The proposed research project focuses on multi-sensor such as heart rate, body temperature, oxygen saturation level, and inertial sensor data-based sleep stage classification, and tremor detection in real-time for preventive health devices. The goal of the proposed research is to build robust and energy-efficient machine learning and deep learning-based approaches that extract and analyze the significant information from the multisensor data coming from wrist-based health devices to help Parkinson's patients with tremors and an individual with sleep quality tracking.

This project will apply the theoretical framework of Activity Theory to Agile product development methods in order to decompose them into salient components, using an integrative literature review of Agile methodologies to do so. This decomposition will then be used to adapt the open-source education management/planning software CourseFlow for use in Agile development. This software has been designed with Activity Theory in mind, viewing different levels of education (lesson, course, curriculum) as interconnected and nested activities.

COVID-19 has already infected more than 2.8 million people with close to 200 thousand death globally (as at 2020 April 25), while it is continuously spreading. Before a vaccine is discovered, the only way to slow down the spread and reduce the number of death is testing. Nevertheless, there is no guarantee that someone recovered will not be infected again. It is necessary to keep monitoring COVID-19 patients and contact tracking. All patients currently tested positive for COVID-19 are sent home for self-confinement. Agents of public health call them on a daily basis by phone one by one.