Sport performance analysis in sports practice cannot be separable. It is important to help coach analyse and improve the performance of their athletes through training or game session. Due to the advancement of technology nowadays, the notational analysis of the video content using various software packages has become possible. Unluckily, the coach needs to recognize the actions manually before doing further analysis. The purpose of this study is to formulate an automated system for badminton smash recognition on widely available broadcasted videos using pre-trained Convolutional Neural Network (CNN) method. Smash and other badminton actions such as clear, drop, lift and net from the video were used to formulate the CNN models. Therefore, two experiments were conducted in this study. The first experiment is the study on the performance between four different existing pre-trained models which is AlexNet, GoogleNet, Vgg-16 Net and Vgg-19 Net in recognizing five actions. The results show that the pre-trained AlexNet model has the highest performance accuracy and fastest training period among the other models. The second experiment is the study on the performance of two different pre-trained models which is AlexNet and GoogleNet to recognize smash and non-smash action only. The results show that the pre-trained GoogleNet model produces the best performance in recognizing smash action. In conclusion, pre-trained AlexNet model is suitable to be used to automatically recognize the five badminton actions while GoogleNet model is excellent at recognizing smash action from the broadcasted video for further notational analysis.

Lower temperature biohydrogen production has always been attractive, due to the lower energy requirements. However, the slow metabolic rate of psychrotolerant biohydrogen-producing bacteria is a common problem that affects their biohydrogen yield. This study reports on the improved substrate synthesis and biohydrogen productivity by the psychrotolerant Klebsiella sp. strain ABZ11, isolated from Antarctic seawater sample. The isolate was screened for biohydrogen production at 30°C, under facultative anaerobic condition. The isolate is able to ferment glucose, fructose and sucrose with biohydrogen production rate and yield of 0.8 mol/l/h and 3.8 mol/g, respectively at 10 g/l glucose concentration. It also showed 74% carbohydrate uptake and 95% oxygen uptake ability, and a wide growth temperature range with optimum at 37°C. Klebsiella sp. ABZ11 has a short biohydrogen production lag phase, fast substrate uptake and is able to tolerate the presence of oxygen in the culture medium. Thus, the isolate has a potential to be used for lower temperature biohydrogen production process.

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Muscle fatigue in sports science is an established research area where various techniques and types of muscles have been studied in order to understand the fatigue condition. It can be used as an indicator for predicting muscle injury and other muscle problems which can decrease athletes’ performance. Muscle fatigue usually occurs after a long lasting or repeated muscular activity. Electromyography (EMG) assessment method is a standard tool used to evaluate muscle fatigue based on the signals from the neuromuscular activation during fatigue condition. However, additional time for equipment set up such as placement of the electrodes and the use of multiple wires make this overall setting a bit complicated. In addition, the signal from EMG which possessed some noise, need to be filtered and post processing time is also required to obtain a reliable measurement signal. Therefore, researchers have explored the application of thermal imaging technique as one of the alternative methods for muscle fatigue assessment. The objective of this study is to investigate the correlation of muscle fatigue condition measured using a non-invasive infrared thermal imaging technique and a standard evaluation method, EMG. Five healthy men were selected to run on a treadmill for 30 minutes with a constant speed setting. Temperature and EMG signals were registered from gastrocnemius muscle of the subjects' dominant leg simultaneously. Result obtained shows that the average temperature of gastrocnemius muscle decrease as subjects start to exercise. Further temperature decrease along with exercise and increase in temperature were observed during the recovery period. Statistical analysis was performed and analyzed using both temperature and EMG parameters. Result shows a significant strong correlation with r = 0.7707 and p < 0.05 between temperature difference and median frequency (MDF) for all subjects compared to average temperature. Therefore, it is concluded that temperature difference extracted from thermal images can be used as an ideal parameter for muscle fatigue evaluation.

Vital sign monitoring is an important body measurement to identify health condition and diagnose any disease and illness. In sports, physical exercise will contribute to the changes of the physiological systems, specifically for the vital signs. Therefore, the objective of this study was to determine the effect of physical fatigue exercise on the vital sign parameters. This is significant for the fitness identification and prediction of each individual when performing an exercise. Five male subjects with no history of injuries and random BMI were selected from students of biomedical engineering, Universiti Teknologi Malaysia. Based on the relationship between physical movement and physiology, the parameters considered were heart rate, blood pressure, and body temperature. Subjects were required to run on the treadmill at an initial speed of 4 km/h with an increase of 1 km/h at every 2 minutes interval. The effect of exercise was marked according to the fatigue protocol where the subject was induced to the maximum condition of performance. All parameters were measured twice, for pre and post exercise-induced protocol. The analysis of relationship of each parameter between pre and post fatigue was p<0.05. The results revealed that the heart rate and gap between blood pressure’s systolic and diastolic were greater for all categories except underweight, where the systolic blood pressure dropped to below 100mmHg at the end of exercise. Also, the body temperature was slightly declined to balance the thermoregulatory system with sweating. Hence, the vigorous physical movement could contribute to the active physiological system based on body metabolism. Heart rate and blood pressure presented significant effects from the fatiguing exercise whereas the body temperature did not indicate any distinguishable impact. The results presented might act as the basis of reference for physical exercise by monitoring the vital sign parameters.