title: Monitoring activity patterns and trends of older adults.
abstract: This paper presents a pattern mining model developed for the continuous monitoring of high level activities and home-based behaviors for functional and cognitive status assessment with ambient assisted living systems such as in the EU-funded SOPRANO project. Motivating older people to engage in regular physical exercise is a key task of SOPRANO to improve health status and executive functions. A case study has been elaborated through software simulations to show how physical everyday life activity such as walking or sitting could be assessed and controlled for a better health lifestyle using the model.
authors.affiliation: Gerontology Research Centre, Simon Fraser University at Harbour Centre, Vancouver, Canada. gvirone@sfu.ca
authors.forename: G
authors.initials: G
authors.lastname: Virone
authors.forename: A
authors.initials: A
authors.lastname: Sixsmith
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D001185:Artificial Intelligence; D002940:Circadian Rhythm; D015444:Exercise; D006801:Humans; D008954:Models, Biological; D018670:Monitoring, Ambulatory / Q000379:methods*; D010363:Pattern Recognition, Automated; D012984:Software