Computational intelligence and deep learning methods for neuro-rehabilitation applications /
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. This book provides in-depth knowledge on the challenges and solutions associated with th...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
London :
Academic Press,
2024.
|
| Schlagworte: | |
| Online-Zugang: | ScienceDirect |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
MARC
| LEADER | 00000cam a2200000 a 4500 | ||
|---|---|---|---|
| 001 | on1409544675 | ||
| 003 | OCoLC | ||
| 005 | 20260915095212.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 231118s2024 enk o 000 0 eng d | ||
| 040 | |a YDX |b eng |c YDX |d OPELS |d OCLCO |d OCLKB |d ORMDA |d OCLCL |d OCLCF |d OCLCO |d ESU | ||
| 019 | |a 1419149205 | ||
| 020 | |a 9780443137730 |q (electronic bk.) | ||
| 020 | |a 0443137730 |q (electronic bk.) | ||
| 020 | |z 9780443137723 | ||
| 020 | |z 0443137722 | ||
| 035 | |a (OCoLC)1409544675 |z (OCoLC)1419149205 | ||
| 050 | 4 | |a RC350.4 | |
| 082 | 0 | 4 | |a 616.8043 |2 23/eng/20240206 |1 https://id.oclc.org/worldcat/ddc/E36dwm7vPFxQpCJ3FBj8hWJKBr |
| 245 | 0 | 0 | |a Computational intelligence and deep learning methods for neuro-rehabilitation applications / |c edited by D. Jude Hemanth. |
| 260 | |a London : |b Academic Press, |c 2024. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a 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. This 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. | ||
| 505 | 0 | |a Front Cover -- Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications -- Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications -- Copyright -- Contents -- Contributors -- Preface -- 1 -- AI-based technologies, challenges, and solutions for neurorehabilitation: A systematic mapping -- 1. Introduction -- 1.1 Artificial intelligence -- 1.2 Neurological disorders -- 2. Recent developments and involvement of artificial intelligence in healthcare and neurorehabilitation -- 3. Clinical challenges and robotic rehabilitation applications -- 3.1 Clinical challenges -- 3.2 Robotic rehabilitation applications -- 4. High-tech collaborations driving neural rehabilitation -- 4.1 Human-robot control interfaces -- 4.1.1 Digital-neural interfaces -- 4.1.2 Electromyography -- 4.1.3 Brain-computer interfaces -- 4.2 Neurorobotics -- 4.2.1 Neuroexoskeletons -- 4.2.2 Neuroprosthetics -- 4.3 Virtual and augmented reality -- 4.4 AI algorithms -- 5. Selection of AI-based supportive technologies and tools for instigating neurorehabilitation -- 6. Conclusions and future directions -- References -- Further reading -- 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 -- 4.1 Multiple sclerosis -- 4.2 Hemiparesis -- 4.3 Paraparesis postchemotherapy (postchemotherapy polyneuropathy CIPN) -- 5. Case studies -- 5.1 Case study 1 -- 5.2 Case study 2 -- 5.3 Case study 3 -- 6. Conclusions -- References -- 3 -- Deep learning method for adult patients with neurological disorders under remote monitoring -- 1. Introduction -- 2. Related works -- 2.1 Modern artificial intelligence -- 2.2 Deep learning. | |
| 505 | 8 | |a 2.3 Convolutional neural networks -- 2.4 Data source -- 2.5 Image classification -- 3. Proposed methodology -- 3.1 Image classification -- 3.2 Object detection -- 3.3 Recognition of human activity -- 3.4 System architecture -- 3.5 Monitoring application -- 4. Discussions -- 5. Conclusion -- 6. Future scope -- References -- 4 -- Rehabilitation for individuals with autism spectrum disorder using mixed reality virtual assistants -- 1. Introduction -- 2. Related work -- 2.1 Common interventions in rehabilitation of ASD -- 3. Exploring the virtual world with Microsoft HoloLens -- 3.1 Microsoft HoloLens, a wearable mixed reality device -- 3.2 HoloLearn: Unpacking the mechanics of mixed reality learning -- 3.3 Exploring the world of autism: A HoloLearn activity -- 3.3.1 Setting the table activity -- 3.3.2 HoloLearn's garbage collection efforts -- 3.3.3 HoloLearn memory activity -- 3.3.4 Virtual assistant -- 3.3.5 Dress-up activity -- 4. ASD pathways: Tools for rehabilitation and progress -- 4.1 Autism XR -- 4.2 Real social skills -- 4.3 PRISMA -- 4.4 Discussions -- 5. Challenges faced -- 6. Future directions -- 7. Conclusion -- References -- 5 -- Wearable sleeve for physiotherapy assessment using ESP32 and IMU sensor -- 1. Introduction -- 1.1 Existing methods -- 1.2 Scope and objectives -- 2. Literature survey -- 3. Proposed system: Materials and methodology -- 3.1 IMU sensor -- 3.2 System architecture -- 3.3 System implementation -- 3.4 Installation of mircopython on ESP32 -- 3.5 Interfacing MPU9250 with ESP32 -- 3.6 Graphical user interface -- 3.7 Updating the database on the server end -- 3.8 Detecting the angle of the set -- 3.9 Graphical report -- 4. Experimental results and discussion -- 4.1 Quantitative measures -- 4.2 Quality measures -- 5. Conclusion and future scope -- References. | |
| 505 | 8 | |a 6 -- Machine learning for Developing neurorehabilitation-aided assistive devices -- 1. Introduction -- 1.1 Neurological disorders -- 1.2 Neurorehabilitation and assistive technology -- 2. Machine learning as a tool in assistive technology for neurorehabilitation -- 2.1 Integration of ML in assistive technology and its applications -- 3. Applications of ML-integrated assistive technology in neurorehabilitation -- 3.1 Prosthetics and orthotics -- 3.2 Augmentative and alternative communication devices -- 3.3 Adaptive computer technology -- 3.4 Mobility aids -- 3.5 Sensory aids -- 3.6 Cognitive aids -- 3.7 Environmental control -- 4. Conclusion -- References -- 7 -- Deep learning and machine learning methods for patients with language and speech disorders -- 1. Introduction -- 1.1 Language and speech disorders: Types, diagnosis, and treatment -- 1.1.1 Fluency speech disorders -- 1.1.2 Resonance disorders -- 1.1.3 Voice disorders -- 1.1.4 Motor speech disorders -- 1.1.5 Language disorders -- 1.1.6 Acquired language disorders -- 1.1.7 Speech sound disorders -- 1.1.7.1 Deep learning and machine learning in AI -- 1.1.7.1 Deep learning and machine learning in AI -- 2. Research methodology -- 2.1 Research questions -- 2.2 Data collection and data analysis process -- 3. DL and ML methods for patients with SLD -- References -- 8 -- Machine learning for cognitive treatment planning in patients with neurodisorder and trauma injuries -- 1. Introduction -- 1.1 Multiple sclerosis -- 1.1.1 Multiple sclerosis signs and symptoms -- 1.1.2 Treatment of multiple sclerosis -- 1.2 Parkinson's disease -- 1.2.1 Signs and symptoms of Parkinson's disease -- 1.2.2 Treatment process of Parkinson's disease -- 1.2.3 Exercise and alternative treatments -- 1.3 Importance of artificial intelligence in healthcare -- 2. Related work -- 3. Proposed methodology. | |
| 505 | 8 | |a 3.1 Methodology for predicting stroke in patients who have already had one -- 4. Results and discussion -- 5. Conclusion -- References -- 9 -- Artifacts removal techniques in EEG data for BCI applications: A survey -- 1. Introduction -- 1.1 Signal capturing block -- 1.2 Processing of signals block -- 1.3 Signal preprocessing -- 1.4 Feature extraction -- 1.5 Classification -- 1.6 Application interface -- 1.7 Applications -- 1.8 Feedback -- 2. Artifacts removal techniques -- 3. Conclusion -- References -- Further reading -- 10 -- Deep learning system of naturalistic communication in brain-computer interface for quadriplegic patient -- 1. Introduction -- 2. Related works -- 3. Dataset -- 4. Methodology -- 4.1 Convolutional neural network -- 4.1.1 CNN layers -- 4.1.2 Pooling layer -- 4.1.3 Fully connected layer -- 4.2 AlexNet -- 4.2.1 Softmax -- 4.2.2 ReLU -- 4.2.3 Data augmentation -- 4.2.4 Dropout -- 4.3 Computer vision -- 4.4 Image recognition -- 5. Experimental results -- 6. Discussion -- 7. Conclusion -- References -- 11 -- Motor imaginary tasks-based EEG signals classification using continuous wavelet transform and LSTM network -- 1. Introduction -- 2. Material and methodology -- 2.1 Dataset (PhysioNet) -- 2.2 EEG preprocessing and feature extraction -- 2.2.1 Continuous wavelet transform -- 2.2.2 Features -- 2.2.2.1 Entropy features -- 2.2.2.1 Entropy features -- 2.2.2.1.1 Shannon Entropy -- 2.2.2.1.1 Shannon Entropy -- 2.2.2.1.2 Sure entropy -- 2.2.2.1.2 Sure entropy -- 2.2.2.2 Statistical features -- 2.2.2.2 Statistical features -- 2.2.2.2.1 Mean -- 2.2.2.2.1 Mean -- 2.2.2.2.2 Standard deviation -- 2.2.2.2.2 Standard deviation -- 2.2.2.2.3 Skewness -- 2.2.2.2.3 Skewness -- 2.2.2.2.4 Normalized standard deviation -- 2.2.2.2.4 Normalized standard deviation -- 2.2.2.2.5 Kurtosis -- 2.2.2.2.5 Kurtosis -- 2.2.2.2.6 Energy -- 2.2.2.2.6 Energy. | |
| 505 | 8 | |a 2.2.2.2.7 Normalized energy -- 2.2.2.2.7 Normalized energy -- 2.2.2.3 Fractal dimension -- 2.2.2.3 Fractal dimension -- 2.2.2.3.1 Higuchi fractal dimension -- 2.2.2.3.1 Higuchi fractal dimension -- 2.2.2.3.2 Katz fractal dimension -- 2.2.2.3.2 Katz fractal dimension -- 2.3 Classification -- 2.3.1 Long short-term memory technique -- 2.3.1.1 Results and discussions -- 2.3.1.1 Results and discussions -- 2.4 Performance measures -- 2.5 Classification performance evaluation -- 3. Conclusion -- References -- Further reading -- 12 -- Enhancing human brain activity through a systematic study conducted using graph theory and probability concept ... -- 1. Introduction -- 2. Methodology -- 2.1 Bayesian inference -- 2.2 Probability density -- 3. Hydar influence in brain activity -- 3.1 Movement of actual robot -- 4. Artificial intelligence framework -- 5. Experiments conducted using Hydar -- 5.1 Experiment-The first -- 5.2 Experiment-Observe what is expected -- 5.3 Experiment-Different forward models -- 5.4 Experiment-Higher embedding orders -- 5.5 Experiment-Colored noise -- 5.6 Experiment-White noise -- 5.7 Plotting 4D probabilistic atlas maps -- 5.8 Plotting a statistical map -- 5.9 Glass brain visualization -- 6. Survey on impact of technology in mental health -- 6.1 Have/had mental health disorder -- 6.2 Companies taking mental health seriously -- 6.3 Discussing mental health at work -- 6.4 Mental health having consequences on career -- 6.5 Being open about mental health with friends and family -- 6.6 Percentages of people with mental health disorders out of all respondents in the United States -- 7. Conclusion -- References -- Index -- Back Cover. | |
| 650 | 0 | |a Nervous system |x Diseases |x Patients |x Rehabilitation. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Deep learning (Machine learning) |x Therapeutic use. | |
| 650 | 6 | |a Intelligence informatique. |0 (CaQQLa)201-0265216 | |
| 650 | 6 | |a Apprentissage profond |0 (CaQQLa)000311746 |x Emploi en thérapeutique. |0 (CaQQLa)201-0373975 | |
| 650 | 7 | |a Computational intelligence |2 fast |0 (OCoLC)fst00871995 | |
| 650 | 7 | |a Nervous system |x Diseases |x Patients |x Rehabilitation |2 fast |0 (OCoLC)fst01036116 | |
| 700 | 1 | |a Hemanth, D. Jude. | |
| 776 | 0 | 8 | |i Print version: |z 0443137722 |z 9780443137723 |w (OCoLC)1380459809 |
| 776 | 0 | 8 | |i Print version: |t Computational intelligence and deep learning methods for neuro-rehabilitation applications. |d Amsterdam : Academic Press, 2023 |z 9780443137723 |w (OCoLC)1400084151 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443137723 |