Introducation:
Advances in lower-limb rehabilitation robotics have demonstrated significant potential in improving mobility and recovery for individuals with physical impairments, such as those caused by stroke, osteoarthritis, or post-surgery recovery. One of the key challenges in this domain is the development of effective human-robot collaboration control mechanisms, where the robotic system works seamlessly with the human user, adapting to their movements and providing personalized assistance. This collaboration is crucial for ensuring the robot can respond dynamically to a patient’s unique movement patterns, muscle strength, and progress in rehabilitation.
This special session aims to explore the intersection of rehabilitation robotics and gait analysis, specifically in the context of knee joint angle estimation and real-time gait recognition. We will focus on human-robot collaborative control systems that enable intuitive interaction between the robot and the patient, ensuring that the system adapts in real-time to provide optimal support. Key topics of interest include:
Human-robot interaction models that integrate biomechanical feedback from wearables and sensors (such as sEMG, IMUs) to adjust robot behavior in real-time.
Collaborative control strategies that balance robot autonomy with human intent and movement, improving the effectiveness and safety of rehabilitation.
Personalized gait rehabilitation: Leveraging machine learning algorithms to tailor rehabilitation protocols based on individual patient data, including knee joint angles and movement strategies.
Clinical validation and real-world implementation: Addressing the challenges of deploying these systems in real clinical settings, including sensor noise, long-term use, and patient-specific variability.
By emphasizing the collaborative control between humans and robots, this session will provide insights into the next generation of rehabilitation systems that not only assist but also learn from the user’s movements, creating a more personalized and effective rehabilitation process.
Organizer(s):
Aihui Wang, Zhonguyan University of Technology, China
Professor Aihui Wang received the PhD degrees from Tokyo University of Agriculture and Technology in Japan, 2012. He then worked as a postdoc at Bournemouth University in UK between 2014 and 2015. He joined Zhongyuan University of Technology in 2004 as a lecturer, and was then promoted to an associate professor in December 2013, and to a Professor in December, 2021. His research interests include biomimetic robot control, micro nano drive control, and complex nonlinear control.
Gui-Bin Bian, Institute of Automation, Chinese Academy of Sciences, China
Professor Gui-Bin Bian received a B.E. degree in mechanical engineering from the North China University of Technology, Beijing, China, in 2004, and the M.E. and Ph.D. degrees in mechanical engineering from Beijing Institute of Technology, Beijing, in 2007 and 2010, respectively. He is a Professor with the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing. His research interests include medical robotics and human-robot interaction.
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