Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras

Voice Analysis for Stress Detection and Application in Virtual Reality to Improve Public Speaking in Real-time: A Review

Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performanceComment: 41 pages, 7 figures, 4 table

Paralinguistic vocal control of interactive media: how untapped elements of voice might enhance the role of non-speech voice input in the user's experience of multimedia.

Much interactive media development, especially commercial development, implies the dominance of the visual modality, with sound as a limited supporting channel. The development of multimedia technologies such as augmented reality and virtual reality has further revealed a distinct partiality to visual media. Sound, however, and particularly voice, have many aspects which have yet to be adequately investigated. Exploration of these aspects may show that sound can, in some respects, be superior to graphics in creating immersive and expressive interactive experiences. With this in mind, this thesis investigates the use of non-speech voice characteristics as a complementary input mechanism in controlling multimedia applications. It presents a number of projects that employ the paralinguistic elements of voice as input to interactive media including both screen-based and physical systems. These projects are used as a means of exploring the factors that seem likely to affect users’ preferences and interaction patterns during non-speech voice control. This exploration forms the basis for an examination of potential roles for paralinguistic voice input. The research includes the conceptual and practical development of the projects and a set of evaluative studies. The work submitted for Ph.D. comprises practical projects (50 percent) and a written dissertation (50 percent). The thesis aims to advance understanding of how voice can be used both on its own and in combination with other input mechanisms in controlling multimedia applications. It offers a step forward in the attempts to integrate the paralinguistic components of voice as a complementary input mode to speech input applications in order to create a synergistic combination that might let the strengths of each mode overcome the weaknesses of the other

ChilDiBu – A Mobile Application for Bulgarian Children with Special Educational Needs

Designing applications adapted to the capabilities of each user is an important part of modern software engineering. Children with Autism spectrum disorder and/or intellectual disabilities are a special group of users where standard application design is not a good approach. Some of them have speech disorders; others have disorders of the musculoskeletal system and in their cognitive development. In the literature, these children are referred to as children with special educational needs. Only a few researchers are involved in the development of various computer technologies, including mobile applications appropriate for these children. Some specialized mobile applications for these users can be found in the different mobile stores. However, most of these apps are rather expensive or with inappropriate user interfaces. Another disadvantage in all the apps is the lack of support in the Bulgarian language. It makes them unusable for the youngest users with special needs in Bulgaria. A conceptual model based on the investigation of some existing applications, the well-established theoretical practices, and on the opinion of children without disorders is proposed in this paper. An approach to designing and developing the interactive mobile application ChilDiBu for children with special needs, suitable for the Windows Phone mobile operating system, is presented. ChilDiBu is a picture-based app that combines graphic images, texts and audio files which can be used to learn the Bulgarian alphabet, the numbers up to 20, some basic colors and the daily activities that children perform. The application is focused on the development of the fine motor skills, observation and decision-making skills which is completed with a puzzle for arranging the two-digit numbers. At the end of the paper the app interface, the way it is used and its app architecture are shown

Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares