Dennis Guse

PalmSpace: continuous around-device gestures vs. multitouch for 3D rotation tasks on mobile devices

Rotating 3D objects is a difficult task on mobile devices, because the task requires 3 degrees of freedom and (multi-)touch input only allows for an indirect mapping. We propose a novel style of mobile interaction based on mid-air gestures in proximity of the device to increase the number of DOFs and alleviate the limitations of touch interaction with mobile devices. While one hand holds the device, the other hand performs mid-air gestures in proximity of the device to control 3D objects on the mobile devices screen. A flat hand pose defines a virtual surface which we refer to as the PalmSpace for precise and intuitive 3D rotations. We constructed several hardware prototypes to test our interface and to simulate possible future mobile devices equipped with depth cameras. We conducted a user study to compare 3D rotation tasks using the most promising two designs for the hand location during interaction -- behind and beside the device -- with the virtual trackball, which is the current state-of-art technique for orientation manipulation on touch-screens. Our results show that both variants of PalmSpace have significantly lower task completion times in comparison to the virtual trackball.

On the Applicability of Computer Vision based Gaze Tracking in Mobile Scenarios

Gaze tracking is a common technique to study user interaction but is also increasingly used as input modality. In this regard, computer vision based systems provide a promising low-cost realization of gaze tracking on mobile devices. This paper complements related work focusing on algorithmic designs by conducting two users studies aiming to i) independently evaluate EyeTab as promising gaze tracking approach and ii) by providing the first independent use case driven evaluation of its applicability in mobile scenarios. Our evaluation elucidates the current state of mobile computer vision based gaze tracking and aims to pave the way for improved algorithms. In this regard, we aim to further foster the development by releasing our source data as reference database open to the public.

Temporal Development of Quality of Experience

Most research on Quality of Experience treats QoE as a static event. As a result, QoE is measured for stimuli of delimited length, and the QoE which is associated with the stimulus is considered to be stable along its duration. However, this rarely happens in reality where usage episodes extend over several seconds and minutes (e.g. a phone call), hours (e.g. a video film), or regularly over periods of weeks or months (when considering QoE of a subscribed service). In this chapter, we will discuss the cognitive processes involved when QoE is integrated over usage episodes, and describe corresponding assessment methods. We will also review models for estimating episodic and multi-episodic QoE from momentary QoE judgments or their predictions.

Web-QOE under real-world distractions: Two test cases

Real-world usage of web browsing differs considerably from typically employed laboratory tests on single or multiple page views. Especially when using mobile devices such as smartphones or tablets, several sources of distraction are prevalent while browsing. In the mobile scenario, users are exposed to distracting factors like other people, traffic, announcements etc., whereas at home parallel use of TV or radio services might distract the user. To assess the impact due to context and distractions, this paper presents two studies of web browsing Quality of Experience. The studied factors of possible influence on QoE include one specific browsing task, the environment and a dedicated distraction task. The results show that, in contrast to ratings for multimedia sessions containing audio, video or speech, QoE ratings for web browsing are not affected by the considered contexts or distractions. However, it was found that the primary task for the browsing session had a significant influence on the QoE ratings regardless of the environment or distracting task.

Insensitivity to Network Delay: Minecraft Gaming Experience of Casual Gamers

Assessing the impact of network delay on perceived quality of gaming has been subject to many studies involving different genres ranging from fast-paced first-person shooters to strategy games. This paper assesses the impact of network latency on the Quality of Experience (QoE) of casual gamers playing Minecraft. It is based on a user study involving 12 casual gamers with no prior experience with Minecraft. QoE is assessed using the Game Experience Questionnaire (GEQ) and dedicated questions for the overall perceived quality and experienced gameplay interruptions. The main finding is that casual Minecraft players are rather insensitive to network delay of up to 1 sec.

Identifying Speech Quality Dimensions in a Telephone Conversation

Speech telecommunication services are traditionally used for communication between two interlocutors interacting in a conversation. Thus, the quality of transmitted speech in a conversational situation, as perceived by the end-users, is the important indicator for service providers to evaluate their systems. In this context, it is not enough to only provide information about the overall quality but also to indicate reasons and sources for quality losses. In this article, we present an approach towards analyzing speech quality in a conversational situation by dividing a conversation into three separate phases and identifying corresponding quality-relevant perceptual dimensions, as perceived by the system users. The identified dimensions can be combined for the overall quality assessment and may separately be used to diagnose the technical reasons of quality degradations. For this, four separate subjective experiments to uncover the underlying dimensions in each conversational phase are conducted. The resulting quality-profile, consisting of seven perceptual dimensions, is then validated in an extensive conversational experiment triggering all three phases of a conversation using a new proposed test-paradigm. This allows deeply analyzing conversational speech quality for diagnosis and optimization of telecommunication systems and provides the fundamentals for instrumental diagnostic conversational speech quality measures.

Modelling multi-episodic quality perception for different telecommunication services: First insights

Most studies on the perceptual quality evaluation of multi-media services are limited to short media samples or episodes, although typical usage of services occurs in a more regular way over longer periods of exposure (weeks, months or years). So far, it is unclear how the experience of individual episodic use is integrated to form an experience of one service as a whole, spanning over multiple usage episodes. In this paper, we present two studies on the quality experienced during multi-episodic usage of different media and communication services, and first approaches to model multi-episodic quality judgments. An analysis of the data shows that the particular instance of episodic judgment strongly affects the integration function, which makes it difficult to derive models of general validity. Ways to over-come the observed limitations are discussed.

Towards training naïve participants for a perceptual annotation task designed for experts

Technical Causes Analysis (P.TCA) is a method for identifying technical causes of sub-optimum speech transmission quality. Originally created as an expert procedure for the annotation of speech samples, its applicability to naïve listener was also studied. Due to the low agreement of naïve listener annotations, it was suggested that detailed training methods are necessary to lift naïve annotations to an agreement level of experts. The aim of this work was to develop training methods for naïve annotators. For this, two different training procedures were developed and tested in two separate annotation experiments. The results are analyzed and discussed regarding the effects of the trainings and their implications for the P.TCA annotation scheme. The outcome shows that these training methods did not meet the expectations for improving the inter-rater agreement of naïve annotators. It is concluded that trainings of 15 to 20 minutes rather confuse naïve annotators by conveying too much information in too little time, and that they are not sufficient to prepare naïve annotators. It is argued that much more extensive training is needed to raise naïve annotators to expert level, and that such a training must include both, in-depth introduction to the annotation process as well as detailed presentation and exercise regarding the P.TCA degradations.