Jérôme Boudy

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.

Sound Environment Analysis in Smart Home

This study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system.

A Dynamic Evidential Network for Fall Detection

This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.

AGACY Monitoring: A Hybrid Model for Activity Recognition and Uncertainty Handling

Acquiring an ongoing human activity from raw sensor data is a challenging problem in pervasive systems. Earlier, research in this field has mainly adopted data-driven or knowledge based techniques for the activity recognition, however these techniques suffer from a number of drawbacks. Therefore, recent works have proposed a combination of these techniques. Nevertheless, they still do not handle sensor data uncertainty. In this paper, we propose a new hybrid model called AGACY Monitoring to cope with the uncertain nature of the sensor data. Moreover, we present a new algorithm to infer the activity instances by exploiting the obtained uncertainty values. The experimental evaluation of AGACY Monitoring with a large real-world dataset has proved the viability and efficiency of our solution.

Adaptive Tracking Model in the Framework of Medical Nursing Home Using Infrared Sensors

On Internet of Things (IoT), everything can be accessed anytime, anywhere, and works without human intervention. In IoT everything collaborates to deliver services and applications to users, Ambient Assisted Living (AAL) being one of these applications. Some AAL smart homes uses infrared sensors to recognize some activities of daily living and to track people along the environment. Location tracking is vital in Ambient Assisted living and can be a useful information to improve AAL systems. A common problem in such systems is that each tracking model is based on a specific sensors placing architecture. In order to assure that the system will work properly, the model has to be fitted by an expert. Modeling is usually costly and it relies on a specific architecture. In our previous work, the tracking model needed to be fitted manually. In order to introduce adaptability, this work proposes an approach to automatically fit the model avoiding the need of an expert to fit a different model for each kind of sensors placing architecture. The proposed approach was evaluated using real data from a set of pyroelectric infrared sensors and a set of scenarios performed in a simulated apartment.

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 Multi-lingual Evaluation of the vAssist Spoken Dialog System. Comparing Disco and RavenClaw

vAssist (Voice Controlled Assistive Care and Communication Services for the Home) is a European project for which several research institutes and companies have been working on the development of adapted spoken interfaces to support home care and communication services. This paper describes the spoken dialog system that has been built. Its natural language understanding module includes a novel reference resolver and it introduces a new hierarchical paradigm to model dialog tasks. The user-centered approach applied to the whole development process led to the setup of several experiment sessions with real users. Multilingual experiments carried out in Austria, France and Spain are described along with their analyses and results in terms of both system performance and user experience. An additional experimental comparison of the RavenClaw and Disco-LFF dialog managers built into the vAssist spoken dialog system highlighted similar performance and user acceptance.

First steps in adaptation of an Evidential Network for data fusion in the framework of medical remote monitoring

This paper presents a medical remote monitoring application which aims at detecting falls. The detection system is based on three modalities: a wearable sensor, infrared sensors and a sound analysis module. The sound analysis is presented briefly. The multimodal fusion is made using the Dempster Schaffer theory through Evidential Network. A first evaluation of the use of data mining techniques in order to extract blindly data representatives is proposed. These representatives are used to continuously increase the system performances. The system is evaluated on a local recorded data base.

Behavior and Capability Based Access Control Model for Personalized TeleHealthCare Assistance

With the growing proportion of dependant people (ageing, disabled users), Tele-assistance and Tele-monitoring platforms will play a significant role to deliver an efficient and less-costly remote care in their assistive living environments. Sensor based technology would greatly contribute to get valuable information which should help to provide personalized access to the services available within their living spaces. However, current access control models remain unsuitable due to the lack of completeness, flexibility and adaptability to the user profile. In this paper, we propose a new access control model based on the user capabilities and behavior. This model is evaluated using the data sensed from our tele-monitoring platform in order to assist automatically the dependent people according to the occurred situation. The design of our model is a dynamic ontology and evolving security policy according to the access rules that are used in the inference engine to provide the right service according to the user’s needs. Our security policy reacts according to the detected distress situation derived from the data combination of both the wearable devices and the pervasive sensors. The security policy is managed through the classification and reasoning process. Our classification process aims to extract the behavior patterns which are obtained by mining the data set issued from our Tele-monitoring platform according to the discriminating attributes: fall, posture, movement, time, user presence, pulse and emergency call. Our reasoning process aims to explore the recognized context and the extracted behavior patterns which set up the rule engine to infer the right decision security policy.

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.