Improving Humans' Ability to Interpret Deictic Gestures in Virtual RealityCollaborative Virtual Environments (CVEs) offer unique opportunities for human communication. Humans can interact with each other over a distance in any environment and visual embodiment they want. Although deictic gestures are especially important as they can guide other humans' attention, humans make systematic errors when using and interpreting them. Recent work suggests that the interpretation of vertical deictic gestures can be significantly improved by warping the pointing arm. In this paper, we extend previous work by showing that models enable to also improve the interpretation of deictic gestures at targets all around the user. Through a study with 28 participants in a CVE, we analyzed the errors users make when interpreting deictic gestures. We derived a model that rotates the arm of a pointing user's avatar to improve the observing users' accuracy. A second study with 24 participants shows that we can improve observers' accuracy by 22.9%. As our approach is not noticeable for users, it improves their accuracy without requiring them to learn a new interaction technique or distracting from the experience.2020SMSven Mayer et al.Carnegie Mellon University & University of StuttgartSocial & Collaborative VRImmersion & Presence ResearchCHI
Effect of Orientation on Unistroke Touch GesturesAs touchscreens are the most successful input method of current mobile devices, touch gestures became a widely used input technique. While gestures provide users with advantages to express themselves, they also introduce challenges regarding accuracy and memorability. In this paper, we investigate the effect of a gesture's orientation on how well the gesture can be performed. We conducted a study in which participants performed systematically rotated unistroke gestures. For straight lines as well as for compound lines, we found that users tend to align gestures with the primary axes. We show that the error can be described by a Clausen function with R² = .93. Based on our findings, we suggest design implications and highlight the potential for recognizing flick gestures, visualizing gestures and improving recognition of compound gestures.2019SMSven Mayer et al.University of StuttgartHand Gesture RecognitionFull-Body Interaction & Embodied InputCHI
Investigating the Feasibility of Finger Identification on Capacitive Touchscreens using Deep LearningTouchscreens enable intuitive mobile interaction. However, touch input is limited to 2D touch locations which makes it challenging to provide shortcuts and secondary actions similar to hardware keyboards and mice. Previous work presented a wide range of approaches to provide secondary actions by identifying which finger touched the display. While these approaches are based on external sensors which are inconvenient, we use capacitive images from mobile touchscreens to investigate the feasibility of finger identification. We collected a dataset of low-resolution fingerprints and trained convolutional neural networks that classify touches from eight combinations of fingers. We focused on combinations that involve the thumb and index finger as these are mainly used for interaction. As a result, we achieved an accuracy of over 92% for a position-invariant differentiation between left and right thumbs. We evaluated the model and two use cases that users find useful and intuitive. We publicly share our data set (CapFingerId) comprising 455,709 capacitive images of touches from each finger on a representative mutual capacitive touchscreen and our models to enable future work using and improving them.2019HLHuy Viet Le et al.Force Feedback & Pseudo-Haptic WeightHand Gesture RecognitionIUI
Online, VR, AR, Lab, and In-Situ: Comparison of Research Methods to Evaluate Smart ArtifactsEmpirical studies are a cornerstone of HCI research. Technical progress constantly enables new study methods. Online surveys, for example, make it possible to collect feedback from remote users. Progress in augmented and virtual reality enables to collect feedback with early designs. In-situ studies enable researchers to gather feedback in natural environments. While these methods have unique advantages and disadvantages, it is unclear if and how using a specific method affects the results. Therefore, we conducted a study with 60 participants comparing five different methods (online, virtual reality, augmented reality, lab setup, and in-situ) to evaluate early prototypes of smart artifacts. We asked participants to assess four different smart artifacts using standardized questionnaires. We show that the method significantly affects the study result and discuss implications for HCI research. Finally, we highlight further directions to overcome the effect of the used methods.2019AVAlexandra Voit et al.University of StuttgartUser Research Methods (Interviews, Surveys, Observation)Field StudiesCHI
Investigating the Effect of Orientation and Visual Style on Touchscreen Slider PerformanceSliders are one of the most fundamental components used in touchscreen user interfaces (UIs). When entering data using a slider, errors occur due e.g. to visual perception, resulting in inputs not matching what is intended by the user. However, it is unclear if the errors occur uniformly across the full range of the slider or if there are systematic offsets. We conducted a study to assess the errors occurring when entering values with horizontal and vertical sliders as well as two common visual styles. Our results reveal significant effects of slider orientation and style on the precision of the entered values. Furthermore, we identify systematic offsets that depend on the visual style and the target value. As the errors are partially systematic, they can be compensated to improve users' precision. Our findings provide UI designers with data to optimize user experiences in the wide variety of application areas where slider based touchscreen input is used.2019ACAshley Colley et al.University of Lapland360° Video & Panoramic ContentPrototyping & User TestingCHI
The Effect of Offset Correction and Cursor on Mid-Air Pointing in Real and Virtual EnvironmentsPointing at remote objects to direct others' attention is a fundamental human ability. Previous work explored methods for remote pointing to select targets. Absolute pointing techniques that cast a ray from the user to a target are affected by humans' limited pointing accuracy. Recent work suggests that accuracy can be improved by compensating systematic offsets between targets a user aims at and rays cast from the user to the target. In this paper, we investigate mid-air pointing in the real world and virtual reality. Through a pointing study, we model the offsets to improve pointing accuracy and show that being in a virtual environment affects how users point at targets. In the second study, we validate the developed model and analyze the effect of compensating systematic offsets. We show that the provided model can significantly improve pointing accuracy when no cursor is provided. We further show that a cursor improves pointing accuracy but also increases the selection time.2018SMSven Mayer et al.University of StuttgartFull-Body Interaction & Embodied InputEye Tracking & Gaze InteractionCHI
PalmTouch: Using the Palm as an Additional Input Modality on Commodity SmartphonesTouchscreens are the most successful input method for smartphones. Despite their flexibility, touch input is limited to the location of taps and gestures. We present PalmTouch, an additional input modality that differentiates between touches of fingers and the palm. Touching the display with the palm can be a natural gesture since moving the thumb towards the device's top edge implicitly places the palm on the touchscreen. We present different use cases for PalmTouch, including the use as a shortcut and for improving reachability. To evaluate these use cases, we have developed a model that differentiates between finger and palm touch with an accuracy of 99.53% in realistic scenarios. Results of the evaluation show that participants perceive the input modality as intuitive and natural to perform. Moreover, they appreciate PalmTouch as an easy and fast solution to address the reachability issue during one-handed smartphone interaction compared to thumb stretching or grip changes.2018HLHuy Viet Le et al.University of StuttgartMid-Air Haptics (Ultrasonic)Hand Gesture RecognitionFoot & Wrist InteractionCHI
Evaluating the Disruptiveness of Mobile Interactions: A Mixed-Method ApproachWhile the proliferation of mobile devices has rendered mobile notifications ubiquitous, researchers are only slowly beginning to understand how these technologies affect everyday social interactions. In particular, the negative social influence of mobile interruptions remains unexplored from a methodological perspective. This paper contributes a mixed-method evaluation procedure for assessing the disruptive impact of mobile interruptions in conversation. The approach combines quantitative eye tracking, qualitative analysis, and a simulated conversation environment to enable fast assessment of disruptiveness. It is intended to be used as a part of an iterative interaction design process. We describe our approach in detail, present an example of its use to study a new call declining technique, and reflect upon the pros and cons of our approach.2018SMSven Mayer et al.University of StuttgartNotification & Interruption ManagementComputational Methods in HCICHI
Pac-Many: Movement Behavior when Playing Collaborative and Competitive Games on Large DisplaysPrevious work has shown that large high resolution displays (LHRDs) can enhance collaboration between users. As LHRDs allow free movement in front of the screen, an understanding of movement behavior is required to build successful interfaces for these devices. This paper presents Pac-Many; a multiplayer version of the classical computer game Pac-Man to study group dynamics when using LHRDs. We utilized smartphones as game controllers to enable free movement while playing the game. In a lab study, using a 4m × 1m LHRD, 24 participants (12 pairs) played Pac-Many in collaborative and competitive conditions. The results show that players in the collaborative condition divided screen space evenly. In contrast, competing players stood closer together to avoid benefits for the other player. We discuss how the nature of the task is important when designing and analyzing collaborative interfaces for LHRDs. Our work shows how to account for the spatial aspects of interaction with LHRDs to build immersive experiences.2018SMSven Mayer et al.University of StuttgartGame UX & Player BehaviorMultiplayer & Social GamesCHI
Fingers' Range and Comfortable Area for One-Handed Smartphone Interaction Beyond the TouchscreenPrevious research and recent smartphone development presented a wide range of input controls beyond the touchscreen. Fingerprint scanners, silent switches, and Back-of-Device (BoD) touch panels offer additional ways to perform input. However, with the increasing amount of input controls on the device, unintentional input or limited reachability can hinder interaction. In a one-handed scenario, we conducted a study to investigate the areas that can be reached without losing grip stability (comfortable area), and with stretched fingers (maximum range) using four different phone sizes. We describe the characteristics of the comfortable area and maximum range for different phone sizes and derive four design implications for the placement of input controls to support one-handed BoD and edge interaction. Amongst others, we show that the index and middle finger are the most suited fingers for BoD interaction and that the grip shifts towards the top edge with increasing phone sizes.2018HLHuy Viet Le et al.University of StuttgartFoot & Wrist InteractionPrototyping & User TestingCHI
The Effect of Road Bumps on Touch Interaction in CarsTouchscreens are a common fixture in current vehicles. With autonomous driving, we can expect touch interaction with such in-vehicle media systems to exponentially increase. In spite of vehicle suspension systems, road perturbations will continue to exert forces that can render in-vehicle touch interaction challenging. Using a motion simulator, we investigate how different vehicle speeds interact with road features (i.e., speed bumps) to influence touch interaction. We determine their effect on pointing accuracy and task completion time. We show that road bumps have a significant effect on touch input and can decrease accuracy by 19%. In light of this, we developed a Random Forest (RF) model that improves touch accuracy by 32.0% on our test set and by 22.5% on our validation set. As the lightweight model uses only features that can easily be determined through inertial measurement units, this model could be easily deployed in current automobiles.2018SMSven Mayer et al.In-Vehicle Haptic, Audio & Multimodal FeedbackAutoUI
<i>InfiniTouch:</i> Finger-Aware Interaction on Fully Touch Sensitive SmartphonesSmartphones are the most successful mobile devices and offer intuitive interaction through touchscreens. Current devices treat all fingers equally and only sense touch contacts on the front of the device. In this paper, we present InfiniTouch, the first system that enables touch input on the whole device surface and identifies the fingers touching the device without external sensors while keeping the form factor of a standard smartphone. We first developed a prototype with capacitive sensors on the front, the back and on three sides. We then conducted a study to train a convolutional neural network that identifies fingers with an accuracy of 95.78% while estimating their position with a mean absolute error of 0.74cm. We demonstrate the usefulness of multiple use cases made possible with InfiniTouch, including finger-aware gestures and finger flexion state as an action modifier.2018HLHuy Viet Le et al.In-Vehicle Haptic, Audio & Multimodal FeedbackHand Gesture RecognitionFull-Body Interaction & Embodied InputUIST