RESUMEN
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.
Asunto(s)
Accidentes por Caídas/prevención & control , Actigrafía/métodos , Algoritmos , Inteligencia Artificial , Minería de Datos/métodos , Monitoreo Ambulatorio/métodos , Telemedicina/métodos , Accidentes por Caídas/estadística & datos numéricos , Sistemas de Apoyo a Decisiones Clínicas , HumanosRESUMEN
Thispaper describes an algorithm for ischemia monitoring in ambulatory electrocardiography. It relies on an automatic ST-segment anlysis. Orecise information about ST segment changes are provided by an original hidden Markov models (HMM) aproach for on line beat segmentation. The performance was evaluated on the two-channel European ST T database, according to its ST episode definitions ...