Zulkarnay Zakaria
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Zulkarnay Zakaria
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Zulkarnay, Zakaria
Alternative Name
Zakaria, Z. N.
Zakaria, Zulkarnay
Zakaria, Z.
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Scopus Author ID
24403085300
Researcher ID
F-5218-2010

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A comprehensive review of the recent developments in wearable Sweat-Sensing Devices

2022-10-01 , Nur Fatin Adini Ibrahim , Norhayati Sabani , Shazlina Johari , Asrulnizam Abd Manaf , Asnida Abdul Wahab , Zulkarnay Zakaria , Anas Mohd Noor

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte’s response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance.

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An Overview of Medical Applications in Meningitis Detection

2020-07-09 , Abdulrahman Ahmed A. , Zulkarnay Zakaria , Hamood Ali M. , Pusppanathan J. , Zarina Mohd Mhji S. , Anas Mohd Noor , Mohd Hafiz Fazalul Rahiman , Muhamad Khairul Ali Hassan , Muhammad Juhairi Aziz Safar , Ahmad Faizal Salleh

Meningitis remains one of the common infections among young children with high morbidity and mortality rates. In Southeast Asian, only few studies were reported published which evaluated meningitis clinically in the last two decades. Similarly, few studies in Malaysia evaluated meningitis among adolescents and children. Globally, more than one million cases with 135,000 deaths has been recorded yearly, and in Malaysia, severe neurological complications occurs in 9-25% of cases which affirms the most serious risk manifests from bacterial meningitis. Therefore, early detection and effective treatment are required before the irreversible damages occur. This paper reviews the current states and perspectives of diagnostic techniques on meningitis detection. Currently, there are three diagnostic techniques available for meningitis detection, such as blood cultures, spinal tap (lumbar puncture), and imaging techniques (CT scan, MRI, EIT, Ultrasonography, Nuclear imaging and X-ray). However, these techniques have limitations that may limit the chances of carrying out the early detection of the disease. The essence of this review is that meningitis requires an effective technique that is capable of carrying out the early detection of the disease by differentiating normal people and Meningitis infected patients so as to promote longevity worldwide. In this review magnetic induction tomography (MIT) technique is proposed to diagnose meningitis earlier as it is non-intrusive, non-invasive, contactless, and electrode-less imaging technique which does not expose the patients to a harmful radiation.

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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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An Open-Source, Miniature UV to NIR Spectrophotometer for measuring the transmittance of liquid materials

2022-01-01 , Anas Mohd Noor , Ahmad Nasrul Norali , Zulkarnay Zakaria , Fook Chong Yen , Basri Noor Cahyadi

The primary disadvantages of commercial spectrophotometers are expensive, heavy, and not portable. Furthermore, conventional instruments are only suitable to be used in a specialized laboratory. Even though some commercially available small-size instruments or devices are available, the price is still high. Therefore, a low-cost device is necessary without sacrificing accuracy and sensitivity. In this work, a low-cost, configurable, open-source and accurate portable spectrophotometer device was developed for education and laboratory analytical use. Commercially available photodetector is utilized as main component of the device due to broad spectral range from ultraviolet to near infra-red. The device performs well over a wide range of spectral wavelengths with small errors. We presume that the use of this work can offer a alternative for affordable and accurate device that is comparable to the commercially available products which also suitable for many applications.

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Classification of White Blood Cells Based on Surf Feature

2021-01-01 , Anas Mohd Noor , Zulkarnay Zakaria , Aishah Mohd Noor , Ahmad Nasrul Norali

Conventional blood analysis using blood smear image were performed manually by experts in hematology is tedious and highly depending on the level of experience. Currently, computer-assist technology is developed to reduce the time-consuming process and improved accuracy. As an example, various image processing techniques used to quantify such as white blood cells (WBCs) morphological conditions or classification in the blood smear image, which assist experts in developing confidence decision making in the analysis of cells conditions linked to the specific diseases. However, the WBCs shape features are arbitrary than the red blood cells (RBCs) because of the maturation state, cell orientations or positions, cell color variations, and the quality of the image captured influences the performance of classification accuracy. Therefore, we proposed a scale and rotation invariance feature for WBCs classification using speed up robust feature (SURF). SURF is suitable to be applied in identifying objects even though the orientation, scale, and position are varying, such as WBCs in microscopic blood smear images. We analyzed the classification performances using a support vector machine (SVM) and an artificial neural network (ANN) of WBCs types in the microscopic image based on the cell nucleus. The results show that the purposed SURF feature method has an excellent performance of accuracy for both methods and suitable to be utilized for the application of cell types classification.

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Muscle Fatigue Assessment Using Multi-sensing Based on Electrical, Mechanical and Acoustic Properties

2021-01-01 , Anas Mohd Noor , Zulkarnay Zakaria , Ahmad Nasrul Norali

This paper shows that a multi-sensing technique using electromyogram (EMG), mechanomyogram (MMG), and acousticmyogram (AMG) used to monitor the status of rectus femoris muscle over three states; minimal stress, moderate fatigue, and severe muscle fatigue. Test subjects need to do the designed exercise protocol to simulate these state conditions. The sensors are located at the rectus femoris muscle, and signals were recorded simultaneously. Analysis of signals is based on root mean square (RMS), mean power frequency (MPF), and power spectral density (PSD) plot were compared between the muscle state conditions. Results show that the RMS values of the muscle are increased as the contraction occurs, and the MPF signal is decreased for all sensing properties. On the other hand, the frequency signal is shifted to the left in the PSD plot as the muscle undergoes fatigued for all sensors. In conclusion, multi-sensing using EMG, MMG, and AMG are useful for assessing muscle fatigue condition. It also provide advantages over the single-measurement muscle assessment method.

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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.

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Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection by Using COMSOL Multiphysics

2021-11-25 , Aiman Abdulrahman Ahmed , Zulkarnay Zakaria , Ali M.H. , Anas Mohd Noor , Siti Fatimah Abdul Halim , Ahmad Nasrul Norali , Pusppanathan J. , Rahim R.A.

Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.

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Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes

2020-06-11 , Anas Mohd Noor , Haniza Yazid , Zulkarnay Zakaria , Aishah Mohd Noor

Researchers developed various methods and algorithms to classify white blood cells (WBCs) from blood smear images to assist hematologists and to develop an automatic system. Furthermore, the pathological and hematological conditions of WBCs are related to diseases that can be analyzed accurately in a short time. In this work, we proposed a simple technique for WBC classification from a peripheral blood smear image based on the types of cell nuclei. The developed algorithms utilized a histogram of oriented gradient (HOG) feature typically known for application in human disease detection. The segmentation of WBC nuclei utilizes a YCbCr color space and K-means clustering techniques. The HOG feature contains information about the cell nuclei shapes, which then is classified using a support vector machine (SVM) and backpropagation artificial neural network (ANN). The results show that the proposed HOG feature is useful for WBC classification based on the shapes of nuclei. We are able to categorize the type of a WBC based on its nucleus shape with more than 95% accuracy.

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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.

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