Adaptive monitoring system for e-health smart homes

Abstract

Existing e-health monitoring systems mainly operate in isolation from the requirements of modern healthcare institutions. They do not include optimized techniques which learn the patient's behavior for predicting future important changes. We propose a new context-aware e-health monitoring system targeted at the elderly and isolated persons living alone. It monitors daily living activities and evaluates dependency based on geriatric scales used by health professionals. Its adaptive framework collects only relevant contextual data for evaluating health status. By monitoring the achievement of daily activities, the system learns the behavior of the monitored person. It is then able to detect risky behavioral changes by using our novel forecasting approach based on the extension of the Grey model GM(1, 1). In order to evaluate our system, we use a Markovian model built for generating long term realistic scenarios. By simulation, we compare the performances of our system to traditional monitoring approaches with various synthetically generated scenarios and profiles. Results show that with minimal sensing and data collection, our system accurately evaluates a person's dependency, predicts its health condition, and detects abnormal situations while preserving system resources.

Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction

“…Issues in ongoing research have been extensively discussed, with initial solutions suggested in [ 4 ]. Context-aware approaches have been proposed previously; for example, [ 5 ]. However, only one [ 6 ] focused on crowd abnormality monitor and mitigation with the use of individual AR.…”

An Intelligent System for Monitoring Skin Diseases

“…The retrieved data may indicate some deviations from the norm in the behavior of the household members. Information can be processed in terms of behavior analysis [ 8 , 9 , 10 ]. There are also very interesting research on sensing technologies for robotics which can assist elderly people in daily routine [ 11 ].…”

Real-Time Elderly Healthcare Monitoring Expert System Using Wireless Sensor Network

“…For the health care systems, this is considered as a big challenge [8], these health care systems need to deal with the health issues of the elderly population [3,[5][6][7][9][10][11][12][13][14][15][16]. In order to develop effective health systems, they must be able to deal with the so-called "four giants" of geriatrics (Isaacs) as: instability, immobility, intellectual impairment and incontinence.…”