Evolutionary Service Composition and Personalization Ecosystem for Elderly Care

Current demographic trends suggest that people are living longer, while the ageing process entails many necessities, calling for care services tailored to the individual senior’s needs and life style. Personalized provision of care services usually involves a number of stakeholders, including relatives, friends, caregivers, professional assistance organizations, enterprises, and other support entities. Traditional Information and Communication Technology based care and assistance services for the elderly have been mainly focused on the development of isolated and generic services, considering a single service provider, and excessively featuring a techno-centric approach. In contrast, advances on collaborative networks for elderly care suggest the integration of services from multiple providers, encouraging collaboration as a way to provide better personalized services. This approach requires a support system to manage the personalization process and allow ranking the {service, provider} pairs. An additional issue is the problem of service evolution, as individual’s care needs are not static over time. Consequently, the care services need to evolve accordingly to keep the elderly’s requirements satisfied. In accordance with these requirements, an Elderly Care Ecosystem (ECE) framework, a Service Composition and Personalization Environment (SCoPE), and a Service Evolution Environment (SEvol) are proposed. The ECE framework provides the context for the personalization and evolution methods. The SCoPE method is based on the match between the customer´s profile and the available {service, provider} pairs to identify suitable services and corresponding providers to attend the needs. SEvol is a method to build an adaptive and evolutionary system based on the MAPE-K methodology supporting the solution evolution to cope with the elderly's new life stages. To demonstrate the feasibility, utility and applicability of SCoPE and SEvol, a number of methods and algorithms are presented, and illustrative scenarios are introduced in which {service, provider} pairs are ranked based on a multidimensional assessment method. Composition strategies are based on customer’s profile and requirements, and the evolutionary solution is determined considering customer’s inputs and evolution plans. For the ECE evaluation process the following steps are adopted: (i) feature selection and software prototype development; (ii) detailing the ECE framework validation based on applicability and utility parameters; (iii) development of a case study illustrating a typical scenario involving an elderly and her care needs; and (iv) performing a survey based on a modified version of the technology acceptance model (TAM), considering three contexts: Technological, Organizational and Collaborative environment

Provision of Personalized Services in Collaborative Environment

We have been witnessing in the past few years a change in the social paradigm, moving, in just a few dozen years, from a population that used to live less years into one that has increased greatly its life expectancy. This change will imply that the reality of the existing services is also changing, with a much deeper attention being drawn into elderly and their needs. Because each person is unique and has specific needs and desires, it is very difficult that a single service or service provider can provide this solution in a satisfying manner. So, concepts like cooperation and collaboration, as well as service ecosystems become very important in the answer to the new social needs, becoming possible to provide ad-equate services to every elderly. The present work is integrated in a PhD research work, in which an ecosystem was conceptualized. That ecosystem has as inputs information regarding elderly people, their care needs, services they require, and the service providers. This ecosystem is man-aged by an ecosystem manager whose responsibility is to gather the information from the different sources and later, upon request, to run the algorithm to find the most ap-propriate solution for an elderly. This work’s goal is to develop an algorithm – named Service Composition and Personalization Environment (SCoPE) - which will evaluate the elderly request and the information that exists regarding the different services and service providers and try to provide several options to that elderly, according to pre-defined criteria. This algorithm is the ecosystem final part. The answers provided by the ecosystem’s algorithm depend, at each moment, on the information that the ecosystem has. This means that, in case the elderly changes its needs, or in case of the appearance of new services or service providers, the algorithm’s answer may be different. Not only that but also the solutions provided by the algorithm are not meant to be taken as definitive answers for the elderly but as suggestions, so that the elderly may make an informed decision. In conclusion, the developed work demonstrates that the proposed algorithm can provide consistent results to the ecosystem

On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

The simplicity project: easing the burden of using complex and heterogeneous ICT devices and services

As of today, to exploit the variety of different "services", users need to configure each of their devices by using different procedures and need to explicitly select among heterogeneous access technologies and protocols. In addition to that, users are authenticated and charged by different means. The lack of implicit human computer interaction, context-awareness and standardisation places an enormous burden of complexity on the shoulders of the final users. The IST-Simplicity project aims at leveraging such problems by: i) automatically creating and customizing a user communication space; ii) adapting services to user terminal characteristics and to users preferences; iii) orchestrating network capabilities. The aim of this paper is to present the technical framework of the IST-Simplicity project. This paper is a thorough analysis and qualitative evaluation of the different technologies, standards and works presented in the literature related to the Simplicity system to be developed

Service Tailoring: Towards Personalized homecare Systems

Health monitoring and healthcare provisioning for the elderly at home have received increasingly attention. Since each elderly person is unique, with a unique lifestyle, living environment and health condition, personalization is an essential feature of homecare software services. Service tailoring, which is creating a new service to meet individual requirements may be achieved in a cost-effective and time-efficient manner if new services can be configured and composed from already existing services. In this paper, we propose an effective service tailoring process and architecture to personalize homecare services according to the individual care-receiver’s needs. In addition, we present a scenario to highlight the need for service tailoring and to demonstrate the feasibility of the proposed approach

A Universalist strategy for the design of Assistive Technology

Assistive Technologies are specialized products aiming to partly compensate for the loss of autonomy experienced by disabled people. Because they address special needs in a highly-segmented market, they are often considered as niche products. To improve their design and make them tend to Universality, we propose the EMFASIS framework (Extended Modularity, Functional Accessibility, and Social Integration Strategy). We first elaborate on how this strategy conciliates niche and Universalist views, which may appear conflicting at first sight. We then present three examples illustrating its application for designing Assistive Technologies: the design of an overbed table, an upper-limb powered orthose and a powered wheelchair. We conclude on the expected outcomes of our strategy for the social integration and participation of disabled people

Personalized Service Creation by Non-technical Users in the Homecare Domain

AbstractOne of the conditions for the successful introduction of ICT-based homecare services is to allow non-technical persons such as home nurses to personalize these services. We refer to this process of homecare service personalization as service tailoring. Service tailoring can be done by configuring and composing previously developed and deployed service building blocks. In this paper, we describe an approach that employs predefined information of care-receivers, called user profile, to hide most of the technical details from care-givers who do the service tailoring. First, we define the information to be included in a user profile and patterns that represent composition structures corresponding to common homecare tasks experienced in homecare. Then, we define how the service tailoring process can exploit information contained in the predefined user profiles. After that, we illustrate the approach with a tailoring scenario