Hamid Medjahed

Human activities of daily living recognition using fuzzy logic for elderly home monitoring

Learning and recognizing human activities of daily living(ADL), is very useful and essential to build a pervasive home monitoring system. These monitoring technologies are indispensable for developing the next generation of smart houses. In this paper we describe a fuzzy logic system for recognizing activities in home environment using a set of sensors: physiological sensors (cardiac frequency, activity or agitation, posture and fall detection sensor), microphones, infrared sensors, debit sensors and state-change sensors. Motivated by the fact that Fuzzy controllers have been successfully embedded within billions of dollars in commercial products, plus the characteristic of data providing from each sensor, the fusion of the different sensors has been performed by using fuzzy logic. This fuzzy logic approach allowed us to recognize several Activities of Daily Living (ADLs) for Ubiquitous Healthcare.

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

Today elderly people are the fastest growing segment of the population in developed countries, 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.

A Fuzzy Logic Approach for Remote Healthcare Monitoring by Learning and Recognizing Human Activities of Daily Living

Improvement of life quality in the developed nations has systematically generated an increase in the life expectancy. A statistic studies curried out by the French national institute of statistic and economic studies (INSEE) shows a new distribution of age classes in France. In fact, almost one in three people will be over 60 years in 2050, against one in five in 2005, and France will have over 10 million of people over 75 years and over 4 million of people over 85 years. Nevertheless, the increasing number of elderly person implies more resources for aftercare, paramedical care and natural assistance in their habitats. The current healthcare infrastructure in those countries is widely considered to be inadequate to meet the needs of this increasingly older population. In this case a permanent assistance is necessary wherever they are, healthcare monitoring is a solution to deal with this problem and ensure the elderly to live safely and independently in their own home for as long as possible.

A MULTIMODAL PLATFORM FOR DATABASE RECORDING AND ELDERLY PEOPLE MONITORING

This paper describes a new platform for monitoring elderly people living alone. An architecture is proposed that includes three subsystems, with various types of sensors for different sensing modalities incorporated into a smart house. The originality of this system is the combination and the synchronization of three different televigilance modalities for acquiring and recording data. The paper focuses on the acquisition step of the system, usage and point out possibilities for future work

Multimodal localization in the context of a medical telemonitoring system

This paper addresses a localization system which is based on a combination of information from two modalities: a Smart Home Person Tracking (SHPT) composed of infrared sensors and an Audio Person Tracking (APT) which uses microphones able to estimate azimuth of acoustic sources. This combination improves precision of localization compared to a standalone or separated module. The localization software facilitates the integration of both SHPT and APT systems, to display the position in real time, to record data and detect some distress situations (some kind of fall). Results on implementation show good adaptation for Smart Home environments and a robust detection.

Evidential Network-Based Multimodal Fusion for Fall Detection

The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and Evidence theories such as Dempster-Shafer Theory (DST) are commonly used to handle the degree of uncertainty in the fusion processes. Based on a graphic representation of the DST called Evidential Networks, we propose a structure of heterogeneous multi-sensor fusion for falls detection. The proposed Evidential Network (EN) can handle the uncertainty present in a mobile and a fixed sensor-based remote monitoring systems (fall detection) by fusing them and therefore increasing the fall detection sensitivity compared to the a separated system alone.

A multimodal database for a home remote medical care application

The home remote monitoring systems aim to make a protective contribution to the well being of individuals (patients, elderly persons) requiring moderate amounts of support for independent living spaces, and improving their everyday life. Existing researches of these systems suffer from lack of experimental data and a standard medical database intended for their validation and improvement. This paper presents a multi-sensors environment for acquiring and recording a multimodal medical database, which includes physiological data (cardiac frequency, activity or agitation, posture, fall), environment sounds and localization data. It provides graphical interface functions to manage, process and index these data. The paper focuses on the system implementation, its usage and it points out possibilities for future work.

Heterogeneous multi-sensor fusion based on an evidential network for fall detection

The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and evidence theories such as Dempster-Shafer Theory (DST), are commonly used to handle the degree of uncertainty in the fusion processes. Based on a graphic representation of the DST called evidential networks, we propose a structure of heterogeneous multisensor fusion for falls detection. The proposed Evidential Network (EN) can handle the uncertainty present in a mobile and a fixed sensor-based remote monitoring systems (fall detection) by fusing them and therefore increasing the fall detection sensitivity compared to the a separated alone system.