The growing dimensions of smartphones have intensified the challenges associated with screen reachability. Back-of-device (BoD) interaction expands the range of reachability and offers a promising solution to mitigate screen occlusion while enhancing one-handed interactions. However, much of the existing research relies on incorporating additional hardware components. In this paper, we present SwipeSense a technique for exploring the feasibility of directional swipe interactions on the back of devices, utilizing built-in inertial measurement unit (IMU) sensors and machine learning models. We conducted a user study with 12 participants who performed 9600 BoD swipes in 8 distinct directions while holding the device naturally. The results of our machine learning models indicate that various directional swipes on the back of the device can be accurately distinguished using only the built-in IMU sensors of the phone, achieving a range of model accuracy between 72% and 95%. Furthermore, we showcase potential applications for these gestures.

Color design is essential in areas such as product, graphic, and fashion design. However, current tools like Photoshop, with their concrete-driven color manipulation approach, often stumble during early ideation, favoring polished end results over initial exploration. We introduced Mondrian as a test-bed for abstraction-driven approach using interactive color palettes for image colorization. Through a formative study with six design experts, we selected three design options for visual abstractions in color design and developed Mondrian where humans work with abstractions and AI manages the concrete aspects. We carried out a user study to understand the benefits and challenges of each abstraction format and compare the Mondrian with Photoshop. A survey involving 100 participants further examined the influence of each abstraction format on color composition perceptions. Findings suggest that interactive visual abstractions encourage a non-linear exploration workflow and an open mindset during ideation, thus providing better creative affordance.

We compare the performance and level of distraction of expressive directional gesture input in the context of in-vehicle system commands. Center console touchscreen swipes and midair swipe-like movements are tested in 8-directions, with 8-button touchscreen tapping as a baseline. Participants use these input methods for intermittent target selections while performing the Lane Change Task in a virtual driving simulator. Input performance is measured with time and accuracy, cognitive load with deviation of lane position and speed, and distraction from frequency of off-screen glances. Results show midair gestures were less distracting and faster, but with lower accuracy. Touchscreen swipes and touchscreen tapping are comparable across measures. Our work provides empirical evidence for vehicle interface designers and manufacturers considering midair or touch directional gestures for centre console input.

Selecting a proper color palette is critical in crafting a high-quality graphic design to gain visibility and communicate ideas effectively. To facilitate this process, we propose De-Stijl, an intelligent and interactive color authoring tool to assist novice designers in crafting harmonic color palettes, achieving quick design iterations, and fulfilling design constraints. Through De-Stijl, we contribute a novel 2D color palette concept that allows users to intuitively perceive color designs in context with their proportions and proximities. Further, De-Stijl implements a holistic color authoring system that supports 2D palette extraction, theme-aware and spatial-sensitive color recommendation, and automatic graphical elements (re)colorization. We evaluated De-Stijl through an in-lab user study by comparing the system with existing industry standard tools, followed by in-depth user interviews. Quantitative and qualitative results demonstrate that De-Stijl is effective in assisting novice design practitioners to quickly colorize graphic designs and easily deliver several alternatives.

Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.

We present EdgeSelect, a linear target selection interaction technique that utilizes a small portion of the smartwatch display, explicitly designed to mitigate the ‘fat finger’ and screen occlusion problems, two of the most common and well-known challenges when interacting with small displays. To design our technique, we first conducted a user study to answer which segments of the smartwatch display have the least screen occlusion while users are interacting with it. We use results from the first experiment to introduce EdgeSelect, a three-layer non-linear interaction technique, which can be used to interact with multiple co-adjacent graphs on the smartwatch by using a region that is the least prone to finger occlusion. In a second experiment, we explore the density limits of the targets possible with EdgeSelect. Finally, we demonstrate the generalizability of EdgeSelect to interact with various types of content.

Supporting eyes-free interaction, mobility and encumbrance, while providing a broad set of commands on a smartwatch display is a difficult, yet important, task. Bezel-to-bezel (B2B) gestures are valuable for rapid command invocation during eyes-free operation, however we lack knowledge regarding B2B interactions on circular devices during common usage scenarios. We aim to improve our understanding of the dynamics of B2B interactions in these scenarios by conducting two studies and a third analysis: First, we explore the performance of B2B in a seated position; second, we explore the effect of mobility and encumbrance on the B2B interaction; finally, we improve on the B2B accuracies by calculating features and utilizing machine learning. With the limited interaction capabilities on smartwatches and the importance of the scenario of use, we conclude with applications and design guidelines for improved utilization of B2B that enables effective smartwatch control while in common, mobile and eyes-free scenarios.

We present BezelGlide, a novel suite of bezel interaction techniques, designed to minimize screen occlusion and ‘fat finger’ effects, when interacting with common graphs on smartwatches. To explore the design of BezelGlide, we conducted two user studies. First, we quantified the amount of screen occlusion experienced when interacting with the smartwatch bezel. Next, we designed two techniques that involve gliding the finger along the smartwatch bezel for graph interaction. Full BezelGlide (FBG) and Partial BezelGlide (PBG), use the full or a portion of the bezel, respectively, to reduce screen occlusion while scanning a line chart for data. In the common value detection task, we find that PBG outperforms FBG and Shift, a touchscreen occlusion-free technique, both quantitatively and subjectively, also while mobile. We finally illustrate the generzability potential of PBG to interact with common graph types making it a valuable interaction technique for smartwatch users.

Multiple embedded sensors enable smartwatch apps to amass large amounts of interrelated time-series data simultaneously, such as heart rate, oxygen levels or steps walked. Visualizing multiple interlinked datasets is possible on smartphones but remains challenging on small smartwatch displays. We propose a new technique, the Space-Filling Line Graph (SF-LG), that preserves the key visual properties of time-series graphs while making available space on the display to augment such graphs with additional information. Results from our first study (N=30) suggest that, while SF-LG makes available additional space on the small display, it also enables effective (i.e. quick and accurate) comprehension of key line graph tasks. We next implement a greedy algorithm to embed auxiliary information in the most suitable regions on the display. In a second study (N=27), we find that participants are efficient at locating and linking interrelated content using SF-LG in comparison to two baselines approaches. We conclude with guidelines for smartwatch space maximization for visual displays.

Hierarchy structures such as file systems are widespread interfaces for item retrieval and selection tasks. Some hierarchies can be modified by end-users, such as application launchers on smartphones or pictures in a file folder. These modifiable hierarchies cannot benefit from an optimization made beforehand as their content, unknown during the design process, is constantly evolving. We hence propose an analytic model which designers can integrate in their system to recommend a range of local structure modifications (e.g., creating new folders) to end-users. Proposing a range of modifications gives flexibility to end-users regarding their own meaningful grouping and labeling choices to follow a recommendation. A first experiment confirms that the recommendations built on our model can lead to modified hierarchies resulting in faster theoretical selection times. A second experiment confirms that the theoretical selection times fit empirical selection times in different hierarchy visual layouts: linear, radial, and grid.

The use of crowdsourcing platforms for data collection in HCI research is attractive in their ability to provide rapid access to large and diverse participant samples. As a result, several researchers have conducted studies investigating the similarities and differences between data collected through crowdsourcing and more traditional, laboratory-style data collection. We add to this body of research by examining the feasibility of conducting social acceptability studies via crowdsourcing. Social acceptability can be a key determinant for the early adoption of emerging technologies, and as such, we focus our investigation on social acceptability for Head-Worn Display (HWD) input modalities. Our results indicate that data collected via a crowdsourced experiment and a laboratory-style setting did not differ at a statistically significant level. These results provide initial support for crowdsourcing platforms as viable options for conducting social acceptability research.

The popularity of head-worn displays (HWD) technologies such as Virtual Reality (VR) and Augmented Reality (AR) headsets is growing rapidly. To predict their commercial success, it is essential to understand the acceptability of these new technologies, along with new methods to interact with them. In this vein, the evaluation of social acceptability of interactions with these technologies has received significant attention, particularly from the performer's (i.e., user's) viewpoint. However, little work has considered social acceptability concerns from observers' (i.e., spectators') perspective. Although HWDs are designed to be personal devices, interacting with their interfaces are often quite noticeable, making them an ideal platform to contrast performer and observer perspectives on social acceptability. Through two studies, this paper contrasts performers' and observers' perspectives of social acceptability interactions with HWDs under different social contexts. Results indicate similarities as well as differences, in acceptability, and advocate for the importance of including both perspectives when exploring social acceptability of emerging technologies. We provide guidelines for understanding social acceptability specifically from the observers' perspective, thus complementing our current practices used for understanding the acceptability of interacting with these devices.