Improving Learning Performance in a Reaching Task by Real-time Adaptation of Augmented Error Feedback Rules

Improving Learning Performance in a Reaching Task by Real-time Adaptation of Augmented Error Feedback Rules

Elzinga, Maurits (TU Delft Mechanical, Maritime and Materials Engineering)

Schouten, Alfred (mentor)
Mugge, Winfred (mentor)
Spronck, Jo (graduation committee)

Delft University of Technology

2018-12-17

Neurologically impaired patients can regain motor function by engaging in rehabilita- tion. Currently there is no conclusive evidence that robotic rehabilitation has better clinical results than conventional rehabilitation but robotic rehabil- itation has the potential to increase efficiency and patient motivation, justifying improvement of reha- bilitation robotics. A variety of approaches to de- sign the interaction between a robotic trainer and the patient is used. For the purpose of improving rehabilitation robotics, a new adaptive algorithm is proposed. Assistive algorithms seem suitable for training impaired patients but prove difficult to val- idate with healthy subjects. For healthy subjects, error augmentation is shown to be more effective for learning. In an experiment, 13 healthy subjects performed a reaching task while strapped to an up- per extremity exoskeleton. During this task they were subject to an adaptive augmented error feed- back controller. Subjects were divided into three groups in which one of each, or both the follow- ing parameters of a force field were adapted: dead band width and divergent force field strength. Per- formance was measured as amount of deviation from a straight line between two targets. Adapt- ing dead band width results in a better movement performance than adapting force field strength (p = 0.0069). Adapting force field strength in addition to adapting dead band width did not improve move- ment performance (p = 0.9960). It is concluded that an adaptive augmented error feedback mechanism can improve movement performance in a reaching task with healthy subjects.

Motor Learning
Adaptive control
Rehabilitation
Exoskeleton

http://resolver.tudelft.nl/uuid:c1b68302-79d7-4dee-9821-5256aa0bbbc4

Student theses

master thesis

© 2018 Maurits Elzinga