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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
Researchers, academics and graduate students, who are studying in the field of neural interfaces and rehabilitation. Bioengineers, Rehabilitation Engineers, Clinicians. Academics that are teaching courses in neural and rehab technologies
- Introduction. In this chapter, the biological systems, neural interfaces and devices for rehabilitation will be introduced; motivation of this book will be clarified
2. State of the Art. In this chapter, the recent development of neuromuscular and brain rehab will be investigated. The modelling and interfacing technologies of the existing research will be reviewed and support the motivation of our work.
3. Neuromuscular Signal Acquisition and Pre-Processing. In this chapter, the neuro activation principle, bio-sensing and acquisition devices will be developed. The most recent EMG signal pre-processing methods will be presented as well
4. EMG-Based Motion Recognition. In this chapter, the motor-related EMG feature extraction and motion recognition methods for body movement rehabilitation will be investigated. Both hand gesture and gait identification strategy will be studied
5. Brain Signal Acquisition and Pre-Processing. In this chapter, various EEG signals such as SSVEP and P300 will be analysed. The bio-sensors and acquisition devices as well as processing methods for brain activity monitoring will be presented
6. EEG-Based Brain Intention Recognition. In this chapter, the collected EEG signals will be used to decode human brain intention. A series of processing methods will be proposed to achieve accurate recognition of the SSVEP and hybrid EEG signal
7. Neuromuscular Modelling. In this chapter, a musculoskeletal model will be developed on the basis of biological organisms and physiological analysis. The EMG-driven musculoskeletal model will be established for interactive rehabilitation
8. Neural Interfaces. In this chapter, the neuromuscular interface and brain computer interface will be developed and can be applied to human robot interactive control. The modelling and application of the interfaces robot will be discussed
9. Neural Devices. In this chapter, a variety of neural devices such as robotic arm and neural keyboard will be designed and developed to verify the bio-interfaces and explore the application of this study for rehabilitation purpose
10. Conclusion. In this chapter, the main contributions and conclusion of this book will be conducted. Current limitations and future trends will also be discussed
- No. of pages:
- © Academic Press 2019
- 20th August 2018
- Academic Press
- Paperback ISBN:
- eBook ISBN:
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.
Professor, School of Information Engineering, Wuhan University of Technology
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.
Professor in School of Information Engineering, Wuhan University of Technology
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.
Lecturer, School of Information Engineering, Wuhan University of Technology
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.
Chair Professor in Robotics and Autonomous Systems, Faculty of Engineering, University of Leeds, UK