Vangelis Metsis

IoT middleware : a survey on issues and enabling technologies

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

Digital cities of the future: Extending @home assistive technologies for the elderly and the disabled

In the digital city of the future there is the vision of seamless virtual and physical access for every home and between each home and the workplace, as well as critical city infrastructure such as the post office, the bank, hospitals, transportation systems, and other entities. This paper provides an overview of technical and other issues in extending at home (@home) assistive technologies for the elderly and the disabled. The paper starts by giving a vision of what this city is supposed to look like and how a human is to act, navigate and function in it. A framework for extending assistive technologies is proposed that considers individuals belonging to special groups of interest and locations other than their home. Technology has already reached the state of ubiquitous and pervasive sensor devices measuring everything, from temperature to human behavior. Implanting intelligence into and connecting such devices will be of immense use in preventive healthcare, security in industrial installations, greater energy efficiency, and numerous other applications. The paper reviews enabling technologies that exist and focuses on healthcare applications that support a longer and higher quality of life at home for the elderly and the disabled. It discusses intelligent platforms involving agents, context-aware and location-based services, and classification systems that enable advanced monitoring and interpretation of patient status and optimization of the environment to improve medical assessments. The paper concludes with a discussion of some of the challenges that exist in extending @home assistive technologies to @city assistive technologies.

Abnormal human behavioral pattern detection in assisted living environments

In recent years, there is a growing interest about assisted living environments especially for the elderly who live alone, due to the increasing number of aged people. In order for them to live safe and healthy, we need to detect abnormal behavior that may cause severe and emergent situations for the elderly. In this work, we suggest a method that detects abnormal behavior using wireless sensor networks. We model an episode that is a series of events, which includes spatial and temporal information about the subject being monitored. We define a similarity scoring function that compares two episodes taking into consideration temporal aspects. We propose a way to determine a threshold to divide episodes into two groups that reduces wrong classification. Weights on individual functions that consist the similarity function are determined experimentally so that they can produce the good results in terms of area under curve in receiver operating characteristic (ROC) curve.

Towards an evaluation framework for assistive environments

As the world population is aging, there is an increasing need to support independent living of elderly people. Assistive environments incorporate the latest pervasive and ubiquitous technologies and provide a viable alternative to traditional assistive living. In this paper, we propose an evaluation framework to assess the quality of assistive environments. An assistive environment can be successful only if the potential users are willing to adopt it. The proposed framework identifies a set of attributes that are considered critical to user adoption. Sample metrics, as well as possible approaches to measure them, are also suggested to quantify those attributes. The framework is illustrated using an experimental assistive apartment environment that is being built at the University of Texas at Arlington.

Non-invasive analysis of sleep patterns via multimodal sensor input

The monitoring of sleep patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns: a contact-based pressure mattress and a non-contact 3D image acquisition device, which can complement each other. To evaluate our system, we used real data collected in Heracleia Lab’s assistive living apartment. Our system uses Machine Learning techniques to automatically analyze the collected data and recognize sleep patterns. It is non-invasive, as it does not disrupt the user’s usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.

Guided physical therapy through the use of the Barrett WAM robotic arm

Physical therapy is a crucial part of the rehabilitation process during recovery from an injury that has resulted in motor function loss. Newly introduced technologies can enhance traditional physical therapy, first, by complementing the experts work, and second, by providing a platform for rich data collection and analysis. In this work, we present a prototype adaptive rehabilitation instrument, based on the use of robotic arm, which can be dynamically controlled to guide the exercise motion of the upper extremities, in patients with motor disabilities. Our proposed method, enables simultaneous active and passive control of the robotic arm, to produce adaptive force feedback for motion guidance, and allow for data collection, for patient motor function assessment.

Computer aided rehabilitation for patients with rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic disease that leads to inflammation of joints and the surrounding tissues and is a major cause of reduced quality of life and disability. RA can also cause major organ damage and is an independent risk factor for cardiovascular disease. Physical Therapy (PT) and Physical Activity (PA) have been shown to mitigate the effects of the disease, however, lack of motivation and low adherence of the patients reduce the benefits of PT and PA. In this paper, we present a cyberphysical system intended to preserve the functional Range-Of-Motion (ROM) and cardiovascular health in persons with RA, by promoting their physical activity levels and enhancing their physical therapy routines. The system uses game-based activities to increase user motivation and vision-based motion tracking to ensure patient compliance with the prescribed physical therapy routines and activity levels.

Energy-Constrained Scheduling of DAGs on Multi-core Processors

This paper proposes a technique to minimize the makespan of DAGs under energy constraints on multi-core processors that often need to operate under strict energy constraints. Most of the existing work aims to reduce energy subject to performance constraints. Thus, our work is in contrast to these techniques, and it is useful because one can encounter numerous energy-constraint scenarios in real life. The algorithm, named Incremental Static Voltage Adaptation (ISVA), uses the Dynamic Voltage Scaling technique and assigns differential voltages to each sub-task to minimize energy requirements of an application. Essentially, ISVA is a framework, rather than yet another DAG scheduling algorithm, in that it can work with any efficient algorithm to generate the initial schedule under no energy constraints. Given the initial schedule, ISVA efficiently identifies tasks’ relative importance and their liabilities on energy. It then achieves the best possible new schedule by observing its energy budget. The algorithm marginally degrades the schedule length with extensive reduction in energy budgets.

An eye tracking dataset for point of gaze detection

This paper presents a new, publicly available eye tracking dataset, aimed to be used as a benchmark for Point of Gaze (PoG) detection algorithms. The dataset consists of a set of videos recording the eye motion of human test subjects as they were looking at, or following, a set of predefined points of interest on a computer visual display unit. The eye motion was recorded using a Mobile Eye, head mounted, infrared monocular camera. The ground truth of the point of gaze and head location and direction in the three dimensional space are provided together with the data. The ground truth regarding the point of gaze at is known in advance since the subjects are always looking at predefined targets, whereas, the head position in 3D is captured using a Vicon Motion Tracking System.

Recognition of sleep patterns using a bed pressure mat

The monitoring of sleep patterns is of major importance for various reason such as, the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a bed pressure mat to assess and report sleep patterns. To evaluate our system we used real data collected in Heracleia Labs assistive living apartment. Our method is non-invasive, as it does not disrupt the users usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.