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
| Author Biography |
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vii | |
| Preface |
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ix | |
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1 | (10) |
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1 | (1) |
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1.2 Human Biological Systems |
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2 | (2) |
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1.3 Neural Interfaces and Devices |
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4 | (2) |
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6 | (1) |
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6 | (2) |
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8 | (2) |
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10 | (1) |
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11 | (22) |
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11 | (8) |
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19 | (6) |
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2.3 Neural Modeling and Interfaces |
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25 | (3) |
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28 | (1) |
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28 | (5) |
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3 Neuromuscular Signal Acquisition and Processing |
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33 | (34) |
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33 | (2) |
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3.2 sEMG Acquisition Devices |
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35 | (20) |
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3.3 sEMG Signal Preprocessing |
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55 | (10) |
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65 | (1) |
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66 | (1) |
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4 sEMG-Based Motion Recognition |
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67 | (38) |
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4.1 sEMG Feature Extraction and Classification |
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67 | (5) |
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4.2 Hand Gesture Recognition |
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72 | (9) |
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4.3 Ankle Motion Recognition |
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81 | (5) |
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4.4 Continuous Motion Recognition of Wrist Joint |
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86 | (15) |
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101 | (1) |
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102 | (2) |
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104 | (1) |
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5 Brain Signal Acquisition and Preprocessing |
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105 | (30) |
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5.1 Research Background and Significance |
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105 | (1) |
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5.2 SSVEP/P300/Motor Imagery Signal |
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106 | (7) |
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5.3 Stimulators and Acquisition Devices |
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113 | (4) |
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117 | (14) |
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131 | (1) |
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132 | (3) |
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6 EEG-Based Brain Intention Recognition |
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135 | (32) |
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6.1 EEG Feature Extraction and Classification |
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135 | (2) |
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6.2 SSVEP-Based Intent Recognition |
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137 | (11) |
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6.3 P300-Based Intention Recognition |
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148 | (6) |
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6.4 Motor Imagery-Based Intention Recognition |
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154 | (10) |
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164 | (1) |
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164 | (3) |
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167 | (40) |
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167 | (7) |
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7.2 sEMG-Driven Musculoskeletal Model |
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174 | (10) |
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7.3 Muscle Force Estimation |
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184 | (12) |
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7.4 Neuromuscular Models in Robotic Rehabilitation |
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196 | (8) |
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204 | (1) |
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204 | (1) |
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205 | (2) |
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207 | (38) |
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8.1 Neuromuscular Interface |
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207 | (12) |
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8.2 Brain-Computer Interface |
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219 | (11) |
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8.3 Interactive Control Interface |
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230 | (12) |
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242 | (1) |
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242 | (1) |
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243 | (2) |
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9 Conclusion and Future Prospects |
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245 | (10) |
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245 | (5) |
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9.2 Outlook and Future Prospects |
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250 | (3) |
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253 | (2) |
| Nomenclatures |
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255 | (4) |
| Index |
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259 | |
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. Quan Liu is currently chair professor at the School of Information Engineering at Wuhan University of Technology. In the past five years, she authored more than 100 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. Wei Meng is currently a lecturer at the School of Information Engineering, Wuhan University of Technology. His research interests include robot-assisted rehabilitation, humanrobot interaction, and iterative learning control. He has co-authored five books, published more than 80 academic journal and conference papers, and holds 22 patents. Sheng Quan Xie is currently chair professor in robotics and autonomous systems at the Faculty of Engineering, University of Leeds. He has published seven 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. Professor 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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