Publication:
Classification of hand movements by using artificial neural network

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dc.contributor.authorsBaspinar U., Varol H.S., Yildiz K.
dc.date.accessioned2022-03-15T02:09:33Z
dc.date.accessioned2026-01-11T11:37:43Z
dc.date.available2022-03-15T02:09:33Z
dc.date.issued2012
dc.description.abstractIn this study, a home-made four channel sEMG amplifier circuit was designed for measuring EMG signals. The recorded sEMG signals were filtered with a band pass filter and afterwards wavelet based filtering was applied to remove unwanted noises. As a second step the recorded and denoised signals' features were extracted. For classification of motions 8 time domain and 2 frequency domain features were used. There is no reduction applied to the features for artificial neural network (ANN) classification while the features were reduced in two dimension using by Diffusion Map for fuzzy classification. Lastly, seven different motions were classified by ANN and Gustafson Kessel algorithm. Also, their classification performances were compared. © 2012 IEEE.
dc.identifier.doi10.1109/INISTA.2012.6247014
dc.identifier.isbn9781467314466
dc.identifier.urihttps://hdl.handle.net/11424/247185
dc.language.isoeng
dc.relation.ispartofINISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Network
dc.subjectDimension Reduction
dc.subjectEMG pattern classification
dc.subjectFuzzy Classifier
dc.titleClassification of hand movements by using artificial neural network
dc.typeconferenceObject
dspace.entity.typePublication
oaire.citation.titleINISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications

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