Steps Toward End-to-End Personalized AAL Services

In Ambient Assisted Living research and development, a significant effort has been dedicated to issues like gathering continuous information at home, standardizing formats in order to create environments more easily, extracting further information from raw data using different techniques to reconstruct context. An aspect relatively less developed but also important is the design of personalized end-to-end services for technology users being them either primary (older people) or secondary (medical doctors, caregiver, relatives). This paper explores an effort, internal to the EU project GIRAFFPLUS, for designing such services starting from a state-of-the-art continuous data gathering infrastructure. The paper presents the general project idea, the current choices for the middleware infrastructure and the pursued direction for a set of services personalized to different classes of users

The OCarePlatform : a context-aware system to support independent living

Background: Currently, healthcare services, such as institutional care facilities, are burdened with an increasing number of elderly people and individuals with chronic illnesses and a decreasing number of competent caregivers. Objectives: To relieve the burden on healthcare services, independent living at home could be facilitated, by offering individuals and their (in)formal caregivers support in their daily care and needs. With the rise of pervasive healthcare, new information technology solutions can assist elderly people ("residents") and their caregivers to allow residents to live independently for as long as possible. Methods: To this end, the OCarePlatform system was designed. This semantic, data-driven and cloud based back-end system facilitates independent living by offering information and knowledge-based services to the resident and his/her (in)formal caregivers. Data and context information are gathered to realize context-aware and personalized services and to support residents in meeting their daily needs. This body of data, originating from heterogeneous data and information sources, is sent to personalized services, where is fused, thus creating an overview of the resident's current situation. Results: The architecture of the OCarePlatform is proposed, which is based on a service-oriented approach, together with its different components and their interactions. The implementation details are presented, together with a running example. A scalability and performance study of the OCarePlatform was performed. The results indicate that the OCarePlatform is able to support a realistic working environment and respond to a trigger in less than 5 seconds. The system is highly dependent on the allocated memory. Conclusion: The data-driven character of the OCarePlatform facilitates easy plug-in of new functionality, enabling the design of personalized, context-aware services. The OCarePlatform leads to better support for elderly people and individuals with chronic illnesses, who live independently. (C) 2016 Elsevier Ireland Ltd. All rights reserved

CONTEXT MANAGEMENT: TOWARD ASSESSING QUALITY OF CONTEXT PARAMETERS IN A UBIQUITOUS AMBIENT ASSISTED LIVING ENVIRONMENT

This paper provides an approach to assessing Quality of Context (QoC) parameters in a ubiquitous Ambient Assisted Living (AAL) environment. Initially, the study presents a literature review on QoC, generating taxonomy. Then it introduces the context management architecture used. The proposal is verified with the Siafu simulator in an AAL scenario where the user’s health is monitored with information about blood pressure, heart rate and body temperature. Considering some parameters, the proposed QoC assessment allows verifying the extent to which the context information is up-to-date, valid, accurate, complete and significant. The implementation of this proposal might mean a big social impact and a technological innovation applied to AAL, at the disposal and support of a significant number of individuals such as elderly or sick people, and with a more precise technology

Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from the user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user's floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Holistic Blockchain Approach to Foster Trust, Privacy and Security in IoT Based Ambient Assisted Living Environment

The application of blockchains techniques in the Internet of Things (IoT) is gaining much attention with new solutions proposed in diverse areas of the IoT. Conventionally IoT systems are designed to follow the centralised paradigm where security and privacy control is vested on a 'trusted' third-party. This design leaves the user at the mercy of a sovereign broker and in addition, susceptible to several attacks. The implicit trust and the inferred reliability of centralised systems have been challenged recently following several privacy violations and personal data breaches. Consequently, there is a call for more secure decentralised systems that allows for finer control of user privacy while providing secure communication. Propitiously, the blockchain holds much promise and may provide the necessary framework for the design of a secure IoT system that guarantees fine-grained user privacy in a trustless manner. In this paper, we propose a holistic blockchain-based decentralised model for Ambient Assisted Living (AAL) environment. The nodes in our proposed model utilize smart contracts to define interaction rules while working collaboratively to contribute storage and computing resources. Based on the blockchain technique, our proposed model promotes trustless interaction and enhanced user's privacy through the blockchain-Interplanetary File System (IPFS) alliance. The proposed model also addresses the shortfall of storage constraints exhibited in many IoT systems

An unsupervised behavioral modeling and alerting system based on passive sensing for elderly care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort

From AAL to ambient assisted rehabilitation: a research pilot protocol based on smart objects and biofeedback

AbstractThe progressive miniaturization of electronic devices and their exponential increase in processing, storage and transmission capabilities, represent key factors of the current digital transformation, also sustaining the great development of Ambient Assisted Living (AAL) and the Internet of Things. Although most of the investigations in the recent years focused on remote monitoring and diagnostics, rehabilitation too could be positively affected by the widespread integrated use of these devices. Smart Objects in particular may be among the enablers to new quantitative approaches. In this paper, we present a proof-of-concept and some preliminary results of an innovative pediatric rehabilitation protocol based on Smart Objects and biofeedback, which we administered to a sample of children with unilateral cerebral palsy. The novelty of the approach mainly consists in placing the sensing device into a common toy (a ball in our protocol) and using the information measured by the device to administer multimedia-enriched type of exercises, more engaging if compared to the usual rehabilitation activities used in clinical settings. We also introduce a couple of performance indexes, which could be helpful for a quantitative continuous evaluation of movements during the exercises. Even if the number of children involved and sessions performed are not suitable to assess any change in the subjects' abilities, nor to derive solid statistical inferences, the novel approach resulted very engaging and enjoyable by all the children participating in the study. Moreover, given the almost non-existent literature on the use of Smart Objects in pediatric rehabilitation, the few qualitative/quantitative results here reported may promote the scientific and clinical discussion regarding AAL solutions in a "Computer Assisted Rehabilitation" perspective, towards what can be defined "Pediatric Rehabilitation 2.0"