Open AccessIn Situ Abnormal Behaviours Detection
Author(s) -
Chaima Bouali,
Olivier Habert,
Abderrahim Tahiri
Publication year - 2020
Publication title -
modelling, measurement and control. c, energetics, chemistry, earth, environmental and biomedical problems
Language(s) - English
Resource type - Journals
ISSN - 1259-5977
DOI - 10.18280/mmc_c.811-401
Subject(s) - computer science , context (archaeology) , judgement , health care , service (business) , warning system , automation , real time computing , data science , computer security , human–computer interaction , engineering , business , telecommunications , mechanical engineering , paleontology , marketing , political science , law , economics , biology , economic growth
This work describes a ‘detection of abnormal activities and health-related changes’ system for an elderly person at her/his home. The analysis is based on the data collected by a domotic box of the market. The box was initially designed to continuously recognize the owner’s daily activities in order to anticipate anomalies and consequently prevent health complications and enhance the rate of disease prevention. The box uses non-intrusive home automation sensors to detect the activity level of the occupants. It is equipped also with other technologies, including humidity sensors, bed and chair sensors to name a few. In order to build a system capable of intercepting warning signs for early intervention, we adopt a Hidden Markov Model based approach that we will initialize beforehand with the activity sequences of the user within a given period. The outcomes of the model paves the way for deducting the final judgement and reporting a relevant context-aware alert to healthcare service experts. Other statistical processes might complete this behavioural analysis later on to enhance the alerts accuracy.