Charalampos Doukas

Mobile healthcare information management utilizing Cloud Computing and Android OS

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Googles Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazons S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.

Bringing IoT and Cloud Computing towards Pervasive Healthcare

Pervasive healthcare applications utilizing body sensor networks generate a vast amount of data that need to be managed and stored for processing and future usage. Cloud computing among with the Internet of Things (IoT) concept is a new trend for efficient managing and processing of sensor data online. This paper presents a platform based on Cloud Computing for management of mobile and wearable healthcare sensors, demonstrating this way the IoT paradigm applied on pervasive healthcare.

Emergency Fall Incidents Detection in Assisted Living Environments Utilizing Motion, Sound, and Visual Perceptual Components

This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patients body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subjects visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patients status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed.

Patient Fall Detection using Support Vector Machines

This paper presents a novel implementation of a patient fall detection system that may be used for patient activity recognition and emergency treatment. Sensors equipped with accelerometers are attached on the body of the patients and transmit patient movement data wirelessly to the monitoring unit. The methodology of support Vector Machines is used for precise classification of the acquired data and determination of a fall emergency event. Then a context-aware server transmits video from patient site properly coded according to both patient and network status. Evaluation results indicate the high accuracy of the classification method and the effectiveness of the proposed implementation.

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.

Region of Interest Coding Techniques for Medical Image Compression

We have provided an overview of state-of-the-art ROI coding techniques applied to medical images. These techniques are classified according to the image type they apply to; thus the first class includes ROI coding schemes developed for two-dimensional (2-D) still medical images whereas the second class consists of ROI coding in the case of volumetric images. In the third class, a prototype ROI encoder for compression of angiogram video sequences is presented. ROI coding preserves image quality in diagnostically critical regions by performing advanced image compression, enabling better image examination and addressing issues regarding image handling and transmission in telemedicine systems. The mapping of the ROI from the spatial domain to the wavelet domain is dependent on the used wavelet filters and it is simplified for rectangular and circular regions. Therefore, ROI coding is considered quite important in distributed and networked electronic healthcare.

Emergency incidents detection in assisted living environments utilizing sound and visual perceptual components

The paper presents the concept and an initial implementation of a patient status awareness system that may be used for patient activity interpretation and emergency recognition in cases like elder falls. The system utilizes audio and video data captured from the patients environment. Visual information is acquired using overhead cameras and audio data is collected from microphone arrays. Proper audio data processing allows the detection of sounds related to body falls or distress speech expressions. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of the subjects. Sound directionality in conjunction to trajectory information and subjects visual location can verify fall and indicate an emergency event. Post fall visual behavior of the subject indicates the severity of the fall (e.g., if patient remains unconscious or patient recovers). A number of advanced classification techniques have been evaluated using the latter perceptual components. The performance of the classifiers has been assessed in terms of accuracy and efficiency and results are presented.

Managing Wearable Sensor Data through Cloud Computing

Mobile pervasive healthcare technologies can support a wide range of applications and services including patient monitoring and emergency response. At the same time they introduce several challenges, like data storage and management, interoperability and availability of heterogeneous resources, unified and ubiquitous access issues. One potential solution for addressing all aforementioned issues is the introduction of the Cloud Computing concept. Within this context, in this work we have developed and present a wearable -- textile platform based on open hardware and software that collects motion and heartbeat data and stores them wirelessly on an open Cloud infrastructure for monitoring and further processing. The proposed system may be used to promote the independent living of patient and elderly requiring constant surveillance.

An overview of body sensor networks in enabling pervasive healthcare and assistive environments

The use of sensor networks for healthcare, well-being, and working in extreme environments has long roots in the engineering sector in medicine and biology community. With the growing needs in ubiquitous communications and recent advances in very-low-power wireless technologies, there has been considerable interest in the development and application of wireless networks around humans. With the maturity of wireless sensor networks, body area networks (BANs), and wireless BANs (WBANs), recent efforts in promoting the concept of body sensor networks (BSNs) aim to move beyond sensor connectivity to adopt a system-level approach to address issues related to biosensor design, interfacing, and embodiment, as well as ultra low-power processing / communication, power scavenging, autonomic sensing, data mining, inferencing, and integrated wireless sensor microsystems. As a result, the system architecture based on WBAN and BSN is becoming a widely accepted method of organization for ambulatory and ubiquitous monitoring systems. This review paper presents an up-to-date report of the current research and enabling applications and addresses some of the challenges and implementation issues.