Abdul Hafidz Omar’s research while affiliated with University of Technology Malaysia and other places

Exhaustion or extreme of fatigue is the highest condition of body performance during exercise. This state presents an optimum energy to execute by an athlete before their level of fitness reduced and required the recovery process. The purpose of this study is to monitor and predict an exhaustion threshold from three physiological systems; respiratory, cardiovascular and muscular by using artificial neural network. A developed wearable device to measure those parameters is needed for the data collection in fatigue experiment protocol. Then, it was separated into its category and filtering that signal to remove all unwanted noise in the database. Statistical feature extraction was executed for divided into five levels of exhaustion to implement supervised machine learning method. A mathematical model for prediction was developed in artificial neural network based on the data obtained from the exhaustion threshold. This model can facilitate the coach and athlete to monitor their level of exhaustion as well as prevent from the severe injury due to over exercise.

Physical fatigue or muscle fatigue is a common problem that affects people who are vigorously involved in activities that require endurance movements. It becomes more complicated to measure the fatigue level when the dynamic motion of the activity is included. Therefore, this paper aims to develop a wearable device that can be used for monitoring physical fatigue condition during aerobic exercise. A 10-bit analog to digital converter (ADC) micro-controller board was used to process the data sensed by Ag/AgCl electrodes and real-time transmitted to the computer through Bluetooth's technology. The wearable was attached to the knee and connected to the biopotential electrodes for sensing the muscle movement and convert it into the electrical signal. The signal then processed by using the fourth-order Butterworth filter to filter the low-pass filter frequency and eliminate the noise signal. The results reveal that the fatigue level increased gradually based on the rating of perceived exertion (RPE), using 10-point Borg's scale, which is rated by the subject’s feeling. Both muscle's activities in lower limb rise as speed is increased, and it was also observed that the rectus femoris is functioning more than gastrocnemius due to the size of muscle fiber. Furthermore, it was established that the maximum volumetric contraction (MVC) could be used as a reference and indicator for measuring the percentage of contraction in pre-fatigue but not to fatigue induced experiment. However, this wearable device for EMG is promising to measure the muscle signal in the dynamic motion of movement. Consequently, this device is beneficial for a coach to monitor their athlete's level of exhaustion to be not over-exercise, which also can prevent severe injury.

Generally, in sports performance, the relationship between movement science and physiological function has been conducted integrating neuronal mechanism over the past decades. However, understanding those interaction between neural network and motor performance comprehensively in achieving optimal performance is still lacking, mainly in cycling. The purpose of this study was to discuss the issues in neuroscience related to brain activity, physiology and biomechanics in achieving optimal performance in cycling. As sports technology improves, more objective measurement can be demonstrated in solving specific issue in cycling, with optimization of performance as the main focus. In this review, the focus on brain activity will be based on the evaluation of the alpha and beta brainwaves as well as the alpha/beta ratio since they are biomarkers of EEG specifically related to cycling performance. Further in-depth understanding of the mechanism and interaction between brain activity, physiology and biomechanics in competitive cycling were acquired and discussed. Moreover, the biomarkers of brain activity related to cycling performance from previous studies were clearly identified and discussed and recommendations to be incorporated in future research were proposed.

Generally, in sports performance, the relationship between movement science and physiological function has been conducted integrating neuronal mechanism over the past decades. However, understanding those interaction between neural network and motor performance comprehensively in achieving optimal performance mainly in cycling is still lacking. The purpose of this study was to discuss the issues in neuroscience related to brain activity, physiological and biomechanics in achieving optimal performance in cycling. As sports technology improves, more objective measurement can be demonstrated in solving specific issue in cycling. In this review, the focus on brain activity will be based on the evaluation of alpha and beta brainwaves as well as the alpha/beta ratio. Understanding of the mechanism and interaction between brain activity, physiology and biomechanics in competitive cycling will be acquired. Moreover, the biomarkers of brain activity related to cycling performance from previous studies will be identified and discussed.

In sport, most of the muscle fatigue occurred on the upper limb is due to the arm movement in static and dynamic activities. Moreover, there are limited devices to measure muscle activity in real-time monitoring when games ongoing. Therefore, the purpose of this study is to detect muscle fatigue by using the wireless surface Electromyogram (sEMG) in a different type of contraction activity. Two types of experiment were conducted in this paper, which is for isotonic and isometric contraction activities. In order to measure muscle activity, sEMG was used as an invasive technique with (Ag/AgCl) wet electrode placed on the skin. Furthermore, heart rate sensor was included to identify the relationship of muscle activity and heart beat. In prior, a prototype of sEMG with 10bit analogue digital converter (ADC) microcontroller was developed for the measurement. Then, it was transmitted the signal to the computer wirelessly for the further post-processing analysis. Reducing the amplitude of signal during exercise indicates the muscle fatigue has occurred. The results reveal that biceps brachii is the most active muscle during forearm lifting movement. It was hugely differences when compared to triceps brachii muscle during isotonic contraction in one-sixth ratio. Also, increasing physical activity significantly accelerated fatigue in muscle and also raised the heart rate per minute. The results presented here may facilitate improvements in the prediction of fatigue that will lead to exhaustion for another muscle.

This article reports on the refinement of selected VI for detecting and mapping of snow algae growth using multi-temporal Landsat Thematic Mapper (TM), archived from early 80’s to the recent Landsat-8 Operational Land Imager (OLI). In-situ spectral radiometry samples during UTM-Antarctica summer 2015 expedition were used in air-to-ground correlation of the corresponding satellite data sets, later yield to retrieve snow algae occurrences in 2009-2015 summer. Multi-temporal analysis of the snow-algae growth extents extracted was then compared to against the climate-change effects of the corresponding area in Antarctic Peninsula. Hence, the indicator for snow-algae growth as effects of the climate-change could be inferred. Results indicated good agreement (Pearson’s coefficient r=0.85, p< 0.01) between snow-algae growth extent with effects of temperature changes reported. Recent in-situ verification of the selected sites in the region confirmed the agreements.