Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A Methodology for Trustworthy IoT in Healthcare-Related Environments

The transition to the so-called retirement years, comes with the freedom to pursue old passions and hobbies that were not possible to do in the past busy life. Unfortunately, that freedom does not come alone, as the previous young years are gone, and the body starts to feel the time that passed. The necessity to adapt elder way of living, grows as they become more prone to health problems. Often, the solution for the attention required by the elders is nursing homes, or similar, that take away their so cherished independence. IoT has the great potential to help elder citizens stay healthier at home, since it has the possibility to connect and create non-intrusive systems capable of interpreting data and act accordingly. With that capability, comes the responsibility to ensure that the collected data is reliable and trustworthy, as human wellbeing may rely on it. Addressing this uncertainty is the motivation for the presented work. The proposed methodology to reduce this uncertainty and increase confidence relies on a data fusion and a redundancy approach, using a sensor set. Since the scope of wellbeing environment is wide, this thesis focuses its proof of concept on the detection of falls inside home environments, through an android app using an accelerometer sensor and a micro- phone. The experimental results demonstrates that the implemented system has more than 80% of reliable performance and can provide trustworthy results. Currently the app is being tested also in the frame of the European Union projects Smart4Health and Smart Bear.A transição para os chamados anos de reforma, vem com a liberdade de perseguir velhas pai- xões e passatempos que na passada vida ocupada não eram possíveis de realizar. Infelizmente, essa liberdade não vem sozinha, uma vez que os anos jovens anteriores terminaram, e o corpo começa a sentir o tempo que passou. A necessidade de adaptar o modo de vida dos menos jovens, cresce à medida que estes se tornam mais propensos a problemas de saúde. Muitas vezes, a solução para a atenção que os mais idosos necessitam são os lares de idosos, ou similares, que lhes tiram a tão querida independência. IoT tem o grande potencial de ajudar os cidadãos idosos a permanecerem mais saudá- veis em casa, uma vez que tem a possibilidade de se ligar e criar sistemas não intrusivos capa- zes de interpretar dados e agir em conformidade. Com essa capacidade, vem a responsabili- dade de assegurar que os dados recolhidos são fiáveis e de confiança, uma vez que o bem- estar humano possa depender dos mesmos. Abordar esta incerteza é a motivação para o tra- balho apresentado. A metodologia proposta para reduzir esta incerteza e aumentar a confiança no sistema baseia-se numa fusão de dados e numa abordagem de redundância, utilizando um conjunto de sensores. Uma vez que o assunto de bem-estar e saúde é vasto, esta tese concentra a sua prova de conceito na deteção de quedas dentro de ambientes domésticos, através de uma aplicação android, utilizando um sensor de acelerómetro e um microfone. Os resultados expe- rimentais demonstram que o sistema implementado tem um desempenho superior a 80% e pode fornecer dados fiáveis. Atualmente a aplicação está a ser testada também no âmbito dos projetos da União Europeia Smart4Health e Smart Bear

Recognition Situations Using Extended Dempster-Shafer Theory

Weiser’s [111] vision of pervasive computing describes a world where technology seamlessly integrates into the environment, automatically responding to peoples’ needs. Underpinning this vision is the ability of systems to automatically track the situation of a person. The task of situation recognition is critical and complex: noisy and unreliable sensor data, dynamic situations, unpredictable human behaviour and changes in the environment all contribute to the complexity. No single recognition technique is suitable in all environments. Factors such as availability of training data, ability to deal with uncertain information and transparency to the user will determine which technique to use in any particular environment. In this thesis, we propose the use of Dempster-Shafer theory as a theoretically sound basis for situation recognition - an approach that can reason with uncertainty, but which does not rely on training data. We use existing operations from Dempster-Shafer theory and create new operations to establish an evidence decision network. The network is used to generate and assess situation beliefs based on processed sensor data for an environment. We also define two specific extensions to Dempster-Shafer theory to enhance the knowledge that can be used for reasoning: 1) temporal knowledge about situation time patterns 2) quality of evidence sources (sensors) into the reasoning process. To validate the feasibility of our approach, this thesis creates evidence decision networks for two real-world data sets: a smart home data set and an officebased data set. We analyse situation recognition accuracy for each of the data sets, using the evidence decision networks with temporal/quality extensions. We also compare the evidence decision networks against two learning techniques: Naïve Bayes and J48 Decision Tree