The ability of the health system to manage a massive influx of patients is based on the combination of four factors: the personnel, the equipment, the physical spaces and the system in place. A combination better known in jargon as the 4 "S" (staff, stuff, structure / space, system). A fifth factor that is often misunderstood is synchronicity.
Front-line clinicians have reported that different respiratory stiffness results in different COVID-19 patient conditions on ventilators. In this project, we test and improve NovaResp’s monitoring hardware and analyze the collected data for development of algorithms with focus on respiratory stiffness of patients with COVID-19. The resulting algorithms and monitoring device could lead to determine whether patients need to be intubated, and when under ventilation, what ventilatory settings need to be applied for better patient outcome.
Gold Sentintel has built a non-visual fall detection and respiration monitoring product for Long Term Care called ElephasCare LTC. ElephasCare LTC utilizes passive radar sensors to detect residents’ position within their rooms, monitoring motion and respiration, and automatically notifying caregivers in the event of a fall, or lack of respiration.
There is currently an abundance of research in the community in terms of capturing X-ray data around COVID-19 analysis to help diagnosis for radiologists. However, at the same time there are staffing shortages that lead either to long diagnosis wait times and potential misdiagnosis. Our research is focused around rapid deployment of AI and developing of a system to deploy AI technologies and algorithms using existing infrastructure within the hospital.
With respect to large-area display applications, it is desirable to have not only the active layers but also the electrodes in the OLEDs that can be formed by solution fabrication process. To address the manufacturing challenges of high-performance OLEDs, several scalable techniques such as doctor blading, ink-jet printing, and ultrasonic spray coating have been developed or employed.
Fourien Inc. is developing a diagnostic medical instrument for rapid and low-cost detection of COVID-19 virus using saliva samples from early-stage infections. The custom-built vibrometer instrument will use micro-sensors to detect the RNA of the virus. There is a need to develop optimized and sensitive modules of optics, electronics and mechanics that are critical for the instrument to work. The intern will use her prior academic experience to bring creative solutions to industry problems.
In light of the COVID-19 pandemic, there has been an increase in sexual assault incidents. Research shows that when there are disasters and economic meltdowns, there is usually a spike in sexual assaults. The world is experiencing both a pandemic and economic downturn. With self-isolation, social distancing, and the stay-at-home orders, the use of technology to provide support to survivors is now more critical than ever before. However, the use of technology could be a double-edged sword. Using technology to support survivors could sometimes increase the risk for survivors.
A study of Covid-19 patients revealed that about five percent of patients with the worst effects of the infection require a ventilator to push air into the lungs, take over the body’s breathing process, and offer the best chance of survival. To have a precise control, this research program aims to develop a Power electronics drivers for high-speed motors to control pressure loop precisely for maximum performance of ventilation capacities.
Many of the current greenhouse cultivation processes can be labor intensive, unable to accurately capture all information on a plant, and hard to manage as grower’s operation scale. By proposing a new method of collecting and analyzing data in these greenhouse using computer vision and machine learning, interns will try to improve the efficiencies of these processes. This proposed system aims to collect valuable information such as plant dimensions and fruit sizes that was previously very inefficient for human labour to do, and to predicts most optimal growing environment with this data.