Uranus: A Middleware Architecture for Dependable AAL and Vital Signs Monitoring Applications

The design and realization of health monitoring applications has attracted the interest of large communities both from industry and academia. Several research challenges have been faced and issues tackled in order to realize effective applications for the management and monitoring of people with chronic diseases, people with disabilities, elderly people. However, there is a lack of efficient tools that enable rapid and possibly cheap realization of reliable health monitoring applications. The paper presents Uranus, a service oriented middleware architecture, which provides basic functions for the integration of different kinds of biomedical sensors. Uranus has also distinguishing characteristics like services for the run-time verification of the correctness of running applications and mechanisms for the recovery from failures. The paper concludes with two case studies as proof of concept

Gait Anomaly Detection of Subjects With Parkinson's Disease Using a Deep Time Series-Based Approach

Parkinson's disease (PD) is a cognitive degenerative disorder of the central nervous system that mainly affects the motor system. The earliest symptoms evidence a general deficit of coordination and an unsteady gait. Current approaches for the evaluation and assessment of gait disturbances in PD have proved to be expensive, inconvenient and ineffective in the detection of anomalous walking patterns. In this paper, we address these issues by defining a deep time series-based approach for the detection of anomalous walking patterns in the gait dynamics of elderly people by analyzing the acceleration values of their movements. The results show a training accuracy and testing accuracy of over 90% with an accuracy improvement of 4.28% in comparison with related works

A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Alzheimer's disease is the most common type of dementia. Patients suffer from of this kind of disease could show symptoms such as sleep disturbances, muscle rigidity or other typical Alzheimer's movement irregularities. In our work, we have focused on those types of disturbances related to sleep disorders. Due to their not well-known nature, it is difficult to develop software able to identify sleep disorders. In this work, we have addressed the problem of the automatic recognition of sleep disorders in patients with Alzheimer's disease by using deep learning algorithm

The heuristic strategies for assessing wireless sensor network: an event-based formal approach

Wireless Sensor Networks (WSNs) are increasingly being adopted in critical applications. In these networks undesired events may undermine the reliability level; thus their effects need to be properly assessed from the early stages of the development process onwards to minimize the chances of unexpected problems during use. In this paper we propose two heuristic strategies: what-if analysis and robustness checking. They allow to drive designers towards optimal WSN deployment solutions, from the point of view of the connection and data delivery resiliency, exploiting a formal approach based on the event calculus formal language. The heuristics are backed up by a support tool aimed to simplify their adoption by system designers. The tool allows to specify the target WSN in a user-friendly way and it is able to elaborate the two heuristic strategies by means of the event calculus specifications automatically generated. The WSN reliability is assessed computing a set of specific metrics. The effectiveness of the strategies is shown in the context of three case studies

Inverse Reinforcement Learning Through Max-Margin Algorithm

Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertainty. An agent finds a suitable policy through a reward function by interacting with a dynamic environment. However, for complex and large problems it is very difficult to specify and tune the reward function. Inverse Reinforcement Learning (IRL) may mitigate this problem by learning the reward function through expert demonstrations. This work exploits an IRL method named Max-Margin Algorithm (MMA) to learn the reward function for a robotic navigation problem. The learned reward function reveals the demonstrated policy (expert policy) better than all other policies. Results show that this method has better convergence and learned reward functions through the adopted method represents expert behavior more efficiently

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision

A formal methodology to design and deploy dependable wireless sensor networks

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence their effects need to be properly assessed before deployment to obtain a good level of expected performance and during the operation in order to avoid dangerous unexpected results. In this paper we propose amethodology that aims at assessing and improving the dependability level of WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of dependable WSN and a tool ("ADVISES") to simplify its adoption. The tool is applicable to homogeneous WSNs with static routing topologies. It allows to generate automatically formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime for WSNs where an acceptable percentage of faults can be defined. During the runtime we can check the behavior of the WSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, as proof-of-concept, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of the WSN at runtime. Although the method scales up to very large WSNs, the applicability of the methodology maybe compromised by the state space explosion of the reasoning model, which must be faced partitioning large topologies into sub-topologies

ALPHA: an eAsy inteLligent service Platform for Healthy Ageing

Dementia is one of the biggest global public health challenges facing our generation. Alzheimers disease (AD) is the most frequent cause of dementia in elderly people over 65 years of age. The typical characteristic of AD is impairment of memory. As the disease progresses, other cognitive domains such as language, praxis, visuo-spatial and executive functions become involved, eventually resulting in global cognitive decline. Behavioral Psychological Symptoms of Dementia (BPSD) problems are constant in AD and have highly negative impact on the quality of life of patients and their families. ALPHA project aims at developing an intelligent situation-aware system to collect and process information about Alzheimer Disease patients? life style. Starting from various data provided by caregivers and a set of non-invasive sensors and devices. ALPHA will provide clinicians with new quantitative and qualitative information about patients? abnormal behavior which, along with medical data, will enhance the accuracy and reliability of monitoring and assessing the patient?s health status. Clinicians will be supported by a suite of specifically designed tools and interfaces to analyze the metadata captured, improve management of personalized care plans and better interact with patients and caregivers. Studies of antique records of former psychiatric hospital will enable us towiden the knowledge of behavioral disorders thus allowing to compare the ancient ones and the curcurrent and to probabilistically determine relation between type of dementia and behavioral disorders

Parasitos em amostras fecais de ambiente da Ilha da Marambaia, Rio de Janeiro, Brasil: uma abordagem em saúde pública

This research aimed to describe the frequency of parasites in stool samples in the environment of Ilha da Marambaia, Rio de Janeiro, Brazil. One hundred and five stool samples were collected and processed by the coproparasitological techniques ethyl acetate sedimentation and centrifuge-flotation using saturated sugar solution. Parasites were detected in 81.9% of the samples, hookworm being the most prevalent, followed by Trichuris vulpis. Ascaris sp. eggs were also found. A high level of evolutive forms of parasites with public health risk was found in stool samples of the environment studied. We propose that health education programs, allied to an improvement of human and animal health care, must be employed to reduce the environmental contamination.O objetivo deste estudo foi descrever a frequência de parasitos em amostras fecais coletadas no ambiente da Ilha da Marambaia, Rio de Janeiro, Brasil. Cento e cinco amostras foram coletadas e processadas pelas técnicas coproparasitológicas de sedimentação em acetato de etila e centrifugo-flutuação em solução saturada de sacarose. Foi observada positividade em 81.9% das amostras, sendo ancilostomídeo o parasito mais frequente, seguido de Trichuris vulpis. Ovos de Ascaris sp. também foram detectados. Observou-se elevada frequência de parasitos com importância em saúde pública nas fezes recolhidas no ambiente. Programas de educação em saúde, aliados a atenção dos serviços das saúdes humana e animal, devem ser empregados para redução dos níveis de contaminação ambiental

Static verification of wireless sensor networks with formal methods

Wireless Sensor Networks (WSNs) are widely recognized as a solution to build monitoring systems, even in critical environments. WSNs, however, are subjected to faults due to several causes (i.e. rain, EMF radiations, vibrations, etc..) and tools and methodologies for the design of dependable WSN-based systems are needed. Formal methods partially meet such needs by assessing the degree of correctness of design models and identifying potential system bottlenecks. The aim of this paper is to define a methodology for the static verification of WSN based systems using a formal language (Event Calculus). In particular we show how the formal specification can be used to verify the design of a WSN in terms of its dependability properties. To this aim, we define a set of correctness specifications that apply to a generic WSN, coupled with specific structural specifications describing the target network topology to evaluate. Finally, after having presented an automatic tool, designed to support the designer, we adopt this methodology to a case study