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Advanced Rehabilitative Technology

  • ID: 4483010
  • Book
  • August 2018
  • Region: Global
  • 276 Pages
  • Elsevier Science and Technology
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Advanced Rehabilitative Technology: Neural Interfaces and Devices teaches readers how to acquire and process bio-signals using biosensors and acquisition devices, how to identify the human movement intention and decode the brain signal, how to design physiological and musculoskeletal models and establish the neural interfaces, and how to develop neural devices and control them efficiently using biological signals. The book takes a multidisciplinary theme between the engineering and medical field, including sections on neuromuscular/brain signal processing, human motion and intention recognition, biomechanics modelling and interfaces, and neural devices and control for rehabilitation.

Each chapter goes through a detailed description of the bio-mechatronic systems used and then presents implementation and testing tactics. In addition, it details new neural interfaces and devices, some of which have never been published before in any journals or conferences. With this book, readers will quickly get up-to-speed on the most recent and future advancements in bio-mechatronics engineering for applications in rehabilitation.

  • Presents insights into emerging technologies and developments that are currently used or on the horizon in biological systems and mechatronics for rehabilitative purposes
  • Gives a comprehensive background of biological interfaces and details of new advances in the field
  • Addresses the challenges of rehabilitative applications in areas of bio-signal processing, bio-modelling, neural and muscular interface, and neural devices.
  • Provides substantial background materials and relevant case studies for each subject

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1. Introduction
2. State of the Art
3. Neuromuscular Signal Acquisition and Pre-Processing
4. EMG-Based Motion Recognition
5. Brain Signal Acquisition and Pre-Processing
6. EEG-Based Brain Intention Recognition
7. Neuromuscular Modelling
8. Neural Interfaces
9. Neural Devices
10. Conclusion
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Ai, Qingsong
Qingsong Ai is currently a Professor at Wuhan University of Technology and a Senior Editor of Cogent Engineering. He is an author of more than 50 technical publications, proceedings, and editorials. In recent years, he has directed more than 10 research projects. His research interests include signal processing, rehabilitation robots, and advanced manufacturing technology.
Liu, Quan
Quan Liu is currently a Professor in School of Information Engineering at Wuhan University of Technology. In the past 5 years, she authored more than 60 technical publications, proceedings, editorials, and books. She has directed more than 20 research projects. Her research interests include signal processing, embedded systems, and robots and electronics. Prof. Liu received two national awards and three provincial and ministerial awards. She was awarded as the "National Excellent Teacher” in 2007. She is a Council Member of the Chinese Association of Electromagnetic Compatibility and the Hubei Institute of Electronics.
Meng, Wei
Wei Meng is currently a Lecturer at the School of Information Engineering, Wuhan University of Technology. His research interests include robot-assisted rehabilitation, human-robot interaction, and iterative learning control. He has co-authored 2 books, published more than 30 academic journal and conference papers, and holds 3 patents.
Xie, Sheng Quan
Sheng Quan Xie is currently a Chair Professor in Robotics and Autonomous Systems, at the Faculty of Engineering, University of Leeds. He has published 7 books, 15 book chapters, and more than 300 international journal and conference papers. His current research interests include medical and rehabilitation robots and advanced robot control. Prof. Xie was elected a Fellow of The Institution of Professional Engineers New Zealand in 2016. He has also served as a Technical Editor of the IEEE/ASME TRANSACTIONS ON MECHATRONICS.
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