Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Like many other industrialized nations, Canada faces the challenge of adapting to a rapidly aging demographic.x000D
Our country requires innovative and transformative solutions in order for our health care and social systems tox000D
remain strong and sustainable, in presence of a growing population of senior citizens. The ability to "age inx000D
place" and remain living independently for a longer period of time is an important objective in this context.x000D
Most Canadians prefer aging in place and independent living over other forms of care (e.g., residential care), ifx000D
they feel adequately supported and cared for. Information and communication technologies (ICT) are expectedx000D
to play a major role in enabling this vision. A senior's home environment equipped with smart sensors may bex000D
able to prevent or detect health and safety-related incidents, such as falls or the onset of an illness.x000D
Since there is a large diversity of lifestyles and living environments, such "Smart Home" technologies mustx000D
be adaptive to the particular habits and needs of an individual. Machine learning algorithms can be used tox000D
provide such intelligent adaptation based on activity data streams sourced from a variety of home-basedx000D
sensors. Our company partner (Tochtech) is a healthcare technology provider start-up firm who is developing ax000D
Smart Home package in support of caring for independently living seniors. Tochtech's solution will be capablex000D
of monitoring daily activity data of seniors and raising alerts when detecting potential safety or health risks.x000D
Tochtech is interested in exploring the use and evaluation of machine-learning algorithms, platforms and toolsx000D
for the purpose of providing an adaptive incident detection system. They have engaged with the University ofx000D
Victoria's LEAD lab to provide expertise and guidance. The objectives of this NSERC Engage project are (1)x000D
to evaluate current machine learning algorithms, platforms and tools and select the ones most suitable for thex000D
application problem, (2) to prototype a machine learning system for detecting two selected risk categoriesx000D
(accidental falls and acute illness), and (3) to design an extensible architecture platform for adding detectionx000D
algorithms to address additional risk categories.