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

Abstract : 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
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Submitted on : Wednesday, April 13, 2016 - 5:23:38 PM
Last modification on : Wednesday, February 2, 2022 - 11:06:02 PM
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Hamid Medjahed, Dan Istrate, Jérôme Boudy, Jean-Louis Baldinger, Bernadette Dorizzi. A pervasive multi-sensor data fusion for smart home healthcare monitoring. FUZZ 2011 : IEEE International Conference on Fuzzy Systems, Jun 2011, Taipei, Taiwan. pp.1466 - 1473, ⟨10.1109/FUZZY.2011.6007636⟩. ⟨hal-01302225⟩



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