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Inférence bayésienne pour la détection des activités de la vie quotidienne pour faciliter le maintien à domicile des personnes âgées

Yassine El Khadiri 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : The increase of the senior population constitutes a major public health issue. The demographic share of the elderly is ever more growing thanks to the progress and advances in medicine and our health care systems. However, with the aging of this population comes a plethora of dependency problems, and this, of course, exponentially.Retirement homes are an expensive and not very popular solution. As a result, we are seeing a surge in home assisted living solutions in the recent years.This topic is in the crossroads between sensor technologies, data transmission, assistance to elderly people and activity monitoring.This thesis explores the application of data analysis algorithms for activity monitoring of elderly people at home. The idea is that with day-to-day monitoring of residents it is possible to infer their autonomy and capacity to perform day-to-day tasks. It also allows caregivers to intervene in cases where the start of some degradation is detected.We explored and adapted some Bayesian inference and time series segmentation methods for activity recognition. And then, we proposed a visualization tool to facilitate the detection of anomalies or changes in everyday habits.This work is part of a CIFRE thesis. The methods and algorithms presented have been put into production and are packaged into Diatelic's the assisted living commercial solution.
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Submitted on : Tuesday, January 25, 2022 - 2:18:06 PM
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Yassine El Khadiri. Inférence bayésienne pour la détection des activités de la vie quotidienne pour faciliter le maintien à domicile des personnes âgées. Informatique [cs]. Université de Lorraine, 2021. Français. ⟨NNT : 2021LORR0251⟩. ⟨tel-03542586⟩

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