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Related Experiment Video

Updated: Apr 22, 2026

Force and Position Control in Humans - The Role of Augmented Feedback

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

Does EMG control lead to distinct motor adaptation?

Reva E Johnson¹, Konrad P Kording², Levi J Hargrove³

¹Center for Bionic Medicine, Rehabilitation Institute of Chicago Chicago, IL, USA ; Department of Biomedical Engineering, Northwestern University Evanston, IL, USA.

Frontiers in Neuroscience

|October 18, 2014

Summary

Bayesian models accurately describe how the brain adapts motor commands for powered prostheses controlled by electromyographic (EMG) signals. Increased errors and reduced visual uncertainty accelerate adaptation, regardless of the control interface used.

Keywords:

EMG motor adaptation prosthesis control sensory feedback uncertainty

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Area of Science:

Neuroscience
Robotics
Biomechanics

Background:

Electromyographic (EMG) signals control powered prostheses, but introduce uncertainty impacting user adaptation.
Bayesian theories suggest uncertainty influences motor command adaptation to errors.
Understanding adaptation is crucial for effective prosthetic device interaction and control.

Purpose of the Study:

To validate Bayesian models for describing motor adaptation in EMG-controlled prostheses.
To investigate the effects of control interface and visual uncertainty on adaptation.
To inform sensory feedback strategies for powered prostheses.

Main Methods:

Subjects performed a target-directed task using joint angle, joint torque, and EMG control interfaces.
Random visual perturbations were introduced to simulate task uncertainty.
Trial-by-trial adaptation was analyzed across different control interfaces and uncertainty levels.

Main Results:

Adaptation speed increased with higher errors and decreased visual uncertainty, aligning with Bayesian predictions.
The control interface did not significantly affect adaptation, beyond influencing error magnitude.
Bayesian models effectively describe motor adaptation in this context.

Conclusions:

Bayesian models are valuable for understanding prosthesis control and user uncertainty.
Further research can characterize prosthesis user uncertainty to guide feedback strategies.
Improved understanding of movement uncertainty can enhance powered prosthesis control and user experience.