Ahmad Faizal Salleh
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Ahmad Faizal Salleh
Official Name
Ahmad Faizal, Salleh
Alternative Name
Ahmad Faizal, S.
Salleh, A.
Salleh, Ahmad Faizal
Faizal, S. Ahmad
Mohd Salleh, A. F.
Salleh, A. F.
Salleh, Ahmad Faizal Bin
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  • Publication
    Assessments of cognitive state of Mitragyna speciosa (ketum) users during relaxation state
    ( 2023-02-21)
    Fadhilah A.W.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Rashid R.A.
    ;
    Palaniappan R.
    ;
    Mutusamy H.
    ;
    Helmy K.
    The abuse of Mitragyna speciosa or commonly known as ketum leaves is widespread across Asian countries. Ketum leaves that were originally used as medicine were abused for the purpose of deluding their minds. As it has intoxicated properties that similar to drugs, EEG signals of ketum users may differ from normal people as the ketum may alter the brain signal and the cognitive state of ketum users may decrease. Therefore, this study was conducted to assess the cognitive state between ketum users and non-ketum users in terms of their relaxation state by using brain signal characteristics. A total of 8 subjects were involved in the experimental session. The 8 subjects were divided into two groups which are 4 subjects were ketum users for at least one year while the other 4 subjects were non-ketum users, had enough sleep for at least 6 hours and had no mental disorders. The EEG data was recorded during awaken relaxed state and was filtered using a notch filter and Independent Component Analysis (ICA) to remove the powerline artefacts, eye blinking and eye movement. Stockwell Transform was used to reduce the amount of the large data and extract useful features from the signal. Student's t-test is performed in order to compute the percentages of the differences between the ketum users and non-ketum users in each brain lobe. Mean of Shannon Entropy, mean of Tsallis Entropy, and mean of Hurst Exponent features used were able to elucidate the differences in brain activities between the two groups investigated.
  • Publication
    Clinical validation of 3D mesh reconstruction system for spine curvature angle measurement
    ( 2023-02-21)
    Shanyu C.
    ;
    Lim Chee Chin
    ;
    Fook C.Y.
    ;
    Azizan A.F.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Shafriza Nisha Basah
    Spine curvature disorders are scoliosis, lordosis, and kyphosis. These disorders are mainly caused by the bad habits of the person during sitting, standing, and lying. There are about 3 to 5 out of 1,000 people who are affected by spine curvature disorder. The current conventional method used for diagnose this disorder, such as radiography, goniometry and palpation. However, these conventional methods require human skills and can be time-consuming, resulting to exhaustion of logistic. Therefore, there is a need to solve this problem by creating a Graphical User Interface (GUI) to analyse the human body posture through the 3D reconstructed model of the person. Hence, 3D map meshing reconstruction of the human body method is proposed. This project divided into three parts, which are the development of the GUI for human posture analysis, clinical validation and posture analysis of the 3D model. The 3D model reconstructed from 3D mapping parameters shows 100% accuracy of the assessed point. The lowest difference of angle for the comparison between clinical method (goniometer) and the GUI for male is (A.Pe) 0.930±0.870 and 1.240±0.860 for female (P.Pe). This finding of 3D model assessment system can be helpful for medical doctor to diagnose patient who have spine problem.
  • Publication
    Non-invasive Detection of Ketum Users through Objective Analysis of EEG Signals
    ( 2021-11-25)
    Nawayi S.H.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Rashid R.A.
    ;
    Planiappan R.
    ;
    Lim C.C.
    ;
    Fook C.Y.
    ;
    Saidatul Ardeenawatie Awang
    Ketum leaves are traditionaly used for treatment of backpain and reduce fatigue. However, in recent years people use ketum leaves to substitute traditional drugs as they can easily be obtained at a low cost. Currently, a robust test for ketum detection is not available. Although ketum usage detection via test strip is available, however, the method is possible to be polluted by other substances and can be manipulated. Brain signals have unique characteristics and are well-known as a robust method for recognition and disease detection. Thus, this study has been done to distinguish between ketum users and non-users via brain signal characteristics. Eight participants were chosen, four of whom are heavy ketum users and four non-users with no health issues. Data were collected using the eegoSports device in relaxed state. In pre-processing, notch filter and Independent Component Analysis (ICA) were used to remove artifacts. Wavelet Packet Transform (WPT) was used to reduce the large data dimension and extract features from the brain signal. To select the most significant features, T-Test was used. Support Vector Machine (SVM), K-Nearest Neighbour, and Ensemble classifier were used to categorize the input data into ketum users and non-users. Ensemble classifier was found to be able to predict the testing instances with 100% accuracy for open and closed eyes task with Teager energy and energy to standard deviation ratio as the features.
  • Publication
    Intelligent fall detection system using traditional and non-traditional machine learning algorithm based on MQTT
    ( 2021-07-21)
    Cheong C.Y.
    ;
    Lim C.C.
    ;
    Chong Y.F.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Affandi M.
    The population of elderly people exposed to the risk of fall increases each year as reported by World Health Organization (WHO). Fall detection system presented normally is high cost, large size and not efficient. Wearable-based sensor fall detection system developed in this project which were small size, portable and low-cost. The concept of Message Queuing Telemetry Transport (MQTT) applied in this fall detection system to ease the process of data transmission from motion sensor to Raspberry Pi for classification via Wi-Fi. A small size and lightweight microcontroller (Wemos D1 mini ESP 8266) integrated with MPU6050 motion sensor to sense and publish the motion data. Raspberry Pi 3 Model B applied to carry out classification of the motion data. Machine learning algorithms used for classification in comparison were k-Nearest Neighbors (k-NN) and Long Short-Term Memory (LSTM) of Recurrent Neural Network (RNN). LSTM achieved better result at 97% than k-NN at 94%. Smartphone used to publish the notification via an application known as Blynk.
  • Publication
    Smart fall detection monitoring system using wearable sensor and Raspberry Pi
    ( 2024-02-08)
    Lim Chee Chin
    ;
    Mahmud N.F.A.
    ;
    Vikneswaran Vijean
    ;
    Ali Yeon Md Shakaff
    ;
    Tan X.J.
    ;
    Ahmad Faizal Salleh
    ;
    Shafriza Nisha Basah
    The Smart Fall Detection Monitoring System is the name of the programme that monitors everyday activities and falls. It has an accelerometer sensor (ADXL345) and Raspberry Pi 3 microcontroller board to recognise and classify the patient's fall. Python programming was done on the Raspberry Pi terminal to enable communication between the accelerometer sensor and the computer. There were 10 subjects (5 males and 5 females) collected. While daily living activities include standing, squatting, walking, sitting, and lying, the data on falling includes forward falls and falls from medical beds. The K-nearest Neighbour (kNN) classifier can categorise the data of falling and non-falling (everyday living activity). The accuracy of the kNN classifier was 100% for the combined feature and (>87%) for each feature during the categorization of the falling and non-falling classes. In the meantime, multiclass classification performance for combining features and for each feature separately was >85%. kNN classifier was used to assess the feature. The feature was chosen based on the k-NN classifier's accuracy score as a percentage. For feature selection for falling and non-falling, feature (AcclX, AcclY, AngX, AngY and AngZ) in City-block distance was selected as they performed high accuracy which was 100%. The performance of the AngZ (77%) was good during the sub-classification of the sub-class dataset. As a result, all feature characteristics were chosen to be incorporated in the IoT fall detection device. The system is real-time communication for classifying fall and non-fall conditions with 100% accuracy using kNN classifier with cityblock distance.
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