Sports injury has long been causing concern to the athlete’s performance, financial aspect, and psychological impact. This is even more prominent in recent years as sports become more widely available o the mass population. Reducing the chances of athletes experiencing injuries not allows them to maintain optimal performance during training and competition but is also psychological benefits for the athletes. Amsterdam UMC has gathered weekly OSTRC data from 19 Waterpolo athletes over 109 weeks. By using this data, it is possible to build a model that can provide an indication of possible injury risks thus helping athletes in controlling possible injuries. Traditionally, statistical models are used for this kind of analysis. However, this often requires large amount of time and a-priori knowledge. Hence an injury risk classifier method based on longitudinal data (OSTRC) and machine learning algorithm was proposed in this study. The chosen ML algorithms for this study are ANN, LSTM, and Random Forrest. To investigate the importance of time dependency between data entries, sliding window, and forward chaining are used during data processing. All models trained with minimally processed data, sliding window data, and forward chaining data to investigate the impact of time dependency on model accuracy. The OSTRC data set is pre-processed, and SW and FC methods are applied. Data is then split into training and testing data set. Metric used to assess model performances are accuracy and confusion matrix. The results of this study show that both RF-SW and LSTM-SW produced an accuracy of 92.17% and 91.67% respectively. Accuracy of ANN on training data indicates adequate performance, but the confusion matrix indicates poor performance. Confusion of RF-SW and LSTM-SW show small variation due to differences in the test data set, which can be mitigated as more data are available. Inconclusion, the high level of accuracy from both RF-SW and LSTM-SW proves that it can be used to provide insight to athletes, helping them to reduce the chances of injury.

Wild_res have always been a problem; but, with human migration all around the world, this problem has signi_- cantly increased its importance and impact. The mounting challenges facing wild_re management, together with its increasing demand, have necessitated research into technological advancements and methods to increase wild_re management ef_cacy. As Unmanned Aerial Vehicles (UAVs) become more and more widespread, development has skyrocketed and is applied to all kinds of areas, such as surveillance, military, and commercial _lming. Fire surveillance is the area of interest for this project. The main goal of this report is to offer a concise overview of the preliminary design of the _re monitoring swarm system. This report will touch upon the main problems encountered while working on the design and the solutions of these problems. The report ends with some recommendations for the future. Forest _res happen all over Europe; however, the Mediterranean region is most affected by these forest _res. Combined with global warming, the occurrence of forest _res will increase, which will lead to an increase of the annual economic and environmental damage caused by these _res. Both _re _ghters and operational centres have been searching for years to effectively combat forest _res, thereby minimising the damage done by forest _res. Different systems have been used in the past, some as simple as _re lookout towers and some as complex as optical-based smoke detection systems. However, none of these systems has been able to provide real-time data to _re_ghters. This is where this _re monitoring swarm comes into play. The swarm system is composed of a searching and a tracking element. The searching element has 5 Penguin B UAVs and has two operating modes. The _rst mode is detection of _res and con_rming upon detection, while the second mode consists of acting as a mothership for that speci_c _re. The tracking element has 10 Albatross UAVs which can perform three functions, namely high altitude tracking; low altitude tracking; and scouting. Members of the swarm will have a communication system for communication among swarm members and for communication with the operational centres. All UAVs will be launched using a catapult and are capable of a fully autonomous landing...