Date Published: March 16, 2017
Publisher: Public Library of Science
Author(s): Reva E. Johnson, Konrad P. Kording, Levi J. Hargrove, Jonathon W. Sensinger, Mikhail A. Lebedev.
The objective of this study was to understand how people adapt to errors when using a myoelectric control interface. We compared adaptation across 1) non-amputee subjects using joint angle, joint torque, and myoelectric control interfaces, and 2) amputee subjects using myoelectric control interfaces with residual and intact limbs (five total control interface conditions). We measured trial-by-trial adaptation to self-generated errors and random perturbations during a virtual, single degree-of-freedom task with two levels of feedback uncertainty, and evaluated adaptation by fitting a hierarchical Kalman filter model. We have two main results. First, adaptation to random perturbations was similar across all control interfaces, whereas adaptation to self-generated errors differed. These patterns matched predictions of our model, which was fit to each control interface by changing the process noise parameter that represented system variability. Second, in amputee subjects, we found similar adaptation rates and error levels between residual and intact limbs. These results link prosthesis control to broader areas of motor learning and adaptation and provide a useful model of adaptation with myoelectric control. The model of adaptation will help us understand and solve prosthesis control challenges, such as providing additional sensory feedback.
Reducing movement errors is a fundamental goal of human learning, but is difficult for amputees using electromyographic (EMG) signals to control powered upper limb prostheses . Errors may be either random (caused by unpredictable temporary changes), or systematic (caused by altered or incorrect estimation of task dynamics). To minimize overall error, random errors should be ignored, whereas systematic errors should result in adaptation of the movement [2,3]. Thus when an error occurs, the person needs to decide if, and how much to adapt the next movement. This decision may be especially difficult when using a myoelectric interface, which involves frequent errors, reduced sensory feedback, unfamiliar dynamics, and highly variable control signals. Adapting appropriately to errors is crucial for improving performance; thus, we need to study adaptation during prosthesis use in order to develop tools to help amputees reduce errors and complete tasks skillfully.
We first report the modeling findings to give a broad sense of how different factors are predicted to influence adaptation. We then report our experimental observations of how our subjects adapted to self-generated errors, perturbations, and feedback uncertainty, with modeling comparisons for each.
Prosthesis control involves frequent movement errors, and amputees using powered prostheses often have difficulty reducing these errors. To better understand this difficulty and improve their ability to control their prosthesis, we studied adaptation—the process of adjusting behavior in response to errors. We focused in particular on how subjects distinguish between random errors and systematic errors, because this distinction may be complicated by the high variability of control signals and reduced feedback associated with prosthesis control. We observed how subjects adapted to self-generated errors and random visual perturbations with varying levels of feedback uncertainty. This paradigm, along with theoretical modeling of adaptation, allowed us to separate the effects of feedforward and feedback uncertainty.