Quantum Neural Network Based Surface EMG Signal Filtering for Control of Robotic Hand

Vaibhav Gandhi, TM McGinnity

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

A filtering methodology inspired by the principles of quantum mechanics and incorporating the well-known Schrodinger wave equation is investigated for the first time for filtering EMG signals. This architecture, referred to as a Recurrent Quantum Neural Network (RQNN) can characterize a non-stationary stochastic signal as time varying wave packets. An unsupervised learning rule allows the RQNN to capture the statistical behaviour of the input signal and facilitates estimation of an EMG signal embedded in noise with unknown characteristics. Results from a number of benchmark tests show that simple signals such as DC, staircase DC and sinusoidal signals embedded with a high level of noise can be accurately filtered. Particle swarm optimization is employed to select RQNN model parameters for filtering simple signals. In this paper, we present the RQNN filtering procedure, using heuristically selected parameters, to be applied to a new thirteen class EMG based finger movement detection system, for emulation in a Shadow Robotics robot hand. It is shown that the RQNN EMG filtering improves the classification performance compared to using only the raw EMG signals, across multiple feature extraction approaches and subjects. Effective control of the robot hand is demonstrated.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Print)ISBN-13: 9781467361293
DOIs
Publication statusPublished (in print/issue) - 4 Aug 2013
EventInternational Joint Conference on Neural Networks, 2013, ISBN-13: 9781467361293; DOI: 10.1109/IJCNN.2013.6706781; - Dallas, Texas, USA
Duration: 4 Aug 2013 → …

Conference

ConferenceInternational Joint Conference on Neural Networks, 2013, ISBN-13: 9781467361293; DOI: 10.1109/IJCNN.2013.6706781;
Period4/08/13 → …

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