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Technology-Assisted Physical Gait Rehabilitation: How Robotics, Data Science, and Neuroscience are Changing Modern Physical Therapy explores computational modeling techniques, robotic assistance, data science, and other technological tools and how they can be jointly used in driving and guiding physical gait therapy in impairments such as stroke, traumatic brain injury, and incomplete spinal cord injury. This book gathers experts in robotics, human biomechanics, physical therapy, neuroscience, engineering, and medicine, presenting their ongoing work and discusses their views of the future direction of technology-assisted gait therapy, showcasing the latest advancements in the interdisciplinary and multidisciplinary field of technology-assisted gait therapy and strives to outline the developments in the coming years that are needed/likely to make the next big breakthrough in the field.
List of contents
1. Introduction
PART I: Neuroscience perspective on motor recovery2. 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 technology4. 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 recovery7. 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 recovery12. 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
About the author
Tomislav Baček is an early career researcher in robot-assisted therapy, human-robot interaction, and human biomechanics. Dr. Baček received his B.Sc. and M.Sc. Engineering degrees from the University of Zagreb, Croatia, in 2011 and 2012, respectively, and his Ph.D. in engineering sciences in 2019 from Vrije Universiteit Brussel, Belgium. In 2020, Tomislav started his ongoing position as a postdoctoral researcher at the University of Melbourne, Australia, where he's been leading a project on personalization of robot-guided gait therapy.Denny Oetomo conducts research in robot dynamics and human-robot interaction applied to assistive robotics technology. He received his Ph.D. in Mechanical Engineering (Robotics) from National University of Singapore in 2004 and was a postdoctoral fellow at INRIA Sophia Antipolis and Monash University (2004-2007). He joined the Department of Mechanical Engineering, The University of Melbourne in 2008. He currently leads the robotic research activities in the Department in the topics of rehabilitation robotics, advanced prosthetics, and assistive robotics in industrial applications.Dana Kulić conducts research in robotics, learning and human-robot interaction (HRI). She received her combined B. A. Sc. and M. Eng. degrees in electro-mechanical engineering and Ph.D. degree in mechanical engineering from the University of British Columbia, Canada, in 1998 and 2005, respectively. From 2006 to 2009, Dr. Kulić was a JSPS Post-doctoral Fellow and a Project Assistant Professor at the Nakamura-Yamane Laboratory at the University of Tokyo, Japan. From 2009 - 2018, she led the Adaptive System Laboratory at the University of Waterloo, Canada, conducting research in human robot interaction, human motion analysis for rehabilitation and humanoid robotics. Dr. Kulić is a professor and director of Monash Robotics at Monash University, Australia. In 2020, Dr. Kulić was awarded the ARC Future Fellowship.Ying Tan is a Professor in the Department of Mechanical Engineering at The University of Melbourne, Australia. She received her bachelor’s degree from Tianjin University, China, in 1995, and her PhD from the National University of Singapore in 2002. She joined McMaster University in 2002 as a postdoctoral fellow in the Department of Chemical Engineering. Since 2004, she has been with the University of Melbourne. She was awarded an Australian Postdoctoral Fellow (2006-2008) and a Future Fellow (2009-2013) by the Australian Research Council. Her research interests are in intelligent systems, nonlinear systems, realtime optimization, sampled-data systems, rehabilitation robotic systems, human motor learning, and model-guided machine learning.
