Leila Alem

A platform for secure monitoring and sharing of generic health data in the Cloud

The growing need for the remote caring of patients at home combined with the ever-increasing popularity of mobile devices due to their ubiquitous nature has resulted in many apps being developed to enable mobile telecare. The Cloud, in combination with mobile technologies has enabled doctors to conveniently monitor and assess a patients health while the patient is at the comfort of their own home. This demands sharing of health information between healthcare teams such as doctors and nurses in order to provide better and safer care of patients. However, the sharing of health information introduces privacy and security issues which may conflict with HIPAA standards. In this paper, we attempt to address the issues of privacy and security in the domain of mobile telecare and Cloud computing. We first demonstrate a telecare application that will allow doctors to remotely monitor patients via the Cloud. We then use this system as a basis to showcase our model that will allow patients to share their health information with other doctors, nurses or medical professional in a secure and confidential manner. The key features of our model include the ability to handle large data sizes and efficient user revocation.

3D helping hands: a gesture based MR system for remote collaboration

There is currently a strong need for collaborative systems with which two or more participants interact over a distance on a task involving tangible artifacts (e.g., a machine, a patient, a tool). The present paper focuses on the specific category of remote-collaboration systems where hand gestures are used by a remote helper to assist a physically distant worker to perform manual tasks. Existing systems use a combination of video capturing, 2D monitors or 2D projectors, however displaying a video of the remote workspace and allowing helpers to gesture over the video does not provide helpers with sufficient understanding of the spatial relationships between remote objects and between their hands and the remote objects. In this paper we introduce our tele-presence Mixed Reality system for remote collaboration on physical tasks based on real-time capture and rendering of the remote workspace and of the helpers hands. We improve on previous 2D systems introducing 3D capturing and rendering, and exploiting the possibility offered by the use of real 3D data to increase the feeling of immersion offered by the system using head tracking, stereoscopic rendering, inter-occlusion handling and virtual shadowing. We performed initial usability test of our system to verify if users are satisfied with the spatial awareness the system provides.

Information Environments for Supporting Consistent Registrar Medical Handover

This study was two-fold in nature. Initially, it examined the information environment and the use of customary information tools to support medical handovers in a large metropolitan teaching hospital on four weekends (i.e. Friday night to Monday morning). Weekend medical handovers were found to involve sequences of handovers where patients were discussed at the discretion of the doctor handing over; no reliable discussion of all patients of concern occurred at any one handover, with few information tools being used; and after a set of weekend handovers, there was no complete picture on a Monday morning without an analysis of all patient progress notes. In a subsequent case study, three information tools specifically designed as intervention that attempted to enrich the information environment were evaluated. Results indicate that these tools did support greater continuity in who was discussed but not in what was discussed at handover. After the intervention, if a doctor discussed a patient at handover, that patient was more likely to be discussed at subsequent handovers. However, the picture at Monday morning remained fragmentary. The results are discussed in terms of the complexities inherent in the handover process

A study of gestures in a video-mediated collaborative assembly task

This paper presents the results of an experimental investigation of two gesture representations (overlaying hands and cursor pointer) in a video-mediated scenario--remote collaboration on physical task. Our study assessed the relative value of the two gesture representations with respect to their effectiveness in task performance, users satisfaction, and users perceived quality of collaboration in terms of the coordination and interaction with the remote partner. Our results show no clear difference between these two gesture representations in the effectiveness and user satisfaction. However, when considering the perceived quality of collaboration, the overlaying hands condition was statistically significantly higher than the pointer cursor condition. Our results seem to suggest that the value of a more expressive gesture representation is not so much a gain in performance but rather a gain in users experience, more specifically in users perceived quality of collaborative effort.

Recent Trends of Mobile Collaborative Augmented Reality Systems

The use of mobile collaborative AR has expended rapidly in recent years, due to the major advances in hardware and networking. The application areas are diverse and multidisciplinary. Recent Trends of Mobile Collaborative Augmented Reality Systems provides a historical overview of previous mobile collaborative AR systems, presents case studies of latest developments in current mobile collaborative AR systems, and latest technologies and system architectures used in this field. Recent Trends of Mobile Collaborative Augmented Reality Systems is designed for a professional audience composed of practitioners and researchers working in the field of augmented reality and human-computer interaction. Advanced-level students in computer science and electrical engineering focused on this topic will also find this book useful as a secondary text or reference.

HandsIn3D: Supporting remote guidance with immersive virtual environments

A collaboration scenario involving a remote helper guiding in real time a local worker in performing a task on physical objects is common in a wide range of industries including health, mining and manufacturing. An established ICT approach to supporting this type of collaboration is to provide a shared visual space and some form of remote gesture. The shared space and remote gesture are generally presented in a 2D video form. Recent research in tele-presence has indicated that technologies that support co-presence and immersion not only improve the process of collaboration but also improve spatial awareness of the remote participant. We therefore propose a novel approach to developing a 3D system based on a 3D shared space and 3D hand gestures. A proof of concept system for remote guidance called HandsIn3D has been developed. This system uses a head tracked stereoscopic HMD that allows the helper to be immersed in the virtual 3D space of the worker’s workspace. The system captures in 3D the hands of the helper and fuses the hands into the shared workspace. This paper introduces HandsIn3D and presents a user study to demonstrate the feasibility of our approach.

Software Tools and Techniques for Big Data Computing in Healthcare Clouds

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis. The data originated from multiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology (ICT) era: how to effectively and optimally manage such large amount of data and identify new ways to analyze large amounts of data for unlocking information. The issue is also known as the ‘Big Data’ problem, which is defined as the practice of collecting complex data sets so large that it becomes difficult to analyze and interpret manually or using on-hand data management applications. From the perspective of real-world applications, the Big Data problem has also become a common phenomenon in domain of science, medicine, engineering, and commerce. Representative applications include clinical decision support systems, digital agriculture, social media analytics, high energy physics, earth observation, genomics, automobile simulations, medical imaging, body area networks, translational medicine, and the like. An important class of Big Data application exists in the healthcare domain. There are wide varieties of health related datasets that play a critical role in the health information systems (HIS) and clinical decision support systems (CDSS). These datasets differ widely in their volume, variety, and velocity, from patient focused sets such as electronic medical records to population focused sets such as public health data, and knowledge focused sets such as drug-to-drug, drug-to-disease, disease to disease interaction registries. While decision makers’ (healthcare practitioner, government decision makers) ability to understand and process the health data dictates the accuracy of the final decision, the exponential growth in the size of the aforementioned health related raw data sets has widened this integration gap. This further makes the timely information aggregation, retrieval, and analysis a challenge. This is severely limiting the potential benefits of having large datasets and HIS/CDSS for medical decision-making processes. Another important class of Big Data application in the healthcare domain includes the Medical Body Area Networks (MBANs). According to the market intelligence company ABI research (http://www.abiresearch.com/), over the next five years, close to five million disposable wireless MBAN sensors will be shipped. MBANs enable a continuous monitoring of patient’s condition by sensing and transmitting measurements such as heart rate, electrocardiogram (ECG), body temperature, respiratory rate, chest

Human Factors in Augmented Reality Environments

Advances in hardware and networking have made possible a wide use of augmented reality (AR) technologies. However, simply putting those hardware and technologies together does not make a good system for end users to use. New design principles and evaluation methods specific to this emerging area are urgently needed to keep up with the advance in technologies. Human Factors in Augmented Reality Environments is the first book on human factors in AR, addressing issues related to design, development, evaluation and application of AR systems. Topics include surveys, case studies, evaluation methods and metrics, HCI theories and design principles, human factors and lessons learned and experience obtained from developing, deploying or evaluating AR systems. The contributors for this cutting-edge volume are well-established researchers from diverse disciplines including psychologists, artists, engineers and scientists. Human Factors in Augmented Reality Environments is designed for a professional audience composed of practitioners and researchers working in the field of AR and human-computer interaction. Advanced-level students in computer science and engineering will also find this book useful as a secondary text or reference.

HandsinAir: a wearable system for remote collaboration on physical tasks

Many real world scenarios involve a remote helper guiding a local worker performing manipulations of physical objects (physical tasks). Technologies and systems have been developed to support such collaborations. However, existing systems often confine collaborators in fixed desktop settings. Yet, there are many situations in which collaborators are mobile and/or desktop settings are not possible to set up. In this paper, we present HandsInAir, a real-time collaborative wearable system for remote collaboration. HandsInAir is designed to support mobility of both the worker and the helper and to provide easy access to remote expertise. In particular, this system implements a novel approach that allows helpers to perform hand gestures in the air and frees two hands of workers for object operations. We describe the system and an evaluation of it and envision future work.

Design of a multi-site multi-state clinical trial of home monitoring of chronic disease in the community in Australia

BackgroundTelehealth services based on at-home monitoring of vital signs and the administration of clinical questionnaires are being increasingly used to manage chronic disease in the community, but few statistically robust studies are available in Australia to evaluate a wide range of health and socio-economic outcomes. The objectives of this study are to use robust statistical methods to research the impact of at home telemonitoring on health care outcomes, acceptability of telemonitoring to patients, carers and clinicians and to identify workplace cultural factors and capacity for organisational change management that will impact on large scale national deployment of telehealth services. Additionally, to develop advanced modelling and data analytics tools to risk stratify patients on a daily basis to automatically identify exacerbations of their chronic conditions.Methods/DesignA clinical trial is proposed at five locations in five states and territories along the Eastern Seaboard of Australia. Each site will have 25 Test patients and 50 case matched control patients. All participants will be selected based on clinical criteria of at least two hospitalisations in the previous year or four or more admissions over the last five years for a range of one or more chronic conditions. Control patients are matched according to age, sex, major diagnosis and their Socio-Economic Indexes for Areas (SEIFA). The Trial Design is an Intervention control study based on the Before-After-Control-Impact (BACI) design.DiscussionOur preliminary data indicates that most outcome variables before and after the intervention are not stationary, and accordingly we model this behaviour using linear mixed-effects (lme) models which can flexibly model within-group correlation often present in longitudinal data with repeated measures. We expect reduced incidence of unscheduled hospitalisation as well as improvement in the management of chronically ill patients, leading to better and more cost effective care. Advanced data analytics together with clinical decision support will allow telehealth to be deployed in very large numbers nationally without placing an excessive workload on the monitoring facility or the patients own clinicians.Trial registrationRegistered with Australian New Zealand Clinical Trial Registry on 1st April 2013. Trial ID: ACTRN12613000635763