INNOVATING CONTROL AND EMOTIONAL EXPRESSIVE MODALITIES OF USER INTERFACES FOR PEOPLE WITH LOCKED-IN SYNDROME

Patients with Lock-In-Syndrome (LIS) lost their ability to control any body part beside their eyes. Current solutions mainly use eye-tracking cameras to track patients' gaze as system input. However, despite the fact that interface design greatly impacts user experience, only a few guidelines have been were proposed so far to insure an easy, quick, fluid and non-tiresome computer system for these patients. On the other hand, the emergence of dedicated computer software has been greatly increasing the patients' capabilities, but there is still a great need for improvements as existing systems still present low usability and limited capabilities. Most interfaces designed for LIS patients aim at providing internet browsing or communication abilities. State of the art augmentative and alternative communication systems mainly focus on sentences communication without considering the need for emotional expression inextricable from human communication. This thesis aims at exploring new system control and expressive modalities for people with LIS. Firstly, existing gaze-based web-browsing interfaces were investigated. Page analysis and high mental workload appeared as recurring issues with common systems. To address this issue, a novel user interface was designed and evaluated against a commercial system. The results suggested that it is easier to learn and to use, quicker, more satisfying, less frustrating, less tiring and less prone to error. Mental workload was greatly diminished with this system. Other types of system control for LIS patients were then investigated. It was found that galvanic skin response may be used as system input and that stress related bio-feedback helped lowering mental workload during stressful tasks. Improving communication was one of the main goal of this research and in particular emotional communication. A system including a gaze-controlled emotional voice synthesis and a personal emotional avatar was developed with this purpose. Assessment of the proposed system highlighted the enhanced capability to have dialogs more similar to normal ones, to express and to identify emotions. Enabling emotion communication in parallel to sentences was found to help with the conversation. Automatic emotion detection seemed to be the next step toward improving emotional communication. Several studies established that physiological signals relate to emotions. The ability to use physiological signals sensors with LIS patients and their non-invasiveness made them an ideal candidate for this study. One of the main difficulties of emotion detection is the collection of high intensity affect-related data. Studies in this field are currently mostly limited to laboratory investigations, using laboratory-induced emotions, and are rarely adapted for real-life applications. A virtual reality emotion elicitation technique based on appraisal theories was proposed here in order to study physiological signals of high intensity emotions in a real-life-like environment. While this solution successfully elicited positive and negative emotions, it did not elicit the desired emotions for all subject and was therefore, not appropriate for the goals of this research. Collecting emotions in the wild appeared as the best methodology toward emotion detection for real-life applications. The state of the art in the field was therefore reviewed and assessed using a specifically designed method for evaluating datasets collected for emotion recognition in real-life applications. The proposed evaluation method provides guidelines for future researcher in the field. Based on the research findings, a mobile application was developed for physiological and emotional data collection in the wild. Based on appraisal theory, this application provides guidance to users to provide valuable emotion labelling and help them differentiate moods from emotions. A sample dataset collected using this application was compared to one collected using a paper-based preliminary study. The dataset collected using the mobile application was found to provide a more valuable dataset with data consistent with literature. This mobile application was used to create an open-source affect-related physiological signals database. While the path toward emotion detection usable in real-life application is still long, we hope that the tools provided to the research community will represent a step toward achieving this goal in the future. Automatically detecting emotion could not only be used for LIS patients to communicate but also for total-LIS patients who have lost their ability to move their eyes. Indeed, giving the ability to family and caregiver to visualize and therefore understand the patients' emotional state could greatly improve their quality of life. This research provided tools to LIS patients and the scientific community to improve augmentative and alternative communication, technologies with better interfaces, emotion expression capabilities and real-life emotion detection. Emotion recognition methods for real-life applications could not only enhance health care but also robotics, domotics and many other fields of study. A complete system fully gaze-controlled was made available open-source with all the developed solutions for LIS patients. This is expected to enhance their daily lives by improving their communication and by facilitating the development of novel assistive systems capabilities

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

OnCreate and the virtual teammate: an analysis of online creative processes and remote collaboration

This paper explores research undertaken by a consortium of 10 universities from across Europe as part of an EU Erasmus Strategic Partnership project called OnCreate. Recent research and experiences prove the importance of the design and implementation of online courses that are learner-centred, include collaboration and integrate rich use of media in authentic environments. The OnCreate project explores the specific challenges of creative processes in such environments. The first research phase comprises a comparative qualitative analysis of collaboration practices in design-related study programmes at the ten participating universities. A key outcome of this research was in identifying the shortcomings of the hierarchical role models of established Learning Management Systems (such as Moodle or Blackboard) and the tendency towards evolving 'mash-up' environments to support creative online collaboration