Bio-inspired motor control strategies for redundant and flexible manipulator with application to tooling tasks [electronic resource]

• 作者： Phan, Gia Hoang.
• 其他作者：
  • Solanki, Vijender Kumar.
  • Quang, Nguyen Ho.
  • SpringerLink (Online service)
• 其他題名：
  • SpringerBriefs in applied sciences and technology.
• 出版： Singapore : Springer Singapore :Imprint: Springer
• 叢書名： SpringerBriefs in applied sciences and technology. Computational intelligence
• 主題： Robotics in medicine. , Robots--Control systems. , Control, Robotics, Automation. , Computational Intelligence. , Robotic Engineering.
• ISBN： 9789811695513 (electronic bk.) 、 9789811695506 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Chapter 1. Bio - inspired Motor Control Strategies for Redundant Manipulators -- Chapter 2. Bio - inspired Motor Control Strategies: Cable - driven Manipulator using Agonist - Antagonis actuation -- Chapter 3. Design and Control of 2D-Plotter Planar Parallel Robot -- Chapter 4. Combining 3D motion tracker with IMU sensor signals of muscles to discover macro- and micro-vibration for Stroke Rehabilitation -- Chapter 5. A Pneumatic Actuator-Powered Robotic Glove for Hand Rehabilitation -- Chapter 6. Artificial Intelligence in Rehabilitation Evaluation Based Robotic Exoskeletons: A Review.
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• 摘要註: This book presents a multi-disciplinary view of all aspects of rehabilitation robotics and non-invasive surgery, ideal for anyone new to the field. It includes perspectives from both engineers and clinicians. For skilled researchers and clinicians, it also summarizes current robot technologies and their application to various pathologies. The book will help the readers to develop the know-how and expertise necessary to guide those seeking a comprehensive understanding of this topic through their use of several commercial devices for robotic rehabilitation. The book targets the implementation of efficient robot strategies to facilitate the re-acquisition of motor skills. This technology incorporates the outcomes of behavioral studies on motor learning and its neural correlates into the design, implementation, and validation of robot agents that behave as optimal trainers, efficiently exploiting the structure and plasticity of the human sensorimotor systems.
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