During the recent years, several new Information and Communication Technology solutions have been developed in order to meet the increasing needs of elderly with cognitive impairments and support their autonomous living. Most of these solutions follow a human-centred paradigm that aims to provide users with personalised services according to their needs by also ensuring their safety with mechanisms that can automatically trigger appropriate actions in situations where there may be a risk for an elderly. The present paper presents a hybrid matchmaking approach that uses efficiently both a rule-based and a statistical matchmaker in order to (a) propose ambient assisted living services to the end-users, based on their role, status and context of use and (b) identify and resolve problematic cases by automatically selecting the most proper set of services to be called in a single or combined manner.
Although the healthcare sector has been hugely benefited from the advantages made in the Information and Communication Technology (ICT) domain in the recent years, the emerging technology breakthrough of the Internet-of-Things (IoT), in which all devices and services are collaborating while reducing human intervention, promises new solutions that will enable users to have a more home-centric healthcare, and a sustainable active and healthy ageing. This paper is proposing a smart-home IoT infrastructure for the support and extension of the independent living of older adults in their living environments that responds also to real needs of caregivers and public authorities. The proposed infrastructure seamlessly utilizes health and monitoring devices for the provision of a safe environment for an elderly, the mitigation of frailty and the preservation of quality of life and autonomy. It also provides a mechanism for easy setup and testing of the installed equipment and a decision support system that offers advanced data analytics and visual analytics mechanisms to the formal and informal caregivers of the elderly for the efficient monitoring of their health and activity status.
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