Combining data science and wearable technology for early health risk detection

It has been well established that if human diseases can be diagnosed early, the prognosis and future quality of life is much improved. With advancements in computing technology in both the hardware (e.g. smaller, lighter, better batteries) and software (i.e. improved artificial intelligence), health systems are entering a renaissance when wearable technology and data science are being applied to clinical diagnostics. The fellowship’s objective is two-fold in which the first is to assess the validity and durability of Ultimate Sensor System’s wearable technology, and secondly, apply their wearable technology to clinical health applications including early health risk detection through the use of data science methods. Phase 1 will consist of reliability, validity, and durability testing of the wireless system while Phase 2 will apply machine learning methods to identify neuromotor diseases (e.g. Parkinson’s disease) and predict future cases. This portable ehealth system will offer Canadians an early detection system that has incredible potential, particularly for remote communities who have difficulties coordinating continual health professional services. By doing so, Ultimate Sensor Systems (industry partner) will directly benefit from this collaboration as they will become global leaders in data science/ehealth systems, which entices new customers and increases their global market share.

Faculty Supervisor:

Ryan Graham

Student:

Partner:

Ultimate Sensor Systems;University of Ottawa

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Ottawa

Program: