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The SARS-CoV-2 (Covid-19) virus has had severe impacts on college students' ways of life. To examine how students were coping and perceiving the Covid-19 pandemic, a secondary analysis of an online survey across the three Arizona public universities investigated students’ knowledge about Covid-19, engagement with preventive strategies, pandemic preparedness and gauged their risk perception. Results from our analysis indicate that the students were knowledgeable about Covid-19 and were changing their habits and engaging with preventive measures. Results further suggest that students were prepared for the pandemic in terms of resources and were exhibiting high-risk perceptions. The data also revealed that students who were being cautious and engaging with preventive behaviors had a higher risk-perception than individuals who were not. As for individuals who were prepared for the pandemic in terms of supplies, their risk perception was similar to those who did not have supplies. Individuals who were prepared and capable of providing a single caretaker to tend to their sick household members and isolate them in a separate room had a higher risk perception than those who could not. These results can help describe how college students will react to a future significant event, what resources students may be in need of, and how universities can take additional steps to keep their students safe and healthy. The results from this study and recommendations will provide for a stronger and more understanding campus community during times of distress and can improve upon already established university protocols for health crises and even natural disasters.
Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.