Human-Machine interfaces in Medical Robotics

Human-Machine Interfaces in Medical Robotics presents essential and advanced information on developing intuitive human-machine interfaces (HMI) for robotic surgery and rehabilitation. This book provides extensive coverage of multidisciplinary information needed to develop efficient HMI, discussing core technologies of the field, including hand-free control strategies, sensory feedback, data-driven approaches, human-robot shared control, autonomous control, human motor adaption, training, and learning. Arranged in three parts, including interfaces in medical robotics, intelligent machines, and human users, this book provides potential solutions to open questions like what the optimal interface and efficient interaction mode is to facilitate a surgeon’s operation, a patient’s motor control, or human augmentation.

Part 1: Human-machine interfaces for medical robotics 1. Interfaces for robotic surgery 2.?Interfaces for rehabilitation 3.?Hand-free interface for human augmentation 4.?Feedback interface 5. Soft robotic human-machine interface Part 2: Intelligent machine and data-driven approach 6. Data-driven approach and personalized interface 7. Human-robot shared control 8. Autonomous manipulation Part3: Impact of HMI on human user 9. Human sensorimotor adaptation and control 10. Human training and learning

Yanpei Huang is a lecturer at the Department of Engineering and Design, University of Sussex, UK. Before joining Sussex, she was a post-doctoral researcher in the Human Robotics Group, at the Department of Bioengineering, Imperial College London, U.K, where she investigated movement augmentation strategies in Virtual Reality. Yanpei Huang completed her Ph.D. study at Nanyang Technological University, Singapore, with a focus on the development of intuitive human-machine interfaces for robotic surgery. Prior to the Ph.D. study, she received the M.Sc. degree in Manufacturing Systems & Engineering from Nanyang Technological University, Singapore. Her current research interests include human–machine interaction and medical robotics.

Xiaoxiao Cheng is a Lecturer in Engineering Systems for Robotics at the University of Manchester. Before joining in the University of Manchester, he worked as a Research Associate at Imperial College London from 2020 to 2023 and a Research Fellow at Stanford University from 2019 to 2020. He received his Ph.D. degree in Electrical and Electronic Engineering from The University of Melbourne in 2019, M. Phil. degree in Mechanical Engineering from Tsinghua University in 2014, and B. Eng. degree in Mechanical Engineering from Beijing Institute of Technology in 2011. His research focuses on developing intelligent autonomous systems and human-machine interfaces by considering and integrating factors from robotics, control, artificial intelligence, and neuroscience.

• Provides a comprehensive review of human-machine interfaces in the medical robotics field and summarizes their core design principles, methods and evaluations
• Presents updated technology coverage in multiple disciplines
• Explores how human users adapt to and learn efficiently from the interface and how machines become more intelligent to assist humans