Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

Telemedicine has evolved rapidly in recent years to enable unprecedented access to digital medical data, such as with networked image distribution/sharing and online (distant) collaborative diagnosis, largely due to the advances in telecommunication and multimedia technologies. However, interactive collaboration systems which control editing of an object among multiple users are often limited to a simple "locking” mechanism based on a conventional client/server architecture, where only one user edits the object which is located in a specific server, while all other users become viewers. Such systems fail to provide the needs of a modern day telemedicine applications that demand simultaneous editing of the medical data distributed in diverse local sites. In this study, we introduce a novel system for telemedicine applications, with its application to an interactive segmentation of volumetric medical images. We innovate by proposing a collaborative mechanism with a scalable data sharing architecture which makes users interactively edit on a single shared image scattered in local sites, thus enabling collaborative editing for, e.g., collaborative diagnosis, teaching, and training. We demonstrate our collaborative telemedicine mechanism with a prototype image editing system developed and evaluated with a user case study. Our result suggests that the ability for collaborative editing in a telemedicine context can be of great benefit and hold promising potential for further researc

An Interactive Table Supporting Mobile Phone Interaction and 3D Content

Due to the small user interfaces (UI) and screen displays of mobile phones it is inconvenient for multiple users to access and view content on phones simultaneously. As such, integrating multi-touch interactive tables with users’ interactions with the content on their phones can be seen as one approach to augment their experience of viewing that content which becomes as a result unlimited to single phone, or single user anymore. In this paper we show a system that implements this augmented integration through utilising Bluetooth, NFC (Near Field Communication), and 3D graphics, which is aimed for medical students

Context-Aware 3D rendering for User-Centric Pervasive Collaborative computing environments

We propose a context-aware adaptive rendering architecture which visualizes 3D content with customized user interfaces, dynamically adapting to current device contexts such as processing power, memory size, display size, and network condition at runtime, while preserving the interactive performance of the 3D content. Using temporal adjustments to the quality of visualization for increased responsiveness and adapting to the current device context. In order to overcome inevitable physical limitations of display capabilities and input controls on client devices, we provide a user interface adaptation mechanism, which dynamically binds operations provided by the 3D application and user interfaces with predefined device and application profiles. For collaborative real-time data manipulation we provide a novel architecture based on publish/subscribe methodology, to handle data between service-service and service-user, allowing for peer to peer or server-client structured communications. An extra layer between the sharing of the data and the local data is applied to provide conversions and update on demand capabilities

Can I Sleep Safely in My Smarthome? A Novel Framework on Automating Dynamic Risk Assessment in IoT Environments

Fully automated homes, equipped with the latest Internet of Things (IoT) devices, aiming to drastically improve the quality of lives of those inhabiting such homes, is it not a perfect setting for cyber threats? More than that, this is a fear of many regular citizens and a trending topic for researchers to apply Cyber Threat Intelligence (CTI) for seamless cyber security. This paper focuses on the Risk Assessment (RA) methodology for smarthome environments, targeting to include all types of IoT devices. Unfortunately, existing approaches mostly focus on the manual or periodic formal RA, or individual device-specific cyber security solutions. This paper presents a Dynamic Risk Assessment Framework (DRAF), aiming to automate the identification of ongoing attacks and the evaluation of the likelihood of associated risks. Moreover, DRAF dynamically proposes mitigation strategies when full automation of the decision making is not possible. The theoretical model of DRAF was implemented and tested in smarthome testbeds deployed in several European countries. The resulting data indicate strong promises for the automation of decision making to control the tightly coupled balance between cyber security and privacy compromise in terms of the embedded services’ usability, end-users’ expectations and their level of cyber concerns

Can I Sleep Safely in My Smarthome? A Novel Framework on Automating Dynamic Risk Assessment in IoT Environments

Fully automated homes, equipped with the latest Internet of Things (IoT) devices, aiming to drastically improve the quality of lives of those inhabiting such homes, is it not a perfect setting for cyber threats? More than that, this is a fear of many regular citizens and a trending topic for researchers to apply Cyber Threat Intelligence (CTI) for seamless cyber security. This paper focuses on the Risk Assessment (RA) methodology for smarthome environments, targeting to include all types of IoT devices. Unfortunately, existing approaches mostly focus on the manual or periodic formal RA, or individual device-specific cyber security solutions. This paper presents a Dynamic Risk Assessment Framework (DRAF), aiming to automate the identification of ongoing attacks and the evaluation of the likelihood of associated risks. Moreover, DRAF dynamically proposes mitigation strategies when full automation of the decision making is not possible. The theoretical model of DRAF was implemented and tested in smarthome testbeds deployed in several European countries. The resulting data indicate strong promises for the automation of decision making to control the tightly coupled balance between cyber security and privacy compromise in terms of the embedded services’ usability, end-users’ expectations and their level of cyber concerns

Analyses on standards and regulations for connected and automated vehicles: Identifying the certifications roadmap

Protecting Connected and Automated Vehicle (CAV) from cyber attacks and data breaches is a major challenge facing the deployment of driverless vehicles. The CAV is a complex interconnected system consisting of sensors, Artificial Intelligence (AI) processors and external units to assure the automated driving without human interaction. Such complexity increases the attack surfaces and makes the CAV highly vulnerable to cyber assaults. It also entangles security audits and certifications procedures. Our work lays out a novel approach towards CAV’s certification focused on cybersecurity and data privacy aspects. Stipulated by the analysis on existing standards’ limitations, we propose a Standards Coverage Map (SCM) outlining the CAV’s entire ecosystem and linking organisational and technical aspects to the latest standards and regulations from the cyber perspective

Motion analysis and classification of salsa dance using music-related motion features

Learning couple dance such as Salsa is a challenge for modern human as it requires to assimilate and understand correctly all the required parameters. In this paper, we propose a set of music-related motion features (MMF) allowing to describe, analyse and classify salsa dancer couple in their respective learning state (beginner, intermediate and expert). These dance qualities have been proposed from a systematic review of papers cross linked with interviews from teacher and professionals in the field of social dance. We investigated how to extract these MMF from musical data and 3D movements of dancers in order to propose a new algorithm to compute them. For the presented study, a motion capture database (SALSA) has been recorded of 26 different couples with varying skill levels dancing on 10 different tempos (260 clips). Each recorded clips contains a basic steps sequence and an extended improvisation sequence during two minutes in total at 120 frame per second. We finally use our proposed algorithm to analyse and classify these 26 couples in three learning levels, which validates some proposed music-related motion features and give insights on others

Classification of Salsa Dance Level using Music and Interaction based Motion Features

Learning couple dance such as Salsa is a challenge for the modern human as it requires to assimilate and understand correctly all the dance parameters. Traditionally learned with a teacher, some situation and the variability of dance class environment can impact the learning process. Having a better understanding of what is a good salsa dancer from motion analysis perspective would bring interesting knowledge and can complement better learning. In this paper, we propose a set of music and interaction based motion features to classify salsa dancer couple performance in three learning states (beginner, intermediate and expert). These motion features are an interpretation of components given via interviews from teacher and professionals and other dance features found in systematic review of papers. For the presented study, a motion capture database (SALSA) has been recorded of 26 different couples with three skill levels dancing on 10 different tempos (260 clips). Each recorded clips con tains a basic steps sequence and an extended improvisation sequence during two minutes in total at 120 frame per second. Each of the 27 motion features have been computed on a sliding window that corresponds to the 8 beats reference for dance. Different multiclass classifier has been tested, mainly k-nearest neighbours, Random forest and Support Vector Machine, with an accuracy result of classification up to 81% for three levels and 92% for two levels. A later feature analysis validates 23 out of 27 proposed features. The work presented here has profound implications for future studies of motion analysis, couple dance learning and human-human interaction

Coupling strategies for multi-resolution deformable meshes: expanding the pyramid approach beyond its one-way nature

Purpose With higher resolutions, medical image processing operations like segmentation take more time to calculate per step. The pyramid technique is a common approach to solving this problem. Starting with a low resolution, a stepwise refinement is applied until the original resolution is reached. Methods Our work proposes a method for deformable model segmentation that generally utilizes the common pyramid technique with our improvement, to calculate and keep synchronized all mesh resolution levels in parallel. The models are coupled to propagate their changes. It presents coupling techniques and shows approaches for synchronization. The interaction with the models is realized using springs and volcanoes, and it is evaluated for the semantics of the operation to share them across the different levels. Results The locking overhead has been evaluated for different synchronization techniques with meshes of individual resolutions. The partial update strategy has been found to have the least locking overhead. Conclusion Running multiple models with individual resolutions in parallel is feasible. The synchronization approach has to be chosen carefully, so that an interactive modification of the segmentation remains possible. The proposed technique is aimed at making medical image segmentation more usable while delivering high performance