Review of Ultra Wide Band (UWB) for Indoor Positioning with application to the elderly

The objective of this review is to analyze Ultra Wide Band (UWB) technology, as an option that allows developing new solutions in indoor positioning systems (IPS), mainly with a approach applied to the elderly. The methodology that has been applied corresponds to the definition of some basics concepts about UWB and some tests in the lab; the above to demonstrate the degree of accuracy that UWB offers compared to other technologies. The findings found and presented in this paper correspond to the identification of UWB as a technology with a high degree of accuracy for IPS; also, that there are other works related to the subject, with application in different areas, but specifically as an application for older people; regarding to the tests, these allowed to verify in the laboratory the operation and accuracy of UWB, for its possible application in IPS. The research described in this paper is the beginning of a implementation in a residence center, where accuracy in location and real-time response are important, in the future we hope make conclusive contributions of the implementations made

A critical analysis of an IoT—aware AAL system for elderly monitoring

Abstract A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions

An IoT-Aware Approach for Elderly-Friendly Cities

The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person

An IoT-aware System for Elderly Monitoring

The aging population is a global phenomenon, characterized by many interesting challenges. In this context, the Internet of Things technologies could allow to analyze the elderly’s behavioral in an unobtrusive way, thus helping to prevent Mild Cognitive Impairment and frailty problems. To this end, this work aims to define a reliable system for controlling the position and the body motility of the elderly in low-cost and low- power way. Movements and body motility are, indeed, good indicators of behavioral changes. The system represents the basis of a complete architecture for behavioral analysis and risk detection developed within the City4Age project, funded by the Horizon 2020 Programme of the European Commission