We introduce Datamancer, a wearable device enabling bimanual gesture interaction across multi-display ubiquitous analytics environments. Datamancer addresses the gap in gesture-based interaction within data visualization settings, where current methods are often constrained by limited interaction spaces or the need for installing bulky tracking setups. Datamancer integrates a finger-mounted pinhole camera and a chest-mounted gesture sensor, allowing seamless selection and manipulation of visualizations on distributed displays. By pointing to a display, users can acquire the display and engage in various interactions, such as panning, zooming, and selection, using both hands. Our contributions include (1) an investigation of the design space of gestural interaction for physical ubiquitous analytics environments; (2) a prototype implementation of the Datamancer system that realizes this model; and (3) an evaluation of the prototype through demonstration of application scenarios, an expert review, and a user study.
https://dl.acm.org/doi/10.1145/3706598.3713123
Exoskeletons open up a unique interaction space that seamlessly integrates users' body movements with robotic actuation. Despite its potential, human-exoskeleton interaction remains an underexplored area in HCI, largely due to the lack of accessible prototyping tools that enable designers to easily develop exoskeleton designs and customized interactive behaviors. We present ExoKit, a do-it-yourself toolkit for rapid prototyping of low-fidelity, functional exoskeletons targeted at novice roboticists. ExoKit includes modular hardware components for sensing and actuating shoulder and elbow joints, which are easy to fabricate and (re)configure for customized functionality and wearability. To simplify the programming of interactive behaviors, we propose functional abstractions that encapsulate high-level human-exoskeleton interactions. These can be readily accessed either through ExoKit's command-line or graphical user interface, a Processing library, or microcontroller firmware, each targeted at different experience levels. Findings from implemented application cases and two usage studies demonstrate the versatility and accessibility of ExoKit for early-stage interaction design.
https://dl.acm.org/doi/10.1145/3706598.3713815
As the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, interaction methods are susceptible to physical motion, leading to input errors and reduced task performance. This work investigates the impact of G-forces, vibrations, and unpredictable maneuvers on 3D interaction methods. We conducted a field study with 24 participants in both stationary and moving vehicles to examine the effects of vehicle motion on four interaction methods: (1) Gaze\&Pinch, (2) DirectTouch, (3) Handray, and (4) HeadGaze. Participants performed selections in a Fitts' Law task. Our findings reveal a significant effect of vehicle motion on interaction accuracy and duration across the tested combinations of Interaction Method $\times$ Road Type $\times$ Curve Type. We found a significant impact of movement on throughput, error rate, and perceived workload. Finally, we propose future research considerations and recommendations on interaction methods during vehicle movement.
https://dl.acm.org/doi/10.1145/3706598.3714077
Automating the authoring of haptic motion effects, while enabling designers to carefully consider user feelings to provide high-quality user experiences, is crucial for effective multisensory content. We present a motion effect-tuning method that elicits desired perceptual or affective attributes from users watching a video. To this end, we test three modulation methods: (1) Altering the extent of low-frequency motion fluctuations, (2) Changing the motion amplitude in a high-frequency band, and (3) Sampling and interpolating significant motion peaks. Our tuning method transforms an input draft waveform using the modulation techniques to obtain an output motion effect that elicits the goal adjective scores. This method requires two regression models accounting for the effects of motion modulation and audiovisual stimuli, respectively, and we obtain them by conducting perceptual experiments. Lastly, we confirm the method's effectiveness through another user study and explore potential users' feedback and suggestions for future applications through open-ended survey questions.
Active transportation is a valuable tool to prevent some of the most common causes of mortality worldwide, but is severely underutilized. The primary factors preventing cyclist adoption are safety concerns, specifically, the fear of collision from automobiles. One solution to address this concern is to direct cyclists to known safe routes to minimize risk and stress, thus making cycling more approachable. However, few localized safety priors are available, hindering safety based routing. Specifically, road user behavior is unknown. To address this issue, we develop a novel handlebar attachment to passively monitor the proximity of passing cars as a an indicator of cycling safety along historically traveled routes. We deploy this sensor with 15 experienced cyclists in a 2 month longitudinal study to source a citywide map of car passing distance. We then compare this signal to both historic collisions and perceived safety reported by experienced and inexperienced cyclists.
https://dl.acm.org/doi/10.1145/3706598.3713325
Weight perception is crucial for immersive virtual reality (VR) interactions, yet providing weight feedback remains a significant research challenge. We introduce a novel weight simulation technique that leverages electrotactile stimulation to induce slip illusions. These slip illusions occur when users grip an object with less force than a predefined threshold, allowing the device to modulate the grip force and encourage a tighter grip. In our approach, heavier virtual weights correspond to higher required grip forces. We conducted a series of user experiments to validate our technique, confirming that it effectively induces slip illusions. We also investigated the relationship between electrotactile sensations and grip force, and changes in force, demonstrating that this association enhances the weight perception experience. Lastly, we explored the mapping between grip force and perceived weight, observing strong linearity within participants but notable variability between individuals.
We examine intermanual deictics, a distinctive class of gesture input characterized by an intermanual structure, asymmetric postural-manipulative articulation, and a deictic nature, drawing from both on-skin and bimanual mid-air gestures. To understand user preferences for gestures featuring these characteristics, we conducted a large-sample end-user elicitation study with 75 participants, who proposed intermanual deictics involving the opposite palm, forearm, and upper arm. Our results reveal a strong preference for physical-contact gestures primarily performed with the index finger, with strokes (62.4%) and touch input (28.8%) being most common, complemented by some preference for non-contact gestures (5.2%). We report similar agreement rates across gestures elicited in the three arm regions, averaging 26.3%, with higher agreement between the forearm and upper arm. We also present a consensus set of sixty gestures for effecting generic commands in interactive systems, along with design principles encompassing multiple practical implications for interactions that incorporate intermanual deictics.
https://dl.acm.org/doi/10.1145/3706598.3713474