From carrying grocery bags to holding onto handles on the bus, there are a variety of situations where one or both hands are busy, hindering the vision of ubiquitous interaction with technology. Voice commands, as a popular hands-free alternative, struggle with ambient noise and privacy issues. As an alternative approach, research explored movements of various body parts (e.g., head, arms) as input modalities, with foot-based techniques proving particularly suitable for hands-free interaction. Whereas previous research only considered the movement of the foot as a whole, in this work, we argue that our toes offer further degrees of freedom that can be leveraged for interaction. To explore the viability of toe-based interaction, we contribute the results of a controlled experiment with 18 participants assessing the impact of five factors on the accuracy, efficiency and user experience of such interfaces. Based on the findings, we provide design recommendations for future toe-based interfaces.

Mid-air gestures, widely used in today’s Augmented Reality (AR) applications, are prone to the “gorilla arm” effect, leading to discomfort with prolonged interactions. While prior work has proposed metrics to quantify this effect and means to improve comfort and ergonomics, these works usually only consider simplistic, one-dimensional AR interactions, like reaching for a point or pushing a button. However, interacting with AR environments also involves far more complex tasks, such as rotational knobs, potentially impacting ergonomics. This paper advances the understanding of the ergonomics of rotational mid-air interactions in AR. For this, we contribute the results of a controlled experiment exposing the participants to a rotational task in the interaction space defined by their arms’ reach. Based on the results, we discuss how novel future mid-air gesture modalities benefit from our findings concerning ergonomic-aware rotational interaction.

Movement guidance in virtual reality has many applications ranging from physical therapy, assistive systems to sport learning. These movements range from simple single-limb to complex multi-limb movements. While VR supports many perspectives – e.g., first person and third person – it remains unclear how accurate these perspectives communicate different movements. In a user study (N=18), we investigated the influence of perspective, feedback, and movement properties on the accuracy of movement guidance. Participants had on average an angle error of 6.2° for single arm movements, 7.4° for synchronous two arm movements, and 10.3° for synchronous two arm and leg movements. Furthermore, the results show that the two variants of third-person perspectives outperform a first-person perspective for movement guidance (19.9% and 24.3% reduction in angle errors). Qualitative feedback confirms the quantitative data and shows users have a clear preference for third-person perspectives. Through our findings we provide guidance for designers and developers of future VR movement guidance systems.

When we get lost in Virtual Reality (VR) or want to return to a previous location, we use the same methods of locomotion for the way back as for the way forward. This is time-consuming and requires additional physical orientation changes, increasing the risk of getting tangled in the headsets’ cables. In this paper, we propose the use of undo actions to revert locomotion steps in VR. We explore eight different variations of undo actions as extensions of point&teleport, based on the possibility to undo position and orientation changes together with two different visualizations of the undo step (discrete and continuous). We contribute the results of a controlled experiment with 24 participants investigating the efficiency and orientation of the undo techniques in a radial maze task. We found that the combination of position and orientation undo together with a discrete visualization resulted in the highest efficiency without increasing orientation errors.

Electrodermal activity (EDA) reflects changes in skin conductance, which are closely tied to human psychophysiological states. For example, EDA sensors can assess stress, cognitive workload, arousal, or other measures tied to the sympathetic nervous system for interactive human-centered applications. Yet, current limitations involve the complex attachment and proper skin contact with EDA sensors. This paper explores the concept of 3D printing electrodes for EDA measurements, integrating sensors into arbitrary 3D-printed objects, alleviating the need for complex assembly and attachment. We examine the adaptation of conventional EDA circuits for 3D-printed electrodes, assessing different electrode shapes and their impact on the sensing accuracy. A user study (N=6) revealed that 3D-printed electrodes can measure EDA with similar accuracy, suggesting larger contact areas for improved precision. We derive design implications to facilitate the integration of EDA sensors into 3D-printed devices to foster diverse integration into everyday objects for prototyping physiological interfaces.