During COVID-19, unexpected death has become more commonplace, and many are finding themselves in the unfortunate position of dealing with their loved ones’ estates while continuing to worry about their own health and well-being. In a time where we are asked to stay indoors to protect ourselves and help ‘flatten the curve’, grieving family members are expected to deal with dozens – or even hundreds – of administrative tasks related to the estate of their deceased loved one.
We are building a machine learning algorithm to be able to better understand the messy clinical notes that doctors write on patients and to automatically structure them. This will help hospitals and healthcare systems standardize and extract insights from these notes to make them more useful for determining how sick COVID patients are and how they are improving over time.
This project explores 3D pose hand reconstruction using machine learning models with sensor data from innovative input devices. The goal is to propose an approach that provides real-time high fidelity hand reconstruction and understand how users perceive its quality to improve user experience and social interaction in AR/VR. The proposed methodology is to train a deep learning network that learns the mapping from sensor signals to hand pose joint locations. User studies will be performed to assess the quality of predicted hand motions.
There currently are no approved treatments for COVID-19, as global case counts increase daily. High infectivity and long time-to-recovery for COVID-19 cases is straining health-care systems globally. There is an urgent need to discover therapeutics that treat patients and improve clinical outcomes. Research and Development timelines associated with vaccines and new chemical entities will not reach clinics in time to mitigate the current patient surge, leaving drug repurposing as the most practical short-term solution.
In order to help deal with COVID-19 pandemic, there is an urgent need for development of fast, reliable, and sensitive tests that will be capable of detecting IgG and IgM proteins directly in people's blood.
The current public health emergency due to the COVID-19 pandemic has changed the way we socialize with eachother, how we access health care, and our economic conditions over a short period of time. For people who are marginalized, these changes may cause decreases in income, loss of social support and community connections, unstable home environments, more substance use withdrawal and overdose, and growing mental health concerns. Some responses to the pandemic, like physical distancing and financial challenges, are likely to last for months.
The projects will involve the modifications of a small automated ventilator (breathing machine) suitable for use in pandemics and underresourced settings (outside hospital, in patient transport, small hospitals), especially suited to patients with severe lung disease. Partner organization will be able to improve the design of the current ventilator to make it better suited for these settings.
Rapid diagnostic testing has proven essential for stemming the on-going COVID-19 pandemic. Given the global threat of COVID-19 transmission, millions of tests are needed per month to isolate infections and to facilitate safe reopening of societies. This internship will contribute to COVID-19 diagnosis efforts by first developing a faster and cost-effective protocol for viral nucleic-acid extraction, the first step common to the majority of COVID-19 molecular diagnostic tests.
In this research, occupancy monitoring data, temperature and humidity, and water quality parameters data collected through image processing and Internet of Things (IoT) sensors from multiple swimming pools are going to be processed and analyzed to identify the meaningful relations between these parameters and freshwater usage. The aim is to identify correlations between parameters and formulate the addition of freshwater as a function of number of swimmers, time spent, and activities.
This proposal details an approach for evaluating a planned project led by SII called Participatory Cities: a new inclusive, system-based approach to stimulating and supporting dense networks of practical ‘participation culture’ in cities around the world. With proof of concept developed and tested in London, UK, by the Participatory City Foundation, the model will now be implemented in Montreal and Halifax, as well as the two Toronto communities at the centre of this proposal: Alexandra Park and Regent Park.