Asyraf Hakimi Abu Bakar
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Preferred name
Asyraf Hakimi Abu Bakar
Official Name
Asyraf Hakimi, Abu Bakar
Alternative Name
Abu Bakar, Asyraf Hakimi
Abu Bakar, A. H.
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Scopus Author ID
57212349544
Researcher ID
HOH-0993-2023

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  • Publication
    Classification of Electromyography Signal from Residual Limb of Hand Amputees
    ( 2022-01-01)
    Ahmad Nasrul Norali
    ;
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Al-Mahdi Y.S.M.
    ;
    Fook Chong Yen
    ;
    Asyraf Hakimi Abu Bakar
    Several researchers had worked on collecting electromyography (EMG) signal from amputees and come out with dataset that could be utilized for study in EMG signal processing and classification for decoding of amputee movement intention. This paper presents the work on classification of EMG signal based on the residual limb of amputees with intuitive hand movement based on interactive exercises. Dataset is obtained from NINAPRO public database website where 11 amputee subjects performed intuitive exercise of 17 hand gestures and EMG signal is acquired from the residual arm. Eight feature extraction methods are performed to obtain the EMG feature which are Mean, Minimum, Median, Skewness, Kurtosis, Approximate Entropy, Fuzzy Entropy and Kolmogorov Complexity. Two classifiers are used for EMG classification which are k-Nearest Neighbour and Ensemble classifier. Results shows average accuracy of 87.65% with Ensemble classifier for classification of movement exercise with all features of EMG is used as input to classifier.
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