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Reducing Motor Variability Enhances Myoelectric Control Robustness Across Untrained Limb Positions

Lookup NU author(s): Simon Stuttaford, Dr Matthew DysonORCiD, Professor Kianoush Nazarpour, Dr Sigrid DupanORCiD


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AuthorsThe limb position effect is a multi-faceted problem, associated with decreased upper-limb prosthesis control acuity following a change in arm position. Factors contributing to this problem can arise from distinct environmental or physiological sources. Despite their differences in origin, the effect of each factor manifests similarly as increased input data variability. This variability can cause incorrect decoding of user intent. Previous research has attempted to address this by better capturing input data variability with data abundance. In this paper, we take an alternative approach and investigate the effect of reducing trial-to-trial variability by improving the consistency of muscle activity through user training. Participants underwent 4 days of myoelectric training with either concurrent or delayed feedback in a single arm position. At the end of training participants experienced a zero-feedback retention test in multiple limb positions. In doing so, we tested how well the skill learned in a single limb position generalized to untrained positions. We found that delayed feedback training led to more consistent muscle activity across both the trained and untrained limb positions. Analysis of patterns of activations in the delayed feedback group suggest a structured change in muscle activity occurs across arm positions. Our results demonstrate that myoelectric user-training can lead to the retention of motor skills that bring about more robust decoding across untrained limb positions. This work highlights the importance of reducing motor variability with practice, prior to examining the underlying structure of muscle changes associated with limb position.

Publication metadata

Author(s): Stuttaford SA, Dyson M, Nazarpour K, Dupan SSG

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering

Year: 2023

Volume: 32

Pages: 23-32

Online publication date: 15/12/2023

Acceptance date: 02/04/2018

ISSN (print): 1534-4320

ISSN (electronic): 1558-0210

Publisher: Institute of Electrical and Electronics Engineers Inc.


DOI: 10.1109/TNSRE.2023.3343621

PubMed id: 38100346


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