Computational 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.
- 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
1. AI based Technologies, Challenges and Solutions for Neuro-rehabilitation: A Systematic Mapping
2. Complex Approaches for Gait Assessment in Neurorehabilitation
3. Deep learning method for adult patients with neurological disorders under remote monitoring
4. Rehabilitation for individuals with Autism Spectrum Disorder Using Mixed Reality-Virtual Assistants
5. Wearable Sleeve for Physiotherapy Assessment Using ESP32 And IMU Sensor
6. Machine Learning for Developing Neuro Rehabilitation-Aiding Assistive Devices
7. Deep Learning and Machine Learning Methods for Patients With Language and Speech Disorders
8. Machine Learning for Cognitive Treatment Planning in Patients with Neuro-disorder and Trauma Injuries
9. Artifacts Removal Techniques In EEG Data for BCI Applications : A Survey
10. Deep learning system based naturalistic communication in brain-computer interface for quadriplegic patient
11. Motor Imaginary Tasks-Based EEG Signals Classification Using Continuous Wavelet Transform And LSTM Network
12. Enhancing human brain activity through a systematic study conducted using graph theory and probability concepts on a Hydra prehistoric organism.
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.
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