Der Schweizerische Nationalfonds SNF finanziert eine Förderprofessur für Frau Dr. Laura Marchal-Crespo am ARTORG Forschungszentrum für Biomedizinische Technik an der Universität Bern und in der Abteilung für Kognitive und Restorative Rehabilitation der Universitätsklinik für Neurologie am Inselspital.
Laura Marchal-Crespo ist eine international anerkannte Expertin für roboterunterstütztes motorisches Lernen. Sie wird neue Trainingsstrategien für bestehende Therapieroboter entwickeln und klinisch erproben. Bei diesen neuen patientenspezifischen Strategien passt das Gerät die Schwierigkeit des Trainings an die individuellen Bedürfnisse des Patienten an. Dies wird erzielt, indem abhängig von der Bewegungsaufgabe, den Fähigkeiten und dem Alter des Patienten Bewegungsfehler durch den Roboter vergrössert oder verkleinert werden. Laura Machal-Crespo erwartet, dass durch die Verstärkung von Bewegungsfehlern Patienten dazu motiviert werden, ihre Bewegungen weiter zu verbessern.
Recent work in robot-aided training has focused on developing sophisticated robotic mechanisms in order to support training of complex movements, such as walking and multi-joint arm movements. Robotic guidance is generally used in motor training to reduce performance errors while practicing. Although there is recent evidence that robotic guidance training can improve motor function more effectively than conventional therapist-assisted practice, the benefits seem to be limited to patients with severe impairment. A possible explanation for this limited benefit is the inability of the controllers to adapt to the subjects’ special needs. Patient’s effort during physical training is dan important factor in order to provoke motor plasticity; hence robotic devices could potentially decrease recovery if they encourage a decrease in effort, energy consumption, or attention during training. It is commonly accepted in the field of neurorehabilitation that recovery is a form of motor learning, and that studying the learning mechanisms during the acquisition of novel motor skills may provide novel ways to improve neurorehabilitation. 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, especially in initially more skilled subjects. Furthermore, robotic strategies that augment errors are likely to increase effort, energy consumption and attention.
The proposed research aims to develop innovative control algorithms that amplify or create movement errors in order to maximize the training benefits of the already existing rehabilitation robots. Within this project, we will perform a systematic analysis of the relative benefits of these novel controllers on motor learning based on subject’s skill level, age and task’s characteristics, and will compare them to classical robotic guidance, and to non-robotic feedback approaches, such as visual feedback.
Most of the motor learning studies performed to date involved only young healthy subjects. However, neurologic injuries, such as stroke, are more preeminent at older ages. Sensory limitations and cognitive changes are some characteristics associated with subjects’ old age that might limit the effectiveness of robotic training. Thereby, we will examine the effectiveness of the developed training strategies with healthy old adults in order to get a better insight into robotic rehabilitation.
Finally, although recovery after a neural insult can be seen as a particular form of motor skill learning, whether this is applicable to the developed training strategies can nevertheless not be assured. Therefore, we will perform further motor learning studies with neurological patients (i.e. stroke patients). The culmination of my work will consist in the development of a closed-loop robotic trainer, which automatically selects the training strategy that optimizes learning based on the task’s characteristics, subjects’ individual skill (disability) level and age.
Last updated:24.05.2022
