An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

Top-Down Delivery of IoT-based Applications for Seniors Behavior Change Capturing Exploiting a Model-Driven Approach

Developing Internet of Things (IoT) requires expertise and considerable skills in different fields in order to cover all the involved heterogeneous technologies, communication formats and protocols. Developers and experts ask for new solutions that speed up the prototyping of IoT applications. One of these solutions is Web of Topics (WoX) middleware, a model-driven Cloud platform that aims to ease IoT applications developing, introducing a strong semantic abstraction of the IoT concepts. In WoX, almost all the IoT entities and concepts are limited to the concept of Topic, i.e. an entity containing the value of a feature of interest that we intend to detect. The local counterpart of WoX is L-WoX (Local-Web of Topics), which manages local instances of features of interest, allowing mobile applications to collaborate among them, offering and receiving data to/from smart objects, and enabling the communication with WoX Cloud platform. The presented study leverages WoX approach for showing an experience in rapid design and prototyping of an ambient assisted living system that detects the movements of elderly persons in their home, acquiring data through sensors in an unobtrusive way. Moreover, The paper shows that the chosen model-driven solution is very suitable in a top-down approach, starting from users requirements: the created system simplifies the user-centered design of IoT applications, adopting a full top-down approach from user required to the technological solution

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 innovative AAL system based on neural networks and IoT-aware technologies to improve the quality of life in elderly people

Nowadays more and more elderly people need support in daily activities. This is due to the increase of cognitive diseases and other conditions which lead the elderly to not being self-sufficient. Considering this, providing an Ambient Assisted Living system could improve significantly people life quality and could support caregivers' tasks. The combination of Ambient Assisted Living systems and information and communication technologies achieve this purpose perfectly. They exploit internet of things and artificial intelligence paradigms to make daily challenges easier for people with neurodegenerative diseases. This work melds technologies mentioned above providing a smart system for elderly to manage goods and fill in shopping lists. It was possible using software, hardware, and cloud systems combined with a neural network aimed to recognise products. The proposed system has been validated both from a functional point of view through a proof-of-concept and quantitatively by a performance analysis of its components

Capturing Behavioral Changes of Elderly people through Unobtruisive Sensing Technologies

The behavioral analysis of individuals is an important science, especially if it is conducted on the elderly population, aiming to prevent Mild Cognitive Impairment (MCI) and frailty problems. A fundamental aspect in this context is to explore the use of innovative technologies enabling the Internet of Things (IoT), above all sensors, to unobtrusively capture personal data for automatically recognizing behavioral changes in elderly people. This is done with the aim to timely identify risks of MCI and frailty before they escalate into more serious conditions such as Alzheimer Disease. This paper aims to briefly describe the overall goal of the City4Age project, funded by the Horizon 2020 Programme of the European Commission, in particular focusing on the IoT-based personal data capturing system