Eye tracking offers many opportunities for direct device control in smart environments, but issues such as the need for calibration and the Midas touch problem make it impractical. In this paper, we propose AmbiGaze, a smart environment that employs the animation of targets to provide users with direct control of devices by gaze only through smooth pursuit tracking. We propose a design space of means of exposing functionality through movement and illustrate the concept through four prototypes. We evaluated the system in a user study and found that AmbiGaze enables robust gaze-only interaction with many devices, from multiple positions in the environment, in a spontaneous and comfortable manner

In this paper we present a smart soccer shoe that uses textile pressure sensing matrices to detect and analyze the interaction between players' foot and the ball. We describe the sensor system that consists of two 3 × 4 and one 3 × 3 matrices sampled at over 500Hz with low power electronics that allows continuous operation (incl. wireless transmission) for 8 hours using a small 800mA/h Li-Po battery. We show how relevant parameters for shot analysis such as contact speed and contact angles can be reliably derived from the sensor signals. To ensure reliable ground truth we evaluated the system with a kick robot in the adidas testing facility, which is the standard approach used by adidas to systematically and quantitatively test new shoes and balls. The test encompasses 17 different types of shots and achieves a near 100% classification accuracy/F-score. The system endured extreme levels of impact resulting in over 100km/hr ball speed.

Stress or anxiety disorders are a growing problem in industrialized countries. Those can be effectively countered by several relaxation techniques which are more effective using biofeedback. Modern virtual reality hardware provides a high level of immersion to its users. This directly affects the feeling of presence. An increased feeling of presence may allow biofeedback mechanisms to work more effectively. We build on this idea and explore how different visualizations of a user's cardiac activity in a virtual environment can be used in biofeedback scenarios - and how effective they are. Using a state-of-the-art virtual reality headset, 14 participants were subjected to four different visualizations of their own heart rate (one control visualization and three experimental visualizations). In different experiments, we examined whether they were able to estimate their heart rate based on the visualization and whether we could influence it subconsciously. Furthermore, we used the AttrakDiff questionnaire to assess the usability and attractiveness of each of the four visualizations. For the three non-reference ones, we observed significant positively correlating changes in the heart rate between the real-time true representation of the heart rate and a simulated increased heart rate visualization with a mean magnitude of 1.96 ± 0.39 beats per minute. The results from the source and number estimation experiments and the questionnaire led to the conclusion that the most appealing and best working visualization for biofeedback is a synchronized modulation/modification of the virtual environment itself.

Negative consequences of stress are a pervasive problem in our modern society. Recent developments in wearable lifestyle hardware have led to unobtrusive, sensor-packed, always-on devices that might finally be able to continuously monitor biosignals to detect, determine or even prevent stress or some of its negative outcomes. In this work, we give a concise overview of a majority of biosignals that are in some way relevant for stress classification and outline state-of-the-art machine learning algorithms for this task. Additionally, we provide a list of all recent wearables including an evaluation of their feasibility to implement such algorithms as well as directions to look for an assessment of the accuracy and validity of their recorded data with respect to stress tracking.

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.

Figure 1: Participant inside the Stroop Room in a virtual reality. Stroop instructions are displayed in a head-up display overlay and the task is to identify the correct answer/color to the instruction and select the matching colored wall.