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Human-Machine interfaces in Medical Robotics
- 1st Edition - September 1, 2025
- Authors: Yanpei Huang, Ziwei Wang, Yongkun Zhao, Xiaoxiao Cheng, Wenjie Lai, Nicolas Herzig, Jiatong Jiang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 7 2 3 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 7 2 4 - 2
Human-Machine Interfaces in Medical Robotics presents essential and advanced information on developing intuitive human-machine interfaces (HMI) for robotic surgery and rehabilit… Read more
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Request a sales quoteHuman-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.
- 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
Researchers working on medical robotics, robotic surgery, human-machine interaction, or rehabilitation, Engineers designing medical interfaces, Graduate-level students in medical robotics, mechatronics and biomedical engineering
Part 1: Human-machine interfaces for medical robotics
1. Interfaces for robotic surgery
1.1 Introduction
1.2 Master interfaces in surgical teleoperation system
1.3 Intuitive mapping and manipulation dexterity
1.4 Implementation and evaluation
1.5 Conclusion and vision
References
2. Interfaces for rehabilitation
2.1 Introduction
2.2 Interfaces for upper limb rehabilitation
2.3 Interfaces for lower limb rehabilitation
2.4 Control of rehabilitation interface
2.5 Evaluation of sensorimotor functions
2.6 Conclusion and vision
References
3. Hand-free interface for human augmentation
3.1 Introduction
3.2 Human-machine interfaces using human body redundancy
3.3 Motor commands computation
3.4 Evaluation of hand-free human-machine interface
3.5 Case study: Development of intuitive 4-DoF foot interface controlling endoscope
3.6 Conclusion and vision
References
4: Feedback interface
4.1 Introduction
4.2 Visual feedback
4.3 Tactile sensation
4.4 Other sensory modalities
4.5 Robotic sensory augmentation
4.6 A case study: visuo-haptic sensation in robotic surgery
4.7 Conclusion and vision
References
5: Soft robotic human-machine interface
5.1 Introduction
5.2 Soft robotic interfaces for surgery
5.3 Soft robotic interface for rehabilitation and assistance
5.4 Design and fabrication of soft robotic interface
5.5 Modelling and control of soft robotic interface
5.6 Conclusion and vision
Part 2: Intelligent machine and data-driven approach
6: Data-driven approach and personalized interface
6.1 Introduction
6.2 Human user diversity and interface customization
6.3 Machine learning algorithms
6.4 Evaluation
6.5 Human-centred assistive interface applications
6.6 Conclusion and vision
References
7: Human-robot shared control
7.1 Overview
7.2 Operational characteristics of human and interfaces
7.3 Task-level shared autonomy
7.4 Effort-level shared autonomy
7.5 Conclusion and vision
References
8: Autonomous manipulation
8.1 Overview
8.2 Typical application summary
8.3 Visual servoing in surgery
8.4 Motion planning strategies for rehabilitation
8.5 Performance assessment
8.6 Conclusion and vision Reference
References
Part3: Impact of HMI on human user
9: Human sensorimotor adaptation and control
9.1 Introduction
9.2 Human adaptation to interfaces
9.3 Computational models for motor adaptation and recovery
9.4 Sensory integration and perturbation
9.5 Feedforward and feedback impedance control
9.6 Conclusion and vision
References
10: Human training and learning
10.1 Introduction
10.2 Training methods of motor skills
10.3 Motor learning during training
10.4 Long-term effects of training
10.5 Conclusion and vision
References
1. Interfaces for robotic surgery
1.1 Introduction
1.2 Master interfaces in surgical teleoperation system
1.3 Intuitive mapping and manipulation dexterity
1.4 Implementation and evaluation
1.5 Conclusion and vision
References
2. Interfaces for rehabilitation
2.1 Introduction
2.2 Interfaces for upper limb rehabilitation
2.3 Interfaces for lower limb rehabilitation
2.4 Control of rehabilitation interface
2.5 Evaluation of sensorimotor functions
2.6 Conclusion and vision
References
3. Hand-free interface for human augmentation
3.1 Introduction
3.2 Human-machine interfaces using human body redundancy
3.3 Motor commands computation
3.4 Evaluation of hand-free human-machine interface
3.5 Case study: Development of intuitive 4-DoF foot interface controlling endoscope
3.6 Conclusion and vision
References
4: Feedback interface
4.1 Introduction
4.2 Visual feedback
4.3 Tactile sensation
4.4 Other sensory modalities
4.5 Robotic sensory augmentation
4.6 A case study: visuo-haptic sensation in robotic surgery
4.7 Conclusion and vision
References
5: Soft robotic human-machine interface
5.1 Introduction
5.2 Soft robotic interfaces for surgery
5.3 Soft robotic interface for rehabilitation and assistance
5.4 Design and fabrication of soft robotic interface
5.5 Modelling and control of soft robotic interface
5.6 Conclusion and vision
Part 2: Intelligent machine and data-driven approach
6: Data-driven approach and personalized interface
6.1 Introduction
6.2 Human user diversity and interface customization
6.3 Machine learning algorithms
6.4 Evaluation
6.5 Human-centred assistive interface applications
6.6 Conclusion and vision
References
7: Human-robot shared control
7.1 Overview
7.2 Operational characteristics of human and interfaces
7.3 Task-level shared autonomy
7.4 Effort-level shared autonomy
7.5 Conclusion and vision
References
8: Autonomous manipulation
8.1 Overview
8.2 Typical application summary
8.3 Visual servoing in surgery
8.4 Motion planning strategies for rehabilitation
8.5 Performance assessment
8.6 Conclusion and vision Reference
References
Part3: Impact of HMI on human user
9: Human sensorimotor adaptation and control
9.1 Introduction
9.2 Human adaptation to interfaces
9.3 Computational models for motor adaptation and recovery
9.4 Sensory integration and perturbation
9.5 Feedforward and feedback impedance control
9.6 Conclusion and vision
References
10: Human training and learning
10.1 Introduction
10.2 Training methods of motor skills
10.3 Motor learning during training
10.4 Long-term effects of training
10.5 Conclusion and vision
References
- No. of pages: 330
- Language: English
- Edition: 1
- Published: September 1, 2025
- Imprint: Academic Press
- Paperback ISBN: 9780443137235
- eBook ISBN: 9780443137242
YH
Yanpei Huang
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.
Affiliations and expertise
Post-doctoral Researcher, Imperial College of London, UKZW
Ziwei Wang
Ziwei Wang is a lecturer in Robotics and the Director of Advanced Robotic Teleoperation Lab, at the School of Engineering, Lancaster University, U.K. He received the PhD from the Department of Automation, Tsinghua University, China. During the period of 2020 and 2022, he was a research associate with the Human Robotics Group, the Department of Bioengineering, Imperial College London, U.K. His research interests focus on autonomous robot, fuzzy control, human-robot shared control and computational intelligence for medical applications, aiming at enhancing human sensorimotor capability and overall robotic system performance.
Affiliations and expertise
Lecturer, Lancaster University, UKYZ
Yongkun Zhao
Yongkun Zhao is a PhD candidate at the Faculty of Engineering's Department of Bioengineering, Imperial College London, United Kingdom.
Affiliations and expertise
Faculty of Engineering, Imperial College London, UKXC
Xiaoxiao Cheng
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.
Affiliations and expertise
Post-doctoral Researcher,Imperial College of LondonWL
Wenjie Lai
Wenjie Lai earned her Ph.D. in the field of flexible endoscopic surgical robotics from Nanyang Technological University (NTU), Singapore, in 2021. She completed her undergraduate studies at NTU in 2015, graduating with the first-class honour in Mechatronics under the SM3 scholarship.
Currently, she works as a research fellow at CREATE, NTU, in the Smart Grippers for Soft Robotics (SGSR) program. After completing her Ph.D., Dr. Lai ventured into the industry at Ronovo Surgical, a Shanghai-based startup specializing in laparoscopic robots. There, she immersed herself in the development of performance metrics for robot-assisted Minimally Invasive Surgery (MIS) instruments, gaining valuable insights into real-world challenges and industry demands.
Dr. Lai has made several contributions to academia, with publications in prestigious journals such as TMECH, RAL, and ABME, and presentations at top-tier international conferences like ICRA. She actively serves as a reviewer for journals such as Soft Robotics, TMECH, and TIM, as well as conferences like IROS and Robosoft. She holds multiple patents in the field of sensors, surgical robots, and soft robots, including one granted in both the US and China. Dr. Lai's research interests include surgical robotics, soft robotics, haptic feedback, and sensor development.Affiliations and expertise
Research Fellow at CREATE, Nanyang Technological University (NTU)NH
Nicolas Herzig
Nicolas Herzig is currently a Lecturer in Industrial Automation, Mechatronics and Control Engineering in the School of Engineering and Informatics, within the Department of Engineering and Design, and he is a member of the Robotics and Mechatronics Systems Research Group. He received the PhD degree in Control Engineering from the Institut National des Sciences Appliquées (INSA) de Lyon, Université de Lyon, Lyon, France in 2016. He also holds an M.Eng. degree in Mechanical Engineering and an M.Sc. in Mechatronics from Polytech Annecy Chambéry, Annecy, France. From 2016 to 2020, He have been working on two EPSRC-funded projects for medical robotics at The University of Sheffield, Imperial College London, and King's College London. Before his PhD degree, He worked for 18 months for 2 French start-ups where he used to design Mechatronic solutions for industrial applications. His research interests are focused on the development of new technologies for Robotic Systems. He is particularly interested in Mechatronics Design and Control Engineering for compliant and soft robots. He aims to address the scientific challenges that limit Human-Robot or Robot-Environment interactions. His research is applied in various several fields such as industrial robotics, biomedical, and nuclear decommissioning.
Affiliations and expertise
Lecturer in Industrial Automation, Mechatronics and Control Engineering in the School of Engineering and Informatics, within the Department of Engineering and Design, University of SussexJJ
Jiatong Jiang
Jiatong Jiang is with the Department of Bioengineering, Faculty of Engineering, Imperial College London, London SW7 2AZ, United Kingdom. Jiatong Jiang received the B.A. and M.Eng. degrees in electrical and information engineering and bioengineering from the University of Cambridge in 2020. She then received an M.Sc. degree in neuroscience from King's College London in 2021. She has worked as an undergraduate research assistant in the Cavendish Laboratory in summer 2019. Between 2022 and 2023, She worked as a research assistant in the Department of Bioengineering, Imperial College London, London, United Kingdom. She has also worked as a visiting research engineer at OT Bioelettonica, Italy between May and June 2024. She is currently pursuing a Ph.D. degree in Bioengineering from Imperial College London between 2024 and 2028, focusing on closed-loop neurostimulation for neurorehabilitation.
Affiliations and expertise
Department of Bioengineering, Faculty of Engineering, Imperial College London, London SW7 2AZ, United Kingdom