• 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