Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications

Table of contents

1. Cover image
2. Title page
3. Table of Contents
4. Copyright
5. Contributors
6. Preface
7. Chapter 1. AI-based technologies, challenges, and solutions for neurorehabilitation: A systematic mapping
   1. 1. Introduction
   2. 2. Recent developments and involvement of artificial intelligence in healthcare and neurorehabilitation
   3. 3. Clinical challenges and robotic rehabilitation applications
   4. 4. High-tech collaborations driving neural rehabilitation
   5. 5. Selection of AI-based supportive technologies and tools for instigating neurorehabilitation
   6. 6. Conclusions and future directions
8. Chapter 2. Complex approaches for gait assessment in neurorehabilitation
   1. 1. Introduction
   2. 2. Theoretical approach of gait analysis
   3. 3. Gait pattern—theoretical and practical framing
   4. 4. Clinical applications of gait analysis in neurorehabilitation
   5. 5. Case studies
   6. 6. Conclusions
9. Chapter 3. Deep learning method for adult patients with neurological disorders under remote monitoring
   1. 1. Introduction
   2. 2. Related works
   3. 3. Proposed methodology
   4. 4. Discussions
   5. 5. Conclusion
   6. 6. Future scope
10. Chapter 4. Rehabilitation for individuals with autism spectrum disorder using mixed reality virtual assistants
    1. 1. Introduction
    2. 2. Related work
    3. 3. Exploring the virtual world with Microsoft HoloLens
    4. 4. ASD pathways: Tools for rehabilitation and progress
    5. 5. Challenges faced
    6. 6. Future directions
    7. 7. Conclusion
11. Chapter 5. Wearable sleeve for physiotherapy assessment using ESP32 and IMU sensor
    1. 1. Introduction
    2. 2. Literature survey
    3. 3. Proposed system: Materials and methodology
    4. 4. Experimental results and discussion
    5. 5. Conclusion and future scope
12. Chapter 6. Machine learning for Developing neurorehabilitation-aided assistive devices
    1. 1. Introduction
    2. 2. Machine learning as a tool in assistive technology for neurorehabilitation
    3. 3. Applications of ML-integrated assistive technology in neurorehabilitation
    4. 4. Conclusion
13. Chapter 7. Deep learning and machine learning methods for patients with language and speech disorders
    1. 1. Introduction
    2. 2. Research methodology
    3. 3. DL and ML methods for patients with SLD
14. Chapter 8. Machine learning for cognitive treatment planning in patients with neurodisorder and trauma injuries
    1. 1. Introduction
    2. 2. Related work
    3. 3. Proposed methodology
    4. 4. Results and discussion
    5. 5. Conclusion
15. Chapter 9. Artifacts removal techniques in EEG data for BCI applications: A survey
    1. 1. Introduction
    2. 2. Artifacts removal techniques
    3. 3. Conclusion
16. Chapter 10. Deep learning system of naturalistic communication in brain–computer interface for quadriplegic patient
    1. 1. Introduction
    2. 2. Related works
    3. 3. Dataset
    4. 4. Methodology
    5. 5. Experimental results
    6. 6. Discussion
    7. 7. Conclusion
17. Chapter 11. Motor imaginary tasks-based EEG signals classification using continuous wavelet transform and LSTM network
    1. 1. Introduction
    2. 2. Material and methodology
    3. 3. Conclusion
18. Chapter 12. Enhancing human brain activity through a systematic study conducted using graph theory and probability concepts on a hydar prehistoric organism
    1. 1. Introduction
    2. 2. Methodology
    3. 3. Hydar influence in brain activity
    4. 4. Artificial intelligence framework
    5. 5. Experiments conducted using Hydar
    6. 6. Survey on impact of technology in mental health
    7. 7. Conclusion
19. Index