Healthcare management has been facing challenges to intervene timely to address the health issues faced by seniors and aging population living with some sort of disabilities either at senior homes or private houses. The recent catastrophes faced by Canada during the Covid-19 pandemic is a burning example. This happened due to the lack of timely information about the health conditions the victims were going through.

An AI powered Smart Digital Surveillance could be the best solution to this challenge. Wearing a simple smart device by seniors can serve the purpose. The device will record and send forward a range of data related to the conditions of these physically disabled people.

Based on my past work with a German Rehabilitation organization (Johanniter), as well as with the WHO, UNDP and Dementia Society of Ottawa, and recent published and current works on AI deep learning algorithms development initiatives with the Cumming School and School of Architectures’ collaborative project, I plan to join forces with colleagues from the Computer Science and Kinesiology Depts., and partnering with colleagues from the Clinical, Radiology and Rehabilitation departments to successfully deliver this project.

This project aims to exploit the most useful Deep Learning AI algorithms, analyze speech, image, video, object recognition algorithms for remote diagnosis and provide time series forecasting. A model developed on such data, can perform automated analyses and raise cascading alerts for actions to be taken proactively.

These actions will prevent victims from getting more serious illness or avoidable accidents, and will cut down costs on the expensive time-consuming administrative and laboratory procedures. Thereby, can enhance health systems’ efficiency by reducing the costs of treatment and rehabilitation programs. Such a model can also track the progress of patients on physical therapy who are recovering from a physical injury by analyzing and comparing these to age, body type, muscle strength, posture, gait and various body poses over time, level and type of physical exertion etc. It can also identify and quantify defects or lesion; thus, determine prognosis. Such assessment and tracking of improvement of gait (analyzing video stream while walking) is crucial to forecast when a patient can return to normalcy. This can also help improving assisted living technologies used by seniors and adding quality of life to the people suffering from chronic disabilities and other debilitating diseases at their advanced age.