Abstract Myoelectric Control with EMG Drive Estimated using Linear, Kurtosis and Bayesian Filtering

Matthew Dyson1, Jessica Barnes1, Kianoush Nazarpour1

  • 1Newcastle University

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Contributed Papers

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11:30 - 13:30 | Fri 26 May | Emerald III, Rose, Narcissus & Jasmine | FrPS1T1

Poster I

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Abstract

Three muscle activation estimators: a linear mean-absolute value filter, a recursive Bayesian method, and a kurtosis filter were compared as control approaches for an abstract myoelectric-controlled interface. The linear filter out performed both the Bayesian and kurtosis methods with respect to participants' overall scores. Despite significantly less efficient trajectories, the Bayesian filter showed a reduction in the time required to reach individual targets. Results demonstrate both that linear methods can outperform more complex filtering techniques, and that real-time kurtosis may be used as an activation estimator.

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