Future engineers shall not only good at knowledge and technology but also good at other attributes such ethical, professional as well as managing people and emotion. Service learning is an effective education model to develop more holistic engineers. However, there is a lack of service learning framework that integrates technology, in order to achieve the acquisition of the above attributes. This paper describes how drone technology is disseminated by engineering students to the public through a service learning programme. Document analysis of the course information, project reports, and students’ reflections were employed in this study to identify the learning process and attributes developed by the students. the students went through 6 phases of service learning implementation. Results show that the service learning has enhanced the students’ learning, sense of responsibility, accountability, and international exposure. These are essential to develop good engineers in the future.
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.
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.
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.
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