RhyGlyph is a radial visualization system that represents rhythmic interaction as three pairwise spacetime trajectories within a compact three-wedge glyph, supporting both overview and detailed reading. Building on this representation, RhyDiff is a generative framework that learns aligned latent spaces for symbolic drum patterns and the encoded trajectory features through paired VQ-VAEs and FiLM-conditioned diffusion. Users edit relational trajectories and metadata to generate new drum patterns by treating visualization itself as a control surface.

Workplace stress significantly affects employee well-being and task efficiency yet current stress recognition approaches remain impractical for real-world deployment. Existing methods mostly depend on intrusive physiological signals which require sensors attached to the body. Inspired by how humans naturally infer stress through observable behavior this PhD proposes a non-intrusive multimodal deep learning approach. This method uses overt expressive cues combining facial expressions to identify emotional valence with voice tone to capture emotional intensity and eye movements to monitor mental fatigue. To ensure reliability the system incorporates facial thermal imaging as a covert but non-intrusive cue to detect involuntary heat changes that reveal genuine stress even when a user attempts to mask their outward expression.

TickTacking is a rhythm-based interface that allows users to control a pointer in a two-dimensional space through dual-button tapping. This paper investigates the generation of human-like trajectories using a receding horizon approach applied to the TickTacking interface in a target-tracking task. By analyzing user-generated trajectories, we identify key human behavioral features and incorporate them in a controller that mimics these behaviors. The performance of this human-inspired controller is evaluated against a baseline optimal-control-based agent, demonstrating the importance of specific control features for achieving human-like interaction. These findings contribute to the broader goal of developing rhythm-based human-machine interfaces by offering design insights that enhance user performance, improve intuitiveness, and reduce interaction frustration.

In this paper, we investigate the effects of asynchrony between the visual and haptic feedback in virtual reality (VR) on user experience, specifically focusing on understanding users' awareness of this asynchrony and its effect on their level of satisfaction. Using Unreal Engine, we created an experimental setup to adjust the timing between these sensory inputs. Our experiment featured a VR dodge game that provides haptic feedback on the body when the player is hit by a multitude of virtual objects. Conducting a targeted, small-scale user study, we aim to understand in what ways an introduced asynchrony influences the VR experience. The results highlight the perceptibility of asynchrony, which significantly affects the overall user experience. Nonetheless, we also find an asymmetry that benefits scenarios where haptic feedback precedes visual cues. Furthermore, our findings suggest that users can generally accept minor levels of asynchrony without significant disadvantages to their satisfaction. However, it is interesting to note that even when users cannot explicitly identify any asynchrony, they might still experience a slight decrease in satisfaction.