Adaptation to random and systematic errors: Comparison of amputee and non-amputee control interfaces with varying levels of process noise

Abstract

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.

Two Criteria for Good Measurements in Research: Validity and Reliability

“…Reva E. Johnson, Konrad P. Kording, Levi J. Hargrove, and Jonathon W. Sensinger have analyzed in some detail the systematic and random errors that often arise [Johnson et al, 2017]. Christopher R. Madan and Elizabeth A. Kensinger have examined the test-retest reliability of several measures of brain morphology [Madan et al, 2017].…”

“…If both systematic error and random error are zero then the research is considered as valid [Bajpai & Bajpai, 2014]. To minimize overall error, random errors should be ignored, whereas systematic errors should result in adaptation of the movement [Johnson et al, 2017]. …”

Evaluating Internal Model Strength and Performance of Myoelectric Prosthesis Control Strategies

“…Quantifying this internal model enables the development of better control strategies by identifying which control strategies promote better understanding of the system and may therefore lead to better long-term performance. In a recent study [20], researchers investigated the effect of using two different myoelectric control strategies on user adaptation, which is one of the facets that can be used to estimate the strength of an internal model [21], [22]. Their results showed promising evidence that inherent feedback in myoelectric control strategies influences adaptation, which should in turn influence the user's corresponding internal model.…”

Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

“…• Adaptation rate was extracted by computing the rate of feedforward modification of the control signal from one trial to the next 37,38 . The first 100 -230 msec window of activations that started on the mark of the visual rendering on the screen for each trial was used to ensure that only the subject's feedforward intent was captured and to avoid the effect of feedback synchronization.…”