Abstract
Ambient Assisted Living is nowadays necessary to support people with special needs in performing their activities of daily living, but it remains unaltered in front of the necessity to accompany aging and dependent people in their outdoors activities. Moreover, the development of multiple frameworks during the last decade has mainly focused on the engineering dimension neglecting impact of human factors and social needs in the design process. New technologies, such as cloud computing and Internet of Things (IoT) could bring new capabilities to this field of research allowing systems to process human condition following usage oriented models (e.g. frailty) in a non-invasive approach. This thesis proposes to consider a new paradigm in assistive technologies for aging and wellbeing by introducing (i) human frailty metrics, and (ii) urban dimension in an ambient assistive framework (extending the living space from indoors to outdoors). It proposes a cloud-based framework for seamless communication with connected objects, allowing the integrated system to compute and to model different levels of human frailty based on several frailty standardized items, and leveraged from an extensive literature and frameworks reviews.This thesis aims at designing and developing an integrated cloud-based framework, which would be able to communicate with heterogeneous real-time non-invasive indoor sensors (e.g. motion, contact, fiber optic) and outdoors (e.g. BLE Beacons, smartphone). The framework stores the raw data and processes it through a designed hybrid reasoning engine combining both approaches, data driven (machine learning), and knowledge driven (semantic reasoning) algorithms, to (i) infer the activities of the daily living (ADL), (ii) detect changes of human behavior, and ultimately (iii) calibrate human frailty values. It also includes a human behavior mobility classifier that uses the inner smartphone sensors to classify the type of movement performed by the individual (e.g. Walk, Cycling, MRT, Bus, Car). The frailty values might allow the system to automatically detect any change of behaviors, or abnormal situations, which might lead to a risk at home or outside.The proposed models and framework have been developed in close collaboration with IPAL and LIRMM research teams. They also have been assessed in real conditions involving end-users and caregivers through different pilots sites in Singapore and in France. Nowadays, the proposed framework, is currently deployed in a real world deployment in 24 individual homes. 14 spaces are located in France (5 privates rooms in nursing home and 9 private houses) in collaboration with a nursing home (Argentan-Normandie and Montpellier). 10 individual homes are located in Singapore in collaboration with a Senior Activity Center (non-profit organization).The long-term ambition is to detect and intervene to avoid a risk even before a medical doctor detects it during a consultation. The ultimate goal is to promote prevention paradigm for health and wellbeing. The obtained data has been analyzed and published in multiple international conferences and journals.