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Online mapping of EMG signals into kinematics by autoencoding

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dc.contributor.authorVujaklija, Ivan
dc.contributor.authorShalchyan, Vahid
dc.contributor.authorKamavuako, Ernest Nlandu
dc.contributor.authorJiang, Ning
dc.contributor.authorMarateb, Hamid R.
dc.contributor.authorFarina, Dario
dc.date.accessioned2018-04-03T18:23:06Z
dc.date.available2018-04-03T18:23:06Z
dc.date.issued2018-03-13
dc.descriptionEuropean Union’s Horizon 2020 research and innovation program || 687795 (project INPUT)en
dc.description.abstractBackground: In this paper, we propose a nonlinear minimally supervised method based on autoencoding (AEN) of EMG for myocontrol. The proposed method was tested against the state-of-the-art (SOA) control scheme using a Fitts’ law approach. Methods: Seven able-bodied subjects performed a series of target acquisition myoelectric control tasks using the AEN and SOA algorithms for controlling two degrees-of-freedom (radial/ulnar deviation and flexion/extension of the wrist), and their online performance was characterized by six metrics. Results: Both methods allowed a completion rate close to 100%, however AEN outperformed SOA for all other performance metrics, e.g. it allowed to perform the tasks on average in half the time with respect to SOA. Moreover, the amount of information transferred by the proposed method in bit/s was nearly twice the throughput of SOA. Conclusions: These results show that autoencoders can map EMG signals into kinematics with the potential of providing intuitive and dexterous control of artificial limbs for amputees.en
dc.identifier.urihttps://doi.org/10.1186/s12984-018-0363-1
dc.identifier.urihttp://hdl.handle.net/10012/13074
dc.language.isoenen
dc.publisherBiomed Centralen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAutoencodingen
dc.subjectMyoelectric signal processingen
dc.subjectOnline performanceen
dc.subjectProsthetic controlen
dc.subjectRegressionen
dc.titleOnline mapping of EMG signals into kinematics by autoencodingen
dc.typeArticleen
dcterms.bibliographicCitationVujaklija, I., Shalchyan, V., Kamavuako, E. N., Jiang, N., Marateb, H. R., & Farina, D. (2018). Online mapping of EMG signals into kinematics by autoencoding. Journal of NeuroEngineering and Rehabilitation, 15, 21. https://doi.org/10.1186/s12984-018-0363-1en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Systems Design Engineeringen
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.typeOfResourceTexten

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