Intelligent Gait Assistive Technologies
Gait Biomechanics and Machine Learning Applications in Rehabilitation and Injury Prevention
Will be published approx. on 1. March 2027
Book
Paperback/Softback
300 pages
978-0-443-14104-1 (ISBN)
Description
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.
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.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 229 mm
Width: 152 mm
Weight
450 gr
ISBN-13
978-0-443-14104-1 (9780443141041)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Rezaul Begg is chair in Assistive Technologies within the Program in Assistive Technology Innovation (PATI) at Victoria University. He leads a multidisciplinary "Gait and Intelligent Technologies? research group focused on technologies with application to both unimpaired and pathological gait. He uses a combination of engineering and biomechanical principles to understand and diagnose locomotion-related deficits, and to provide intelligent technology solutions to improving walking efficiency and safety. Professor Begg has published five books and over 300 refereed papers in scientific journals and conferences proceedings. He is associate editor of Frontiers in Bioengineering and Biotechnology, and an editorial board member of the Journal of Biomechanics and Sensors. He has received several Best Paper awards in conferences, the Victoria University Vice Chancellor's citation award for Excellence in Research, a Gold Medal from BUET and Chancellor's Prize from Bangladesh Government for Academic Excellence. Hanatsu Nagano specializes in gait biomechanics with special focus on application for practical interventions. He invented the special shoe-insole that reduces tripping, falls, and improves dynamic balance, which was awarded 'Most Innovative New Care Product in the World' for the three consecutive years. He is experienced in working closely with industry-leading research & development projects including regenerative medical technology, management of knee osteoarthritis, wearable sensor application, artificial intelligence, exercise intervention, and active exoskeletons. His entrepreneurship and business communication skills have successfully established various international academia-industry-government collaborations. He sets his current missions in developing smart footwear for safe walking, inventing regenerative technology integrated knee orthosis, coordinating health enhancing intervention programs for government-leading initiatives, establishing further synergy effects based on research-industry linkage, and promoting Hybrid Assistive Limb (HAL) and its core philosophy, Cybernics, worldwide.
Author
Professor, Chair, Assistive Technologies within the Program in Assistive Technology Innovation (PATI), Footscray Park Campus, Victoria University, Australia
Postdoctoral Research Fellow, Institute of Sport, Exercise & Active Living (ISEAL), Footscray Park Campus, Victoria University, Australia
Content
Part I: Gait Biomechanics - Tripping, Slipping and Balance Loss
1. 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
1. 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