DocumentCode
2078450
Title
Perspectives in Home TeleHealthCare System: Daily Routine Nycthemeral Rhythm Monitoring from Location Data
Author
Franco, C. ; Demongeot, J. ; Fouquet, Y. ; Villemazet, C. ; Vuillerme, N.
Author_Institution
AFIRM, TIMC-IMAG Lab., Grenoble, France
fYear
2010
fDate
15-18 Feb. 2010
Firstpage
611
Lastpage
617
Abstract
Free of most social constraints, elderly people tend to perform activities of daily living following the same sequence. This paper proposes a method for medical telesurveillance to detect and quantify a nycthemeral shift in this daily routine. While this common phenomenon is mostly mild, in acute cases, however, it may reveal a pathological behavior requiring a rapid medical examination. This method allows to compare two sequences of activities using the Hamming distance and to interpret the result according to the Gumbel distribution. It may be used to compare either consecutive sequences thereby taking into account evolution in the habits or a sequence to the person´s individual activity profile to detect dementia´s onset. In this early stage, only elementary activities were considered. That is the reason why location data were used to monitor the person´s nycthemeral rhythm of activity. IR sensors placed in her own flat allowed us to follow-up the inhabitant´s successive activities. Improvements of this method have already been planned. They include the use of a multi-sensors network to monitor both actimetric (location, movement, posture) and physiological nycthemeral rhythms (ECG, respiratory frequency) and to detect the use of particular items (fridge, chairs, bed). This more sophisticated sensors network will allow us to monitor more complex tasks execution and then to detect pathological behaviors such as perseveration in a task or wandering. On the other hand, multiplying sensors will require more storage capacities and the use of time-consuming data fusion tools. Therefore, a classification phase will be necessary to reduce as possible the number of relevant sensors.
Keywords
biomechanics; electrocardiography; geriatrics; infrared detectors; intelligent sensors; medical signal processing; patient monitoring; pneumodynamics; sensor fusion; signal classification; telemedicine; ECG; Gumbel distribution; Hamming distance; IR sensors; actimetric rhythms; daily routine nycthemeral rhythm monitoring; data classification; data fusion; dementia´; elderly people; home telehealthcare system; location data; medical telesurveillance; movement; multisensors network; posture; respiratory frequency; Biomedical monitoring; Capacitive sensors; Dementia; Electrocardiography; Frequency; Hamming distance; Infrared sensors; Pathology; Rhythm; Senior citizens; Health smart home; alarm triggering; chronobiometry; elderly people monitoring; nycthemeral rhythm;
fLanguage
English
Publisher
ieee
Conference_Titel
Complex, Intelligent and Software Intensive Systems (CISIS), 2010 International Conference on
Conference_Location
Krakow
Print_ISBN
978-1-4244-5917-9
Type
conf
DOI
10.1109/CISIS.2010.192
Filename
5447534
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