A spatial dashboard for Alzheimer's disease in New South Wales

© 2017 The authors and IOS Press. This paper illustrates a proof of concept scenario for the application of comprehensive data visualisation methods in the rapidly changing aged care sector. The scenario we explored is population ageing and the dementias with an emphasis on the spatial effects of change over time at the Statistical Area 2 (SA2) level for the state of New South Wales. We did this using a combination of methods, culminating in the use of the Tableau software environment to explore the intersections of demography, epidemiology and their formal cost of care implications. In addition, we briefly illustrate how key infrastructure data can be included in the same data management context by showing how service providers can be integrated and mapped in conjunction with other analyses. This is an innovative and practical approach to some of the complex issues already faced in the health and aged care sectors which can only become more pronounced as population ageing progresses

Directing and orienting ICT healthcare solutions to address the needs of the aging population

Background: With an aging population, it is essential to maintain good health and autonomy for as long as possible. Instead of hospitalisation or institutionalisation, older people with chronic conditions can be assisted in their own home with numerous “smart” devices that support them in their activities of daily living, manage their medical conditions, and prevent fall incidents. Information and Communication Technology (ICT) solutions facilitate the monitoring and management of older people’s health to improve quality of life and physical activity with a decline in caregivers’ burden. Method: The aim of this paper was to conduct a systematic literature review to analyse the state of the art of ICT solutions for older people with chronic conditions, and the impact of these solutions on their quality of life from a biomedical perspective. Results: By analysing the literature on the available ICT proposals, it is shown that different approaches have been deployed by noticing that the more cross-interventions are merged then the better the results are, but there is still no evidence of the effects of ICT solutions on older people’s health outcomes. Furthermore, there are still unresolved ethical and legal issues. Conclusion: While there has been much research and development in healthcare ICT solutions for the aging population, ICT solutions still need significant development in order to be user-oriented, affordable, and to manage chronic conditions in the aging wider population

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

HUMAN ACTIVITY RECOGNITION IN SMART-HOME ENVIRONMENTS FOR HEALTH-CARE APPLICATIONS

With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This thesis presents several contributions on this topic. The major ones are two novel hybrid ADLs recognition algorithms. The former is supervised while the latter is unsupervised. Preliminary results, which still need to be confirmed, show that the recognition rate of the unsupervised method is comparable to the one obtained by the supervised one, with the great advantage of not requiring the acquisition of an annotated dataset. Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are presented. Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors. Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented. Experimental results on several datasets show the effectiveness of all the proposed techniques

Intelligence artificielle: Les défis actuels et l'action d'Inria - Livre blanc Inria

Livre blanc Inria N°01International audienceInria white papers look at major current challenges in informatics and mathematics and show actions conducted by our project-teams to address these challenges. This document is the first produced by the Strategic Technology Monitoring & Prospective Studies Unit. Thanks to a reactive observation system, this unit plays a lead role in supporting Inria to develop its strategic and scientific orientations. It also enables the institute to anticipate the impact of digital sciences on all social and economic domains. It has been coordinated by Bertrand Braunschweig with contributions from 45 researchers from Inria and from our partners. Special thanks to Peter Sturm for his precise and complete review.Les livres blancs d’Inria examinent les grands défis actuels du numérique et présentent les actions menées par noséquipes-projets pour résoudre ces défis. Ce document est le premier produit par la cellule veille et prospective d’Inria. Cette unité, par l’attention qu’elle porte aux évolutions scientifiques et technologiques, doit jouer un rôle majeur dans la détermination des orientations stratégiques et scientifiques d’Inria. Elle doit également permettre à l’Institut d’anticiper l’impact des sciences du numérique dans tous les domaines sociaux et économiques. Ce livre blanc a été coordonné par Bertrand Braunschweig avec des contributions de 45 chercheurs d’Inria et de ses partenaires. Un grand merci à Peter Sturm pour sa relecture précise et complète. Merci également au service STIP du centre de Saclay – Île-de-France pour la correction finale de la version française

Analyse des mouvements 3D en temps réel pour un dispositif médical destiné au maintien de l'indépendance fonctionnelle des personnes âgées à domicile

We propose in this manuscript a realtime3D movement analysis system for inhomefunctionalabilities assessment in aged adults. As a first step, the purpose is to maintain the functionalindependence of this population and to allow an earlier detection of a motor decompensation inorder to facilitate a rehabilitation process. To quantify the equilibrium quality of a subject, webuilt a system using the Kinect sensor in order to analyze a simple clinical test validated in geriatricrehabilitation: the Timed Up and Go (TUG). Three experiments conducted in heterogeneousenvironments (laboratory, day hospital and home) showed good measurement reliability of theidentified parameters. In particular, they allow to assign a motor control note indicating themotor frailty. Then, we proposed a video processing chain to increase the robustness of theanalysis of the various TUG phases: automatic detection of the sitting posture, patientsegmentation and three body joints extraction. The results of this work allow us to considerseveral perspectives. First, we believe conduct experiments on a larger population in order toconfirm the system reliability. Then, various technical and ergonomic improvements would benecessary to facilitate general public use. Finally, it would be interesting to extend the proposedmethodology for other clinical test to prolong the autonomy at home.Dans ce manuscrit, nous proposons un système d'analyse automatique des mouvements 3D entemps réel permettant l'évaluation des capacités fonctionnelles chez les personnes âgées àdomicile. Dans un premier temps, l'objectif est de contribuer à maintenir l'indépendancefonctionnelle de cette population et permettre une détection précoce d'une décompensationmotrice pour faciliter une démarche de rééducation. Pour quantifier la qualité d'équilibre d'unsujet en temps réel, nous avons conçu un système en utilisant le capteur Kinect et permettantd'analyser un test clinique simple et validé en rééducation gériatrique: le Timed Up and Go (TUG).Trois expériences, réalisées dans des environnements hétérogènes (laboratoire, hôpital de jouret domicile) ont montré une bonne fiabilité de la mesure des paramètres identifiés. Ellespermettent notamment d'attribuer une note de contrôle moteur indiquant la fragilité motrice.Dans un second temps, nous avons proposé une chaîne de traitement vidéo permettantd'augmenter la robustesse d'analyse de différentes phases du TUG : détection automatique de laposition assise, segmentation du patient et extraction de 3 articulations du corps. Les résultats deces travaux nous permettent d'envisager plusieurs perspectives. Tout d'abord, nous pensonseffectuer des expérimentations sur une population plus large afin de confirmer la fiabilité dusystème. Puis, différentes améliorations techniques et ergonomiques seraient nécessaires pourfaciliter l'utilisation grand public. Enfin, il serait intéressant d'étendre la méthodologie proposéepour d'autres tests cliniques en vue de prolonger l'autonomie à domicile

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare