Research Projects

A Novel Sensor to Reveal COVID-19 “hidden” Infection Symptom

Currently, only symptomatic cases of COVID-19, caused by the pathogen 2019-nCoV, are being identified and isolated. A large percentage of these cases lack the typical known symptoms of fever, fatigue, and dry cough. Furthermore, COVID-19 carriers can remain asymptomatic during the incubation period and thus facilitate community spread. In Canada, as of July 6, only 2,940,925 people (~7.82% of the population) have been tested for COVID-19, with more than 105,536 positive cases identified. Given that as many as 33%–41% of all COVID-19 cases lack the known symptoms, up to an estimated 34,826–43,269 cases could be asymptomatic and likely endangering public health. Early detection and isolation of COVID-19 cases, especially asymptomatic cases, remains an unmet challenge and is therefore crucial for controlling this outbreak and future hazards. In short, a novel method to identify asymptomatic cases is urgently needed. We aim to realize a rapid testing solution by developing a new sensing technology to identify asymptomatic and presymptomatic cases through early detection of a “hidden” symptom using the saliva sample. Our COVID-19-related research is supported by CMC and Mitacs Accelerate funding to develop a safe, low-complexity, rapid, and easy-to-use at-home sensing device (with mass-production potential) for the early detection of infection as a reliable symptom to isolate COVID-19 cases. The proposed technology—encompassing bioengineering, microelectronic open-JFET, computer vision, deep learning techniques—will allow accurate testing of saliva at home using a portable sensor communicated with cloud computational platform that can evaluate disease progress or treatment.

Development of severity index of exacerbation for COVID-19 symptoms from abnormal respiratory patterns

Struggling to breathe has been a challenge that existing monitoring devices have faced, for identifying early symptoms of respiratory diseases such as novel Coronavirus (COVID-19). Measuring respiratory rate alone would result in potentially missing if a patient was unable to inhale a full breath.

To overcome this challenge, a novel technology solution invented in Canada is used through an Intelligent Bed Sheet, with the ability to continuously monitor if a patient has normal, shallow, or irregular breathing.

This collaboration with York University will revolve around developing a Severity Index by identifying earlier when a patient is struggling to breathe. Using the most advanced electronic fabric technology, the SOSCIP Platform will support computing challenges by using large amounts of breathing data in a constantly changing environment, to precisely identify before a patient’s breathing becomes more severe.

Forecasting Covid-19 Epidemic in Canada with Spatial-Temporal Models That Exploit Population Behaviour on Twitter

The Covid-19 pandemic is creating unprecedented damage to the public health and world economy.  Being able to accurately forecast the spread of Covid-19 is critical for the federal and provincial governments of Canada to devise policies and measures maximally protecting the lives of Canadians and rapidly reviving the Canadian economy. Not only important at present, developing advanced epidemic projection algorithms and techniques also helps prevent future epidemics of other infectious diseases. In this project, we set out to develop advanced forecasting algorithms for the spread of Covid-19 across Canada. Specifically, we will exploit the behaviour information of the population revealed on social media as well as the correlation of the Covid-19 spread across different regions of Canada. Our development will integrate modern AI techniques in data analytics, machine learning and natural language processing with the conventional mathematical models for infectious diseases. The developed algorithms will be hosted on a web platform, which will provide accurate daily predictions of Covid-19 spread and release them to the public. These predictions will assist the governments to strategically adjust policies for the protection of Canadian lives and the revival of the Canadian economy. Individuals, families, schools and businesses will all benefit from this new source of information in their planning processes.

Getting ahead of the curve: a novel way to find people who are likely to be asymptomatic carriers of COVID-19 before they infect others

The spread of COVID-19 has been slowed by physical distancing, self-isolation, lockdowns, masks and travel restrictions. Doing more testing and contact tracing of known cases has helped contain the spread. However, these approaches are ‘behind the virus’ – by at least 5-10 days. We need a way of getting ahead of the virus, before it spreads further. We know that about half the infections are transmitted by people who do not have any symptoms, and this is sufficient to cause a growing epidemic. Therefore, we need to find, isolate and test at least some asymptomatic people. Like intelligence officers hunting hidden terrorists, we need to prevent attacks before they happen, instead of chasing the culprits after the fact. In other words, we need to identify carriers before outbreaks occur, not after. Our team proposes a new way to do this.

The overall goal of this project is to develop models and an intervention prototype to predict which individuals are most likely to be exposed to COVID-19 and are therefore most at risk of onward transmission. We will apply statistical modeling and Artificial Intelligence (AI) methods to demographic, occupational, social networking and geolocation data. In simulations, we will test different ‘smart isolation and testing strategies’ that could be used by public health officials to determine which would most effectively reduce viral transmission. Applying AI-informed strategies could enable partial relaxation of confinement rules for lower-risk segments, and allow greater reopening of economic and social life without risking a second wave and further lockdowns.

Remote production monitoring in a post COVID manufacturing environment

Given COVID-19 spread around the world, the importance of maintaining health is further understood through maintaining social distancing as when one coughs, sneezes or speaks, they spray small liquid droplets from their nose or mouth, which may contain the virus. If someone is too close to a person, he or she can breathe in the droplets, including the COVID-19 virus. This project proposes an approach to provide a real-time computer vision and deep learning method for inspection of social distancing as well as optimizing production operations while non-essential personnel, i.e. operations managers and manufacturing/production engineers, work remotely for manufacturing organizations. It will be the ideal solution for Canadian manufacturers that are re-starting production post-COVID-19 ensuring that worker safety is maintained without sacrificing production output and quality.

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The bandwidth bottleneck has become worse post-COVID-19 as more people are working from home and thereby taking up much bandwidth. Small manufacturers, who cannot afford high-speed internet, are more critically impacted by this limitation. Hence, the goal of this project is to move video processing from the cloud to manufacturing sites via a reliable gateway device design and deployment that does not sacrifice quality and reliability for speed. This means videos do not have to be streamed to the cloud, and only the vital information extracted from the video feeds will be uploaded. Therefore, data transfer to the cloud can be accomplished with low internet bandwidth to ensure live-stream video feeds can be processed in near real-time without loss of quality or reliability for the required monitoring systems at hand.

Reopening the economy responsibly: Recruit and measure the impact of volunteers during and after COVID-19 pandemic

Canada is reputed to have one of the largest and most dynamic voluntary sectors in the world. However, due to the COVID-19 pandemic, the majority of organizations have lost a minimum of 50% if not all of their workforce, and others on the frontline of COVID response like Meals on Wheels etc. are in desperate need of volunteers to support their operational capacity. The impacts of lacking of volunteers will persist post-COVID with municipalities across our nations seeking ways to rebuild their communities and engage citizens. There will be increased caution and hesitation from people to engage in public activities and volunteer roles, particularly those that involve human interactions. Using our data analysis skills and the app, @MyEffect, we propose to further develop our digital tool to connect volunteers and organizations to help reopen our economy responsibly by recruiting and measuring the impact of volunteers during and after the COVID-19 pandemic.

The proposed research includes two folds: (1) Develop a recommender system to match volunteers and the needs of non-profit organizations during and after COVID-19; (2) Develop an impact measurement of voluntary actions on the recovery of Canadian economy after COVID-19. Overall, we hope the resulting innovation will support the urgent need of Canadian non-profit and charitable organizations to recruit, retain and rebuild their volunteer workforce during and post the COVID-19 pandemic; along with providing a trusted mechanism for Canadian municipalities and government to responsibly reopen the economy by monitoring community needs and mobilizing human capital resources to bridge opportunity gaps to strengthen communities.