title: Identification of inactivity behavior in smart home.
abstract: To help elderly people live independently at home, the TIMC-IMAG laboratory developed Health Smart Homes called 'HIS'. These smart Homes are composed of several sensors to monitor the activities of daily living of the patients. Volunteers have accepted to be monitored during 2 years in their own flats. During one year, we carried out our survey on one elderly patient. Thanks to this experimentation, we will access to relevant information like physiological, environmental and activity. This paper focuses on daily living activity. We will introduce an original data splitting method based on the relationship between the frame of time and the location in the flat. Moreover we will present two different methods to determine a threshold of critical inactivity and eventually we will discuss their possible utilities.
authors.affiliation: UMR CNRS/UJF 5525, team AFIRM, Faculté de Médecine de Grenoble, bätiment Jean Roget, 38706 La Tronche, France. Julien.Poujaud@imag.fr
authors.forename: J
authors.initials: J
authors.lastname: Poujaud
authors.forename: N
authors.initials: N
authors.lastname: Noury
authors.forename: J-E
authors.initials: JE
authors.lastname: Lundy
mesh_terms: D000203:Activities of Daily Living; D000368:Aged; D003195:Computer Communication Networks; D016852:Housing for the Elderly; D006801:Humans; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity; D010363:Pattern Recognition, Automated / Q000379:methods