In recent years, advances in sensors, Internet and communication technologies opened many promising opportunities for remote services of industrial robots. With remote service applications, potential problems in robot systems can be detected before they happen, and prevented pro-actively avoiding costly production shut-downs. However, to be able to effectively perform remote service operations, remote support engineers need to analyse vast amounts of information. For a remote support engineer to be able to manage such amount of data, effective data visualization is crucial. Based on field studies and collected needs of support engineers working with industrial robots, we have designed and developed a prototype, i.e. one possible solution of an effective industrial fleet visualization, applied on the overheated robots use case. The initial validation of the prototype showed the benefits of such visual data presentation, as well as future directions.

Interactive mobile technologies have become part of audience experiences of live performances in terms of both general media sharing and specific (sometimes official) extra content. At the same time, high bandwidth affords streaming of live events to mobile devices. We take advantage of these technologies in our high resolution, panoramic image video stream and study a scenario of audience members viewing the very same live event they are watching on a tablet. The video stream on the tablet is navigational and enables audience members to pan and zoom in the real-time video feed. We studied audience interaction and impressions in three performances of a dance and music show and found distinct uses of the second screen video stream. We emphasize that despite initial reluctance, the observed utilization of the technology opened up for new potential practices. Our study shows how working with perceived conflict in technology can still open up design space for interactive technologies.

The possibilities of panoramic video are based on the capabilities of high-resolution digital video streams and higher bandwidth's opportunities to broadcast, stream and transfer large content across platforms. With these opportunities also come challenges such as how to focus on sub-parts of the video stream and interact with the content shown on a large screen. In this paper, we present studies of two different interaction modes with a large-scale panoramic video for live experiences; we focus on interactional challenges and explore if it is (1) possible to develop new interactional methods/ways of approaching this type of high-resolution content and (2) feasible for users to interact with the content in these new ways. We developed prototypes for two different interaction modes: an individual system on a mobile device, either a tablet or a mobile phone, for interacting with the content on the same and a non-touch gesture-based system for the home or small group interaction. We present pilot studies where we explore the possibilities and challenges with these two interaction modes for panoramic content.

Mobile broadcasting services, allowing people to stream live video from their cameraphones to viewers online, are becoming widely used as tools for user-generated content. The next generation of these services enables collaboration in teams of camera operators and a director producing an edited broadcast. This paper contributes to this research area by exploring the possibility for the director to join the camera team on location, performing mixing and broadcasting on a mobile device. The Mobile Vision Mixer prototype embodies a technical solution for connecting four camera streams and displaying them in a mixer interface for the director to select from, under the bandwidth constraints of mobile networks. Based on field trials with amateur users, we discuss technical challenges as well as advantages of enabling the director to be present on location, in visual proximity of the camera team.

In this paper we present a new method for mapping a natural speech to the lip shape animation in the real time. The speech signal, represented by MFCC vectors, is classified into viseme classes using neural networks. The topology of neural networks is automatically configured using genetic algorithms. This eliminates the need for tedious manual neural network design by trial and error and considerably improves the viseme classification results. This method is suitable for real-time and offline applications