing 3 Post-Doctoral Research Associates and 8 PGR students.
Arpan Adhikary, an Assistant Professor at Haldia Institute of Technology, previously served at Bengal College of Engineering and Technology, Durgapur, India. He earned his M. Tech in Computer Science and Engineering in 2022 from Maulana Abul Kalam Azad University of Technology. His expertise spans Artificial Intelligence, Machine Learning, Deep Learning, IoT, Cybersecurity, and Smart Healthcare. He is a prolific author, contributing to books, chapters, and papers while actively engaging as a Reviewer for international publications.
Content
1. Introduction. 2. Foundations of Deep Learning. 3. Neurological Conditions and Rehabilitation Challenges. 4. Personalized Healthcare through Deep Learning. 5. Case Studies in Stroke Recovery. 6. Advancements in Brain Injury Rehabilitation. 7. Neural Rehabilitation for Neurological Disorders. 8. Wearable Technology in Neural Rehabilitation. 9. Ethical Considerations and Patient Privacy. 10. Interdisciplinary Collaborations: The Future of Healthcare. 11. Global Perspectives on Neural Rehabilitation. 12. Challenges and Future Directions. 13. Industry Perspectives: Innovations and Partnerships. 14. Towards Inclusive Healthcare: Accessibility and Affordability. 15. Conclusion: Shaping the Future of Neural Rehabilitation.