Wrist speed feedback improves elbow compensation and reaching accuracy for myoelectric transradial prosthesis users in hybrid virtual reaching task
Artikel i vetenskaplig tidskrift, 2023

Background: Myoelectric prostheses are a popular choice for restoring motor capability following the loss of a limb, but they do not provide direct feedback to the user about the movements of the device—in other words, kinesthesia. The outcomes of studies providing artificial sensory feedback are often influenced by the availability of incidental feedback. When subjects are blindfolded and disconnected from the prosthesis, artificial sensory feedback consistently improves control; however, when subjects wear a prosthesis and can see the task, benefits often deteriorate or become inconsistent. We theorize that providing artificial sensory feedback about prosthesis speed, which cannot be precisely estimated via vision, will improve the learning and control of a myoelectric prosthesis. Methods: In this study, we test a joint-speed feedback system with six transradial amputee subjects to evaluate how it affects myoelectric control and adaptation behavior during a virtual reaching task. Results: Our results showed that joint-speed feedback lowered reaching errors and compensatory movements during steady-state reaches. However, the same feedback provided no improvement when control was perturbed. Conclusions: These outcomes suggest that the benefit of joint speed feedback may be dependent on the complexity of the myoelectric control and the context of the task.

Compensatory movement

Sensory feedback

Motor adaptation

Center-out reaching

Motor learning

Myoelectric prosthesis


Eric Earley

Center for Bionics and Pain Research

Center for Bionic Medicine

Chalmers, Elektroteknik, System- och reglerteknik

Northwestern University

Reva E. Johnson

Valparaiso University

Jonathon W. Sensinger

University of New Brunswick

Institute of Biomedical Engineering

Levi J. Hargrove

Center for Bionic Medicine

Northwestern University

Journal of NeuroEngineering and Rehabilitation

17430003 (eISSN)

Vol. 20 1 9








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