In recent years, the popularization of devices to monitor people in combination with Machine Learning (ML) in the context of Internet of Things (IoT) has grown significantly. Then, the number of applications to solve many health issues that require data collection and processing has increased. One of the common concerns by Health institutions is human falls, which can lead to severe health damages or death. Thus, it is crucial to detect quickly when a fall occurs, to reduce the possible sequels. One way to identify potential falls is using data collected from wearable devices as input of an IoT system using ML models, which is the solution proposed in this work using Cloud computing. Thus, we present this solution and its deployment and evaluation that consists of three modules: data acquisition and transfer, intelligent cloud application, and notification service. The best result of the ML models presented is 94.4% of accuracy, considering a low rate of false negatives of 4.3%.
Scaffolding is an approach used by some modern web frameworks in order to generate an initial version of applications code based on domain model meta data. Since this temporary code should be customized by programmers to implement real systems, its quality metrics are important aspects. In this paper, a methodology is proposed and applied in order to relate domain model size and a quality metric -amount of duplicated code -focusing on Graphical user interface implementation. Results show that code duplication grows at least linearly with the growth of the number of entities in domain model. There are also some scenarios where quadratic proportions were found. These observations suggest that, for large domain models, code quality and its evolution would be affected when scaffolding frameworks are used.
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