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.
Main Affiliation
Scopus Author ID
57212349544
Researcher ID
HOH-0993-2023

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  • Publication
    Development of Real Time Arsenic Heavy Metal Concentration Monitoring System
    ( 2021-11-25)
    Asyraf Hakimi Abu Bakar
    ;
    Hilmi W.N.W.Q.W.
    ;
    Muhammad Nur Aiman Uda
    ;
    Nor Azizah Parmin
    ;
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Ahmad Nasrul Norali
    ;
    Fook C.Y.
    ;
    Rosly S.
    This paper focuses on the ongoing development of real-time monitoring system with implementing the Internet of Things (IoT) element for arsenic heavy metal concentration in paddy field using pH sensor for data collection. The pH sensor will detect the hydrogen ion concentration from the prepared soil put in pot contained with various arsenic concentration. The developed system is then compared with the pH sensor in the market to verify its accuracy and sensitivity. The collected data will be transferred wirelessly into data cloud so that it can store the previous and current reading data. Besides, the system’s function is also to ensure the safeness of paddy plant to be planted with knowing the right amount of arsenic concentration can be passed.
  • Publication
    Electromyography Signal Pattern Recognition for Movement of Shoulder
    ( 2021-11-25)
    Ahmad Nasrul Norali
    ;
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Muhammad Asymawi Mohd Reffin
    ;
    Asyraf Hakimi Abu Bakar
    ;
    Chong Yen Fook
    Pectoralis major and deltoid are two muscles that are associated with the movement of the shoulder. Electromyography (EMG) signal acquired from these two muscles can be used to classify the movement of the shoulder based on pattern recognition. In this paper, an experiment for EMG data collection involves eight healthy male subjects who perform four shoulder movements which are flexion, extension, internal rotation and external rotation. Feature extraction of EMG data is done using root mean square (RMS), variance (VAR) and zero crossing (ZC). For pattern recognition, the classifiers that are used are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). Classification results shows highest accuracy on ZC feature using an SVM classifier with cubic kernel. The study on shoulder movement using EMG of pectoralis and deltoid muscles could be extended on arm amputees based on hypothesis that the EMG signal could be utilized for control of robotic prosthetic arm.
      1
  • 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.
      1
  • Publication
    Development of Real Time Arsenic Heavy Metal Concentration Monitoring System
    ( 2021-11-25)
    Asyraf Hakimi Abu Bakar
    ;
    Hilmi W.N.W.Q.W.
    ;
    Muhammad Nur Aiman Uda
    ;
    Nor Azizah Parmin
    ;
    Anas Mohd Noor
    ;
    Zulkarnay Zakaria
    ;
    Ahmad Nasrul Norali
    ;
    Chong Yen Fook
    ;
    Rosly S.
    This paper focuses on the ongoing development of real-time monitoring system with implementing the Internet of Things (IoT) element for arsenic heavy metal concentration in paddy field using pH sensor for data collection. The pH sensor will detect the hydrogen ion concentration from the prepared soil put in pot contained with various arsenic concentration. The developed system is then compared with the pH sensor in the market to verify its accuracy and sensitivity. The collected data will be transferred wirelessly into data cloud so that it can store the previous and current reading data. Besides, the system’s function is also to ensure the safeness of paddy plant to be planted with knowing the right amount of arsenic concentration can be passed.
      1