Date(s) - 25 Jan 2018
1:30 pm – 3:30 pm
There is increasing interest in using robotic devices to provide rehabilitation therapy following stroke. Robotic guidance is generally used in motor training to reduce performance errors while practicing. However, up to date, the functional gains obtained after robotic rehabilitation are limited. A possible explanation for this limited benefit is the inability of the controllers to adapt to the subjects’ special needs. Research on motor learning has emphasized that movement errors are fundamental signals that drive motor adaptation. Thereby, robotic algorithms that augment errors rather than decrease them have a great potential to provoke better motor learning and neurorehabilitation outcomes, especially in initially more skilled subjects.
Although there is an initial body of work that compared the effectiveness of robotic strategies that amplify or reduce movement errors on motor learning, results are still inconclusive. A possible rationale is that studies have searched for the strategy that enhances learning, independently of the subjects’ individual needs and the characteristics of the task to be learned. Some theories have suggested that optimal learning is achieved when the difficulty of the task is appropriate for the individual subject’s level of expertise. Additionally, the specific characteristics of the task to be learned might play an important role on motor learning outcomes.
In this talk I will present my experience and results from motor learning experiment performed with different robotic training strategies that reduce or amplify movement errors, as well as other robotic strategies, such as random-evoked destabilizing forces. I will discuss the role of subjects’ initial skill level and the motor task characteristics on the effectiveness of the different robotic training strategies. Finally, the possibility of using these novel strategies in robotic neurorehabilitation will be further discussed.
About the speaker:
Laura Marchal-Crespo is an Assistant Professor at the ARTORG Center for Biomedical Engineering Research, University of Bern. She is also affiliated with the Sensory-Motor Systems at the Department of Health Sciences and Technology, ETH Zurich. Laura Marchal-Crespo obtained her M.Sc. and Ph.D degrees from the University of California at Irvine, USA, in 2006 and 2009, respectively. In 2010 she joined the Sensory-Motor Systems, ETH Zurich, as a postdoc researcher. In 2017 she obtained a Swiss National Science Foundation (SNSF) Professorship and joined the ARTORG Center for Biomedical Engineering Research as medical faculty. She carries out research in the general areas of human-machine interfaces and biological learning, and, specifically, in the use of robotic assistance and virtual reality to aid people in learning motor tasks and rehabilitate after neurologic injuries.
Location: EPFL, Room ME D2 1124