Toufik Guettari

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.

Design and first evaluation of a sleep characterization monitoring system using remote contactless sensor.

This paper presents the design and a first evaluation of a new monitoring system based on contactless sensors to estimate sleep quality. This sensor produces thermal signals which have been used, at first, to detect a human presence in the bed and then to estimate sleep quality. To distinguish between different sleep phases, we have used methods of signal processing in order to extract the necessary features for learning an adapted statistical model. The existing monitoring systems use sensors attached to the bed or worn by the person. We propose in this paper a system based on a passive thermal sensor which has the advantage of being fixed on the wall; thus, it is easier to use and more reliable. We explain different signal processing steps and describe sleep stage recognition algorithms. We propose an adaptation of the SAX method for the thermal signal. Finally, we evaluate our system in comparison with a polysomnographic recording system in the Hospital (CHU) of Limoges.

Thermal-Signature-Based Sleep Analysis Sensor

This paper addresses the development of a new technique in the sleep analysis domain. Sleep is defined as a periodic physiological state during which vigilance is suspended and reactivity to external stimulations diminished. We sleep on average between six and nine hours per night and our sleep is composed of four to six cycles of about 90 min each. Each of these cycles is composed of a succession of several stages of sleep that vary in depth. Analysis of sleep is usually done via polysomnography. This examination consists of recording, among other things, electrical cerebral activity by electroencephalography (EEG), ocular movements by electrooculography (EOG), and chin muscle tone by electromyography (EMG). Recordings are made mostly in a hospital, more specifically in a service for monitoring the pathologies related to sleep. The readings are then interpreted manually by an expert to generate a hypnogram, a curve showing the succession of sleep stages during the night in 30s epochs. The proposed method is based on the follow-up of the thermal signature that makes it possible to classify the activity into three classes: “awakening,” “calm sleep,” and “restless sleep”. The contribution of this non-invasive method is part of the screening of sleep disorders, to be validated by a more complete analysis of the sleep. The measure provided by this new system, based on temperature monitoring (patient and ambient), aims to be integrated into the tele-medicine platform developed within the framework of the Smart-EEG project by the SYEL–SYstemes ELectroniques team. Analysis of the data collected during the first surveys carried out with this method showed a correlation between thermal signature and activity during sleep. The advantage of this method lies in its simplicity and the possibility of carrying out measurements of activity during sleep and without direct contact with the patient at home or hospitals.