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Technological Approach for Early and Unobtrusive Detection of Possible Health Changes toward Better Adaptation of Services for Elderly People

Abstract : Aging process is associated with serious decline in physical and cognitive abilities. Aging-related health problems present growing burden on public health and economy. Nowadays, existing geriatric services have limitations in terms of early detecting possible health changes toward better adaptation of medical assessment and intervention for elderly people. Bridging the gap between these geriatric needs and existing services is a major enabler to improve their impact. In this thesis, proposed technological approach employs unobtrusiveInternet of Things (IoT) technologies for long-term behavior monitoring and early detection of possible changes. Proposed methodology identifies geriatric indicators that can be monitored via unobtrusive IoT technologies, and are associated with physical and cognitive problems. This thesis develops data processing algorithms that convert raw sensor data into geriatric indicators. These geriatric indicators are analyzed on a daily basis, in order to early detect possible changes. This thesis evaluates and adapts further statistical, probabilistic and machine-learning techniques for long-term change detection. Adapting these techniques discards transient deviations, and retains permanent changes in monitored behavior. Real 3-year deployments in nursing home and individual houses validate proposed approach. Medical clinic geriatrician and nursing home team validate medical relevance of detected changes.
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Firas Kaddachi. Technological Approach for Early and Unobtrusive Detection of Possible Health Changes toward Better Adaptation of Services for Elderly People. Other [cs.OH]. Université Montpellier, 2018. English. ⟨NNT : 2018MONTS047⟩. ⟨tel-02138134⟩

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