A bilateral rehabilitation system assisted by parallel haptic robots for training the upper limb after stroke

Updated::2018-11-21    Font Size:[Larger Smaller]  
 A bilateral rehabilitation system assisted by parallel haptic robots for training the upper limb after stroke


Rehabilitation robots have the potential to provide intensive and accurate long-term rehabilitation continuously to avoid fatigue of the therapist. Several robot-assisted rehabilitation systems have been developed. Most of the developments adopt robots in series that have an anthropomorphic arm structure and a large workspace. However, these robots have some disadvantages, such as the low rigidity inherent to an open structure, the complexity of the mechanical system. The errors accumulate and amplify from link to link, etc. These disadvantages can be avoided by parallel robots. A parallel robot is designed so that each chain is generally short, simple and, therefore, can be rigid against unwanted movements, so that errors in the position of a chain are averaged together with others, instead of being cumulative. Although parallel robots have the disadvantage of having a limited workspace, it is sufficient for training the upper limbs. Studies suggest that coordination and limb synchronization can improve treatment efficacy, it is worth trying the bilateral system design. We designed a bilateral rehabilitation system using two parallel robots of six degrees of freedom. The training exercises are visualized in PC through video games. The robots are also able to provide the virtual forces feedback to the user, such that the patient has a haptic feeling. The new bilateral control scheme provides bilateral elbow, forearm and wrist training through two Stewart platforms that work in master-slave mode. Our bilateral rehabilitation system has the advantage of low cost and 3D movement training.

CV sketch:

Prof. Xiaoou Li obtained B. S degree of applied mathematics in 1991 and PhD degree of Automatic Control in 1995 from Northeastern University, Shenyang, P. R. China. She has been a professor of Computer Science Department, at The Research and Advanced Studies Centre of the National Polytechnic Institute (CINVESTAV-IPN) in Mexico since 2000.  She was a lecturer at Northeastern University from 1995 to 1997; then, she was a postdoc of the National Autonomous University of Mexico (UNAM) from 1998 to 2000. She was a senior research fellow of the School of Electronics, Electrical Engineering & Computer Science in Queen's University Belfast, UK from 2006 to 2007; and a visiting professor at the School of Engineering of the University of California Santa Cruz in 2010. Her research interests include knowledge based system, machine learning and data mining applications, social network analysis, Petri nets, neural networks, system modeling and simulation, human machine interface (HMI), etc. She has published more than 100 papers on international journals, books, and conferences. As PI, she has successfully finished three CONACYT (equivalent to NSF in Mexico) Basic Science projects in the field of Knowledge and Data Engineering, one collaborative project with the University of California Riverside, and several other collaborative research projects. She is an active organizer of IEEE international conferences such as ICNSC, SMC, CASE, etc. She is currently Editor of IEEE Press, and associate editor of IEEE Transactions on Automation Science and Engineering, IEEE Access, IEEE/CAA Journal of Automatica Sinica, and IEEE SMC Magazine. She is a regular member of the AMC (Mexican Association of Science), and a member of SNI (National Researcher System of Mexico) level 2.

Her research interests include machine learning and Big Data applications, social network analysis, rehabilitation robotics, intelligent manufacturing, knowledge based system, Petri nets applications, active database system, etc.

Time: 10:00, Nov. 23, 2018
Location: 218, Logistics Building