TY - JOUR
T1 - Adaptation to random and systematic errors
T2 - Comparison of amputee and non-amputee control interfaces with varying levels of process noise
AU - Johnson, Reva E.
AU - Kording, Konrad P.
AU - Hargrove, Levi J.
AU - Sensinger, Jonathon W.
N1 - Funding Information:
This work was funded by the National Institutes of Health and the National Institute of Child Health and Human Development through the Ruth L. Kirschstein National Research Service Award (REJ), and by the National Science Foundation through the National Robotics Initiative (grant number 1317379, JWS, KPK, LJH). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PY - 2017/3
Y1 - 2017/3
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0170473
DO - 10.1371/journal.pone.0170473
M3 - Article
C2 - 28301512
AN - SCOPUS:85015429338
VL - 12
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 3
M1 - e0170473
ER -