Technology-Assisted Physical Gait Rehabilitation: How Robotics, Data Science, and Neuroscience are Changing Modern Physical Therapy delves into the integration of advanced computational modeling, robotic systems, data analytics, and neuroscientific principles to revolutionize physical gait therapy. This book addresses rehabilitation techniques for conditions like stroke, traumatic brain injury, and incomplete spinal cord injury, showcasing a multidisciplinary approach that brings together expertise from various domains. This comprehensive volume gathers insights from leading experts in robotics, human biomechanics, physical therapy, neuroscience, engineering, and medicine.
It discusses current advancements and future directions in technology-assisted gait therapy, emphasizing the synergy of interdisciplinary collaboration to achieve breakthroughs in rehabilitation techniques. The book also highlights ongoing work and the potential developments necessary to foster significant progress in this field.
1. Introduction
PART I: Neuroscience perspective on motor recovery
2. Clinical perspective on functional gait disorders
3. Motor learning - what constitutes, enables, and improves outcomes in neuro-impaired individuals
PART II: Opinion pieces on the main technology
4. Closing the loop between wearable technology and human biology
5. Crunching through data - how machine learning is transforming human movement analysis
6. Challenges in making neuromusculoskeletal models clinically useful
PART III: The role of human biomechanics in motor recovery
7. The outcomes and lessons from a constrained walking study
8. Motion and joint function in human gait
9. The role of muscle synergies in maximizing motor recovery
10. Optimality in human gait - the role of symmetry in motor learning
11. Error augmentation and haptic interventions during motor learning
PART IV: Technology-assisted motor function recovery
12. An overview of technology-assisted gait rehabilitation
13. Predictive simulations for better understanding neuromechanics of gait
15. Portable gait lab - taking mocap into clinical and community environments
16. Analyzing human gait using machine learning and explainable artificial intelligence
17. Concluding remarks
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