A pervasive multi-sensor data fusion for smart home healthcare monitoring

Abstract

Abstract-Today elderly people are the fastest growing segment of the developing countries population, and they desire to live as independently as possible. But independent lifestyles come with risks and challenges. Medical in-home telemonitoring (and, more generally, telemedicine) is a solution to deal with these challenges and to ensure that elderly people can live safely and independently in their own homes for as long as possible. In this context we propose an automatic in-home healthcare monitoring system for several uses and to meet the needs identified above. The proposed telemonitoring system is a multimodal platform with several sensors that can be installed at home and enables us to have a full and tightly controlled universe of data sets. It integrates elderly physiological and behavioral data, the acoustical environment of the elderly, environmental conditions and medical knowledge. Each modality is processed and analyzed by specific algorithms. A data fusion approach based on fuzzy logic with a set of rules directed by medical recommendations, is used to fuse the various subsystem outputs. This multimodal fusion increases the reliability of the whole system by detecting several distress situations. In fact this fusion approach takes into account temporary sensor malfunction and increases the system reliability and the robustness in the case of environmental disturbances or material limits (Battery, RF range, etc.). The Fuzzy logic fusion methods brings high flexibility to the telemonitoring platform especially in combining modalities or adding other sensors. The proposed telemonitoring system will ensure pervasive in-home health monitoring for elderly people.

Evaluation of a monitoring system for event recognition of older people

“…The approach uses sensors such as actimeter, microphones, passive infrared sensors, and door contacts whose outputs are fused by an SVM classifier. Medjahed et al [4] have proposed a similar approach relying on infrared, change state, audio, and physiological sensors; but the combination of sensor events is performed using a Fuzzy inference approach.…”

A Review of Smart House Analysis Methods for Assisting Older People Living Alone

“…Nevertheless, a drawback is that sometimes neural networks are not suitable for working in environments containing a dynamic sensor configuration. This is because each sensor in the ANN requires a singular input node, and also training is required for the configuration of each sensor set [99]. Another drawback is that ANN requires users to define in advance several learning parameter [20].…”

“…Medjahed et al [99] used fuzzy logic in a pervasive multi-sensor environment for a Smart House health-care monitoring system. The system consisted of a multimodal platform with several sensors installed throughout the house to collect data.…”

A tailored smart home for dementia care

“…There are multiple approaches (e.g., probability theory and fuzzy logic) for a system to cope with uncertainty. The most common way is to employ fuzzy logic (Zadeh 1965;Medjahed et al 2011;Doctor et al 2014). Using fuzzy logic, users can define rules that have premises with linguistic variables.…”