Intelligent Gait Assistive Technologies: Gait Biomechanics and Machine Learning Applications in Rehabilitation and Injury Prevention brings together contemporary research and applications to show how gait biomechanics combined with machine learning can be used to develop techniques to provide safer, more mechanically efficient locomotion to individuals with significant visual, musculoskeletal, or neurological deficits. Developments in gait rehabilitation and injury prevention outlined in this book will contribute to improved quality of life for individuals with gait-related impairments, with a major contribution to medical cost savings due to reduction in falls. Researchers, engineers, and students in biomedical engineering and biomechanics will find this a welcomed reference in better understanding the role of machine learning and intelligent technologies in the advancement of gait rehabilitation and injury reduction to both impaired and healthy individuals.
Table of Contents
Part I: Gait Biomechanics Tripping, Slipping and Balance Loss1. Fundamentals of Gait Biomechanics
2. Kinematics and Kinetics of Lower Limb Swing Phase Trajectory Control
3. Minimum Foot-Ground Clearance (MFC) and Tripping Probability Modelling
4. Required Coefficient of Friction (RCOF) and Slipping Prediction
5. Gait Adaptations due to Ageing, Injury and Pathologies
6. Gait Impairments Causing Tripping, Slipping and Balance Loss
Part II: Gait Assisting Techniques and Devices
7. Biofeedback-Based Gait Training Interventions
8. Passive Exoskeletons
9. Active Exoskeletons
10. Intelligent Footwear: Smart Insoles, Shoe-Mounted Sensors
Part III: Machine Learning Applications to Gait Assisting Techniques
11. Predicting Gait Kinematics from Inertial Sensors
12. Limb Trajectory Prediction (i): Critical Failure Events
13. Limb Trajectory Prediction (ii): Intelligent Assistive Device Control and Tripping Hazard Recognition
Part IV Conclusions, Emerging Techniques and Future Directions
14. Future Challenges in Rehabilitation and Injury Prevention
15. Research Directions in Gait Biomechanics and Machine Learning
16. References