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Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications
- 1st Edition - November 14, 2023
- Editor: D. Jude Hemanth
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 7 7 2 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 7 7 3 - 0
Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation method… Read more
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Request a sales quoteComputational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. The book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries.
Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control.
Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control.
- Incorporates artificial intelligence techniques into neuro-rehabilitation and presents novel ideas for this process
- Provides in-depth case studies and state-of-the-art methods, along with the experimental study
- Presents a block diagram based complete set-up in each chapter to help in real-time implementation
All undergraduates, post graduates, researchers, faculty members and physicians. All UG students, PG students, research scholars, faculty members
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter 1. AI-based technologies, challenges, and solutions for neurorehabilitation: A systematic mapping
- 1. Introduction
- 2. Recent developments and involvement of artificial intelligence in healthcare and neurorehabilitation
- 3. Clinical challenges and robotic rehabilitation applications
- 4. High-tech collaborations driving neural rehabilitation
- 5. Selection of AI-based supportive technologies and tools for instigating neurorehabilitation
- 6. Conclusions and future directions
- Chapter 2. Complex approaches for gait assessment in neurorehabilitation
- 1. Introduction
- 2. Theoretical approach of gait analysis
- 3. Gait pattern—theoretical and practical framing
- 4. Clinical applications of gait analysis in neurorehabilitation
- 5. Case studies
- 6. Conclusions
- Chapter 3. Deep learning method for adult patients with neurological disorders under remote monitoring
- 1. Introduction
- 2. Related works
- 3. Proposed methodology
- 4. Discussions
- 5. Conclusion
- 6. Future scope
- Chapter 4. Rehabilitation for individuals with autism spectrum disorder using mixed reality virtual assistants
- 1. Introduction
- 2. Related work
- 3. Exploring the virtual world with Microsoft HoloLens
- 4. ASD pathways: Tools for rehabilitation and progress
- 5. Challenges faced
- 6. Future directions
- 7. Conclusion
- Chapter 5. Wearable sleeve for physiotherapy assessment using ESP32 and IMU sensor
- 1. Introduction
- 2. Literature survey
- 3. Proposed system: Materials and methodology
- 4. Experimental results and discussion
- 5. Conclusion and future scope
- Chapter 6. Machine learning for Developing neurorehabilitation-aided assistive devices
- 1. Introduction
- 2. Machine learning as a tool in assistive technology for neurorehabilitation
- 3. Applications of ML-integrated assistive technology in neurorehabilitation
- 4. Conclusion
- Chapter 7. Deep learning and machine learning methods for patients with language and speech disorders
- 1. Introduction
- 2. Research methodology
- 3. DL and ML methods for patients with SLD
- Chapter 8. Machine learning for cognitive treatment planning in patients with neurodisorder and trauma injuries
- 1. Introduction
- 2. Related work
- 3. Proposed methodology
- 4. Results and discussion
- 5. Conclusion
- Chapter 9. Artifacts removal techniques in EEG data for BCI applications: A survey
- 1. Introduction
- 2. Artifacts removal techniques
- 3. Conclusion
- Chapter 10. Deep learning system of naturalistic communication in brain–computer interface for quadriplegic patient
- 1. Introduction
- 2. Related works
- 3. Dataset
- 4. Methodology
- 5. Experimental results
- 6. Discussion
- 7. Conclusion
- Chapter 11. Motor imaginary tasks-based EEG signals classification using continuous wavelet transform and LSTM network
- 1. Introduction
- 2. Material and methodology
- 3. Conclusion
- Chapter 12. Enhancing human brain activity through a systematic study conducted using graph theory and probability concepts on a hydar prehistoric organism
- 1. Introduction
- 2. Methodology
- 3. Hydar influence in brain activity
- 4. Artificial intelligence framework
- 5. Experiments conducted using Hydar
- 6. Survey on impact of technology in mental health
- 7. Conclusion
- Index
- No. of pages: 302
- Language: English
- Edition: 1
- Published: November 14, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780443137723
- eBook ISBN: 9780443137730
DH
D. Jude Hemanth
Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of “Visiting Professor” in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.
Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
Affiliations and expertise
Professor, ECE Department, Karunya Institute of Technology and Sciences, Coimbatore, IndiaRead Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications on ScienceDirect