CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Predicting Depression Using Social Media Posts

The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental healthparticularly depression where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all of the information about a person's mood and negativism can be gather from their SNS user profile. Therefore, this study utilize SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models are evaluated to classify the UGC which are : Decision Forest, Neural Network and Support Vector Machine (SVM). The resuls shows that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model which is 78.27% and 0.042 but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best models for making predictions to determine the level of depression by using social media posts

Ambient assisted living deployment aims to empower people living with dementia (AnAbEL)

Ambient Assisted Living aims to support the wellbeing of people with special needs by offering assistive solutions. Those systems focused on dementia claim to increase the autonomy of people living with dementia by monitoring their activities. Thus, topics such as Activity Recognition related to dementia and specific solutions such as reminders and tracking users by Global Positioning System offer great advances that seek users' safety and to preserve their healthier lifestyle. However, these solutions address secondary parties by providing useful activities logs or alerts but excluding the main interested user: the person living with dementia. Although primary users are taken into consideration at some design stages by using user-centred design frameworks, final products tend not to fully address the user's needs. This paper presents an Ambient Intelligent system aimed to reduce this limitation by developing a final solution more strongly focused on enhancing a healthy lifestyle by empowering the user's autonomy. Through continued activities monitoring in real-time, the system can provide reminders to the users by coaching them to keep healthy routines. Continuous monitoring also provides a complete user's behaviour tracking and the context-awareness logic used involves the caregivers through alerts when necessary to ensure the user's safety. This article describes the process followed to develop the system aimed to cover the previous concerns and the practical feedback from health professionals over the system deployment working in a real environment

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Active aging in place supported by caregiver-centered modular low-cost platform

Aging in place happens when people age in the residence of their choice, usually their homes because is their preference for living as long as possible. This research work is focused on the conceptualization and implementation of a platform to support active aging in place with a particular focus on the caregivers and their requirements to accomplish their tasks with comfort and supervision. An engagement dimension is also a plus provided by the platform since it supports modules to make people react to challenges, stimulating them to be naturally more active. The platform is supported by IoT, using low-cost technology to increment the platform modularly. Is a modular platform capable of responding to specific needs of seniors aging in place and their caregivers, obtaining data regarding the person under supervision, as well as providing conditions for constant and more effective monitoring, through modules and tools that support decision making and tasks realization for active living. The constant monitoring allows knowing the routine of daily activities of the senior. The use of machine learning techniques allows the platform to identify, in real-time, situations of potential risk, allowing to trigger triage processes with the older adult, and consequently trigger the necessary actions so that the caregiver can intervene in useful time.O envelhecimento no local acontece quando as pessoas envelhecem na residência da sua escolha, geralmente nas suas próprias casas porque é a sua preferência para viver o máximo de tempo possível. Este trabalho de investigação foca-se na conceptualização e implementação de uma plataforma de apoio ao envelhecimento ativo no local, com particular enfoque nos cuidadores e nas suas necessidades para cumprir as suas tarefas com conforto e supervisão. Uma dimensão de engajamento também é um diferencial da plataforma, pois esta integra módulos de desafios para fazer as pessoas reagirem aos mesmos, estimulando-as a serem naturalmente mais ativas. A plataforma é suportada por IoT, utilizando tecnologia de baixo custo para incrementar a plataforma de forma modular. É uma plataforma modular capaz de responder às necessidades específicas do envelhecimento dos idosos no local e dos seus cuidadores, obtendo dados relativos à pessoa sob supervisão, bem como fornecendo condições para um acompanhamento constante e mais eficaz, através de módulos e ferramentas que apoiam a tomada de decisões e realização de tarefas para a vida ativa. A monitorização constante permite conhecer a rotina das atividades diárias do idoso, permitindo que, com a utilização de técnicas de machine learning, a plataforma seja capaz de detetar em tempo real situações de risco potencial, permitindo desencadear um processo de triagem junto do idoso, e consequentemente despoletar as ações necessárias para que o prestador de cuidados possa intervir em tempo útil

A methodology for the design and evaluation of user interfaces for interactive information systems

The definition of proposed research addressing the development and validation of a methodology for the design and evaluation of user interfaces for interactive information systems is given. The major objectives of this research are: the development of a comprehensive, objective, and generalizable methodology for the design and evaluation of user interfaces for information systems; the development of equations and/or analytical models to characterize user behavior and the performance of a designed interface; the design of a prototype system for the development and administration of user interfaces; and the design and use of controlled experiments to support the research and test/validate the proposed methodology. The proposed design methodology views the user interface as a virtual machine composed of three layers: an interactive layer, a dialogue manager layer, and an application interface layer. A command language model of user system interactions is presented because of its inherent simplicity and structured approach based on interaction events. All interaction events have a common structure based on common generic elements necessary for a successful dialogue. It is shown that, using this model, various types of interfaces could be designed and implemented to accommodate various categories of users. The implementation methodology is discussed in terms of how to store and organize the information