The data of this project includes two main monitoring data (set of activities/actions) of a person monitored during one year.
The data are generated using a pseudo Markovian model using 7 transition matrices.
The "/Graphs" folder of this project includes the transition matrices in SVG and PNG formats.
Graph files are:
namesTransition_XX.XX_YY.YY_ZZZ.format presents the Markovian matrices with states (activities) names
codesTransition_XX.XX_YY.YY_ZZZ.format presents the Markovian matrices with states (activities) codes
XX.XX: the starting time of the monitored period
YY.YY: the ending period of the monitored period
ZZZ: indicates whether the period concerns any day of the week (ZZZ=Week) or a specific day (ZZZ=Friday/Sunday)
format: is the file format (PNG or SVG)
The file "activity_action_codes.txt" gives the different activity/actions and their codes.
Data files :
monitoringP1HRversion.txt : human readable version of one-year monitoring of a person belonging to the same SMAF* profile P1
monitoringP1CDversion.txt : coded version of one-year monitoring of a person belonging to the same SMAF profile P1
monitoringPxHRversion.txt : human readable version of one-year monitoring of a person where the dependency level was changing
monitoringPxCDversion.txt : coded version of one-year monitoring of a person where the dependency level was changing
* Functional Autonomy Measurement System (SMAF) is a clinical rating scale that measures the functional autonomy of elderly patients.
References:
Haider Mshali, Tayeb Lemlouma, Damien Magoni, Analysis of Dependency Evaluation Models for eHealth Services, GLOBECOM'14 - IEEE Global Communications Conference, 7 pp., December 8-12, 2014, Austin, TX, USA. -- (Rank : CORE B)
Haider Mshali, Tayeb Lemlouma, and Damien Magoni, Adaptive Monitoring System for e-Health Smart Homes , Elsevier Journal of Pervasive and Mobile Computing, November, 43:1–19, 2018. ISSN:1574-1192, DOI:10.1016/j.pmcj.2017.11.001, Impact Factor: 3.453.
Haider Mshali, Tayeb Lemlouma, Maria Moloney, and Damien Magoni, A Survey on Health Monitoring Systems for Smart Health Homes, Elsevier Journal of Industrial Ergonomics, pp 26-56, Volume 66, July 2018, Impact Factor: 2.656.
