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首页 >> 新闻公告 >> 学术报告 >> 12月20日(周三)下午4:00新加坡国立大学喻豪勇教授学术报告—A Novel Robotic System for Gait Rehabilitation
12月20日(周三)下午4:00新加坡国立大学喻豪勇教授学术报告—A Novel Robotic System for Gait Rehabilitation
2017-12-18 12:05

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报告题目:A Novel Robotic System for Gait Rehabilitation

人:喻豪勇教授

    位:新加坡国立大学

    间:20171220日周三下午4:00

    点:中心楼二楼教育部重点实验室会议室

邀请人/主持人:李世华 教授

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欢迎各位老师和研究生参加!

 

报告摘要:

Gait disability is common for survivors of stroke or patients with neurological conditions such as spinal cord injuries, traumatic brain injuries and Parkinson’s disease. Robotic assisted gait rehabilitation has the potentials to reduce labor intensity for therapists, provide stimulating biofeedback to patients, provide quantitative measures to clinicians, and ultimately better functional outcome than conventional therapies. However, these potentials have far from being achieved with current complex and expensive robotic systems. Constraining the patients to one plane and on the treadmill is the key limitation of most current systems. It is believed that most effective gait training can only be achieved in trainings resembling the most natural over-ground walking, which will best facilitate brain plasticity and motor learning and motor skill retention.

 

We are developing a novel intelligent robotic platform that enables patients to perform over-ground gait training at home or community rehab centers. The system consists of an omni-directional mobility platform, an active body weight support (BWS) unit, and a pelvic and trunk support and assist module. The omni-directional motion coupled with the pelvic support allows unrestricted natural trunk posture and pelvic motion. The adaptive shared controller enables several control modes depending on the patient condition. The system can provide stability, balance, and gait training. It can also provide perturbation, resistance, and error augmentation training methods to enhance training efficacy. A set of inertia measurement unit (IMU) sensors is used to measure the gait kinematics and provide quantitative measures of gait recovery. Surface electromyogram (EMG) sensors are used to monitor muscle condition and activation pattern. A functional electrical stimulation module can also be implemented on the system to provide simulations for patients with severe drop foot to enhance gait recovery.  In this talk, we will discuss the design, modeling, control and experimental results of the system.

 

报告人简介:

Dr. Yu Haoyong is an Associate Professor of the Department of Biomedical Engineering at the National University of Singapore. He received his Bachelor’s Degree and Master’s Degree from Shanghai Jiao Tong University and PhD from Massachusetts institute of Technology (MIT). He was the Principal Member of Technical Staff in DSO National Laboratories of Singapore before he joined NUS 2010. He is a Principal Investigator of the Singapore Institute of Neurotechnology (SiNAPSE) and the Advanced Robotic Center of NUS (ARC) of NUS. His current research interests include biomedical robotics and devices, rehabilitation engineering and assistive technology, biologically inspired robotics, intelligent control and machine learning. http://www.bioeng.nus.edu.sg/biorob/

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