QoE Aspects of Motion Tracking in Sensor Based Digital Storytelling

Abstract

Storytelling is, and always will be, an important part of cultures all around the world. It is used for education, to entertain and to preserve history. Sen- sor based digital storytelling is a new concept within Interactive and Immersive Narratives(i 2 n) where sensors are used to offer interactivity, multi-sensory stim- uli and non-linear storylines. These features are important in order to make the Participants immersed in the story, meaning that they will move to another men- tal state and perceive the story as they were a part of it. The work related to this report is researching interactivity in sensor based digital storytelling with focus on Quality of Experience(QoE). It aims to investigate how the QoE is influenced by different motion tracking systems and the level of accuracy needed in the in- teraction. The research is valuable in order to enhance the QoE in sensor based digital storytelling by getting a better understanding of the interactivity aspect.

This report describes the design and the implementation of a system for sensor based digital storytelling. The system consists of two motion tracking systems, Leap Motion and OptiTrack, a Head Mounted Display(HMD), Oculus Rift DK2, and a Storyteller Tool, the game engine Unity. In Unity three stories using this system were created in order to use them in an experiment investigating the above questions. The experiment was conducted after a methodology described in a research protocol developed as a part of this project.

OptiTrack was chosen as one of the motion tracking systems because it was of great interest to see if it was possible to use it for hand -and finger tracking in sensor based digital storytelling. The implementation showed that it was possi- ble to use OptiTrack for finger tracking, but that it was a bit difficult with the available software release TrackingTools.

In total 50 Participants conducted the experiment. Both objective and subjective measures were gathered and used in the analysis. The objective measure used was the time used to finishing the stories, while a questionnaire was used to get the subjective data. It was also saved paths for how the Participants moved their hands or head, but after conducting an expert test the data was found to difficult to analyze. The results stated that it could not be found any significant difference between the QoE of the two systems, but the tendency was that the Participants experienced OptiTrack as slightly better. The experiment also showed that the Participants had a significantly higher QoE when the level of accuracy needed in the interaction was low, compared to when the level of accuracy needed was high.