Talk by David Hofmann: Bayesian Filter for Myoelectric Signal Amplitude Estimation

David Hofmann from the Max Planck Institute for Dynamics and Self-Organization, Göttingen, will give a lecture entitled “Bayesian filter for myoelectric signal amplitude estimation”

Tuesday 20th May, at 12:00pm
Goldsmiths College, Ben Pimlott Building, Lecture Theatre

Abstract

Myoelectric signals are important for a variety of medical applications. Besides this myoelectric signals find application also in game industry and art.
A central feature of myoelectric signals that is important to all those fields is its amplitude. The amplitude of a myoelectric signal is thought to represent the contraction force of the respective muscle. We present different ways of amplitude estimation and show the superiority of a specific Bayesian filter with respect to common estimators. We provide evidence that the control of a hand prosthesis gains significantly from amplitude estimation based on this Bayesian filter.
Finally we discuss the sonification of myoelectric signals as a further possible application of the Bayesian filter.

Bio

David Hofmann is from Italy and studied physics at the Technische Universität München. Afterwards he moved to Göttingen and started a PhD in computational neuroscience at the Max Planck Institute for Dynamics and Self-Organization. His PhD thesis that he completed recently is about the improvement of hand prostheses controllers that are based on myoelectric signals.