Typical success-rate prediction models for tapping exclude targets near screen edges; however, design constraints often force such placements. Additionally, in scrollable UIs any element can move close to an edge. In this work, we model how target–edge distance affects 1D touch pointing accuracy. We propose the Skewed Dual Normal Distribution Model, which assumes the tap coordinate distribution is skewed by a nearby edge. The results of two smartphone experiments showed that, as targets approached the edge, the distribution’s peak shifted toward the edge and its tail extended away. In contrast to prior reports, the success rate improved when the target touched the edge, suggesting a strategy of “tapping the target together with the edge.” By accounting for skew, our model predicts success rates across a wide range of conditions, including edge‑adjacent targets, thus extending coverage to the whole screen and informing UI design support tools.

While music recommendation systems effectively deliver music tailored to individual preferences, they face challenges such as filter bubbles, where users are recommended only music matching their preferences, and a reduction in serendipitous musical encounters. In this research, as an approach to these challenges, we reinterpret the Music Leak system in today’s music streaming environment, which we originally proposed at WISS2007 in 2007, focusing on the phenomenon of “sound leakage” in daily life. Music Leak is a system that automatically shares the music a user is listening to with nearby people using the same system via FM radio waves. In evaluation experiments, 75% of users expressed interest in using the system, and 71% showed interest in music from people they did not know. Additionally, all test subjects were able to identify songs even in environments where different genres of music were playing simultaneously. The distinctive feature of this system is that it provides a music sharing experience with a sense of physical distance, creating qualitatively different musical encounters from algorithmic recommendations by sharing information with strong reality—music that ‘someone’ is listening to ‘now’ and ‘there’. This research proposes a new approach to promote ‘unexpected discoveries’ in music recommendation, demonstrating the potential of musical experiences through ‘unintentional sharing’ through implementation and evaluation.

This study investigated the use of the deformation of the fingertips of a virtual hand to enhance depth perception during freehand interaction in a virtual reality (VR). Artificial fingertip deformation may generate mapping of real hand position and pseudo-haptics, improving UI usability. We conducted two experiments focusing on depth manipulation in both pointing and steering tasks. Our results revealed that changes in fingertip shape reduced operation time in pointing tasks and improved accuracy in steering tasks. Additionally, we conducted subjective evaluation surveys for both experiments, which showed improvements in pseudo-haptics, spatial perception, and user experience. Based on these results, we propose several applications and demonstrate that fingertip deformations in virtual hands can contribute to better 3D UI design.

In Fitts’ law studies to investigate pointing, throughput is used to characterize the performance of input devices and users, which is claimed to be independent of task difficulty or the user’s subjective speed-accuracy bias. While throughput has been recognized as a useful metric for target-pointing tasks, the corresponding formulation for path-steering tasks and its evaluation have not been thoroughly examined in the past. In this paper, we conducted three experiments using linear, circular, and sine-wave path shapes to propose and investigate a novel formulation for the effective parameters and the throughput of steering tasks. Our results show that the effective width substantially improves the fit to data with mixed speed-accuracy biases for all task shapes. Effective width also smoothed out the throughput across all biases, while the usefulness of the effective amplitude depended on the task shape. Our study thus advances the understanding of user performance in trajectory-based tasks.

Steering a cursor through a constrained path is required for operating graphical user interfaces, such as when navigating a cascaded menu. Recently, the accuracy with which users successfully accomplish a task is emerging as an important performance indicator. In this study, we evaluated the performance of a Servo-Gaussian model to predict the success rates in linear and circular paths with 212 and 166 crowdsourced participants, respectively. The results showed that, for linear paths, the model achieved r2=0.9676, MAE=2.036%, and RMSE=2.692%, and for circular paths, it achieved r2=0.9787, MAE=3.199%, and RMSE=3.927%. Shuffle-split cross-validation with five train-test data-size ratios also demonstrated the robust prediction accuracy of the model. These findings will provide designers with a useful tool to judge if an interface can be accurately operated without running costly user studies for new task conditions.

Previous studies on the Finger-Fitts law (FFitts law) are lacking in sufficient experiments to verify its inherent potential. Since the FFitts law is originally a modified version of the effective width method to normalize speed-accuracy biases, the model fit would improve if multiple biases were mixed together and the throughputs would be more stable than using the nominal target width. In this study, we conduct an experiment in which participants tap 1D-bar and 2D-circular targets under three subjective biases: balancing the speed and accuracy, emphasizing speed, and emphasizing accuracy when they perform the tasks. The results showed that applying the effective width to Ko et al.'s refined FFitts law, which represents the touch ambiguity with a free parameter, was the most successful in normalizing biases. Reanalyzing another dataset on ray-casting pointing also led to the same conclusion. We thus recommend using Ko et al.'s model with effective width when researchers compare several experimental conditions such as devices and user groups.

In typical experiments to evaluate novel pointing-facilitation techniques, participants are asked to perform a task as rapidly and accurately as possible. However, the balance can differ among participants, and the techniques’ effectiveness would change if the majority of participants give weight to either speed or accuracy. We investigated the effects of three subjective biases (emphasizing speed, neutral, and emphasizing accuracy) on the evaluation results of pointing-facilitation techniques, namely Bubble Cursor and Bayesian Touch Criterion (BTC). The results indicate that Bubble Cursor outperformed the baseline in terms of movement time and error rate under all bias conditions, while BTC underperformed a simpler target-prediction technique, which was an inconsistent outcome to the original study. Examining multiple biases enables researchers to discuss the (dis)advantages of novel or existing techniques more precisely, which can be beneficial to reach a more reliable conclusion.

The effective width method of Fitts’ law can normalize speed-accuracy biases in 1D target pointing tasks. However, in graphical user interfaces, more meaningful target shapes are rectangular. To empirically determine the best way to normalize the subjective biases, we ran remote and crowdsourced user experiments with three speed-accuracy instructions. We propose to normalize the speed-accuracy biases by applying the effective sizes to existing Fitts’ law formulations including width W and height H. We call this target-size adjustment the bivariate effective width method. We found that, overall, Accot and Zhai’s weighted Euclidean model using the effective width and height independently showed the best fit to the data in which the three instruction conditions were mixed (i.e., the time data measured in all instructions were analyzed with a single regression expression). Our approach enables researchers to fairly compare two or more conditions (e.g., devices, input techniques, user groups) with the normalized throughputs.

We prototyped a seasoning appliance that applies a “taste display” technology that employs a taste sensor to reproduce flavors via the spraying and subsequent mixing of colored, flavored liquids to create a printed image on the surface of another food. For example, when toasted bread is used as the medium, the appliance changes its appearance and taste into other food items, such as pizza or chocolate brownie, and the user can then virtually eat that food.

In this paper, we reproduce the taste of a crab cream croquette, which are harmful to people with shellfish allergies using TTTV2 (Transform the Taste and Visual Appearance), which is a taste-reproduction technology using the mixing and spraying of solutions. Participants recognized the food from only the taste. It is possible to safely taste flavors virtually by reproducing them through the mixing of safe materials that do not contain allergens.

VR content does not have to follow reality entirely, thus creating a variety of experiences, and the same should be true for content for taste displays. It may be worthwhile to create experiences that cannot be experienced in the real world. For example, many VR games create thrill and excitement through experiences that would be life-threatening in the real world. In this context, we developed taste content that allows users to safely experience the taste of poisonous mushrooms.

This study used front-camera face tracking to realize interactions such as blowing on a smartphone to make it foggy or tapping on it to make it puff. We used the parameters of the lower jaw opening, the lip closing, and the lip stretching to identify these actions. Additionally, we enabled pointing based on the position and direction of the face and implemented four applications using this interface.

We propose a method that predicts the success rate in pointing to 2D rectangular targets by using 1D vertical-bar and horizontal-bar task results. The method can predict the success rates for more practical situations under fewer experimental conditions. This shortens the duration of experiments, thus saving costs for researchers and practitioners. We verified the method through two experiments: laboratory-based and crowdsourced ones. In the laboratory-based experiment, we found that using 1D task results to predict the success rate for 2D targets slightly decreases the prediction accuracy. In the crowdsourced experiment, this method scored better than using 2D task results. Thus, we recommend that researchers use the method properly depending on the situation.

Models of lassoing time to select multiple square icons exist, but realistic lasso tasks also typically involve encircling non-rectangular objects. Thus, it is unclear if we can apply existing models to such conditions where, e.g., the width of the path that users want to steer through changes dynamically or step-wise. In this work, we conducted two experiments where the objects were non-rectangular, with path widths that narrowed or widened, smoothly or step-wise. The results showed that the baseline models for pen-steering movements (the steering and crossing law models) fitted the timing data well, but also that segmenting width-changing areas led to significant improvements. Our work enables the modeling of novel UIs requiring continuous strokes, e.g., for grouping icons.

In touch interfaces, a target, such as an icon, has two widths: the visual width and the touchable width. The visual width is the target's appearance, and the touchable width is the area in which users can touch a target and execute an action. In this study, we conduct two experiments to investigate the effects of the visual and touchable widths on touch pointing performance (movement time and success rate). Based on the results, we build candidate models for predicting the movement time and compare them by the values of adjusted R^2 and AIC. In addition, we build a success rate model and test it through cross-validation. Existing models can be applied only to situations where the visual and touchable widths are equal, and we show that our refined model achieves better model fitness, even when such widths are different. We also discuss the design implications of the touch interfaces based on our models.

The steering law is a model for predicting the time and speed for passing through a constrained path. When people can view only a limited range of the path forward, they limit their speed in preparation of possibly needing to turn at a corner. However, few studies have focused on how limited views affect steering performance, and no quantitative models have been established. The results of a mouse steering study showed that speed was linearly limited by the path width and was limited by the square root of the viewable forward distance. While a baseline model showed an adjusted R2 = 0.144 for predicting the speed, our best-fit model showed an adjusted R2 = 0.975 with only one additional coefficient, demonstrating a comparatively high prediction accuracy for given viewable forward distances.

Aiming for the creation and development of taste media, a taste display was developed in this study that can reproduce tastes measured using taste sensors. By performing iontophoresis on five gels, which contain dissolved electrolytes that reproduce the five basic tastes, the quantity of ions that contact the tongue was controlled. A tasteless gel was added, so that the sum of the currents flowing in the six gels could be kept constant, ensuring a uniform amount of stimulation on the tongue. The measured tastes could be successfully reproduced through calibration, in which the indicated taste levels were matched with the taste-sensor measurements. Furthermore, video-editing software was adapted to edit taste information as well as recorded audio and video. In addition, effector and equalizer prototypes were built that can not only reproduce the recorded tastes in their original states but also adjust the tastes to match individual preferences.

We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D moving target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints form a bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls inside the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R^2>0.92 and MAE<4.9% for steering and R^2>0.95 and MAE<6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

When users operate smartphones and desktop interfaces with their fingers, there are differences between the motor and visual widths. For example, when a user selects an item from a vertical menu, the area that is physically touched by the user is often larger than the visual width (e.g., of the label for the item selected). Therefore, the user aims for the label assuming that the label width (the visual width) means the motor width. Consequently, the user performs operations more carefully than necessary. We conducted an experiment to investigate the effect of the motor and visual widths on finger pointing. After asking participants to explore the motor width, they performed an experimental task. Our experiment shows that the users' movement time depends on the motor width and can be predicted. We also analyze existing interfaces and discuss the implications.

In graphical user interfaces, users can select multiple objects simultaneously via lasso selection. This can be implemented, for example, by having users select objects by looping around their centers or entire areas. Based on differences in lassoing criteria, we presume that the performance of the criteria also differs. In this study, we compare three lassoing criteria and model lassoing motions while considering these criteria. We conducted two experiments; participants steered through straight-line and one-loop paths by using three criteria. The participants handled the lassoing criteria correctly and performed lassoing at appropriate speeds for each path shape. Although the drawn trajectories varied depending on the lassoing criteria, the criteria in the performance and subjective evaluations did not differ significantly. Additionally, from our results, we build a baseline model to predict the movement time by considering the lassoing criteria. We also discuss further experiments to predict movement time under more complex conditions.

For pen-steering tasks, the steering law can predict the movement time for both straight and circular paths. It has been shown, however, that those path shapes require different regression expression coefficients. Because it has also been shown that the path curvature (i.e., the inverse of the curvature radius) linearly decreases the movement speed, we refined the steering law to predict the movement time under various curvature radii; our pen-steering experiment included linear to circular shapes. The result showed that the proposed model had good fit for the empirical data: adjusted r2 > 0.95, with the smallest Akaike information criterion (AIC) value among various candidate models. We also discuss how this refined model can contribute to other fields such as predicting driving difficulties.

To achieve better touch GUI designs, researchers have studied optimal target margins, but state-of-the-art results on this topic have been observed for only middle-size touchscreens. In this study, to better determine more practical target arrangements for smartphone GUIs, we conducted four experiments to test the effects of gaps among targets on touch pointing performance. Two lab-based and two crowd-based experiments showed that wider gaps tend to help users reduce the task completion time and the rate of accidental taps on unintended items. For 1D and 2D tasks, the results of the lab-based study with a one-handed thumb operation style showed that 4-mm gaps were the smallest sizes to remove negative effects of surrounding items. The results of the crowd-based study, however, indicated no specific gaps to completely remove the negative effects.

Humans can perceive tactile sensation through multimodal stimuli. To demonstrate realistic pseudo tactile sensation for the users, a tactile display is needed that can provide multiple tactile stimuli. In this paper, we have explicated a novel printed tactile display that can provide both the electrical stimulus and the electrostatic force. The circuit patterns for each stimulus were fabricated by employing the technique of double-sided conductive ink printing. Requirements for the fabrication process were analyzed and the durability of the tactile display was evaluated. Users' perceptions of a single tactile stimulus and multiple tactile stimuli were also investigated. The obtained experimental results indicate that the proposed tactile display is capable of exhibiting realistic tactile sensation and can be incorporated by various applications such as tactile sensation printing of pictorial illustrations and paintings. Furthermore, the proposed hybrid tactile display can contribute to accelerated prototyping and development of new tactile devices.

We investigate stroking motions through successive objects with styli. There are several promising models for stroking motions, such as crossing tasks, which require endpoint accuracy of a stroke, or steering tasks, which require continuous accuracy throughout the trajectory. However, a task requiring users to repeatedly steer through constrained path segments has never been studied, although such operations are needed in GUIs, e.g., for selecting icons or objects on illustration software through lassoing. We empirically confirmed that the interval, trajectory width, and obstacle size significantly affect the movement speed. Existing models can not accurately predict user performance in such tasks. We found several unexpected results such as that steering through denser objects sometimes required less times than expected. Speed profile analysis showed the reasons behind such behaviors, such as participants' anticipation strategies. We also discuss the applicability of exiting performance models and revisions.

Fused deposition modeling (FDM) 3D printers form objects by stacking layers having a linear structure. To print fine structures, an appropriate choice of parameters is necessary, or printing error occurs. On the other hand, the printing error is exploited as an expression technique. However, the relation between the printed structure and the parameters causing the printing error is unclear. In this paper, we focus on the height position of the extruder and the amount of extruded material, and explore the combination of these parameters to enhance the capability of FDM. By extending an equation that calculates the amount of material from the layer height, we investigate the behavior and structure of material extruded from various height positions. On the basis of experimental results, the printed structure is classified into six categories according to the structural feature. We describe these structural features and demonstrate examples with new inherent expressions for FDM.

The steering law models human motor performance and has been verified to hold for a single linear and/or circular path. Some extensions investigated steering around corners. Yet, little is known about human performance in navigating joined linear paths, i.e., successions of path segments with different widths. Such operations appear in graphical user interface tasks, including lasso operations in illustration software. In this work, we conducted several experiments involving joined paths. The results show that users significantly changed their behavior, and that this strategy change can be predicted beforehand. A simple model summing the two indexes of difficulty (IDs) for each path predicts movement time well, but more sophisticated models were also evaluated. The best model in terms of both of R2 and AIC values includes the ID of the crossing operation to enter the second path.

The performance of trajectory-based tasks is modeled by the steering law, which predicts the required time from the index of difficulty (ID). This paper focuses on the fact that the time required to pass through a straight path with linearly-varying width alters depending on the direction of the movement. In this study, an expression for the relationship between the ID of narrowing and widening paths has been developed. This expression can be used to predict the movement time needed to pass through in the opposite direction from only a few data points, after measuring the time needed in the other direction. In the experiment, the times for five IDs were predicted with high precision from the measured time for one ID, thereby illustrating the effectiveness of the proposed method.

In this study, we propose a striped pattern sticker called ExtensionSticker that allows a touch input to be transferred from an external source by simply attaching the sticker to a touch panel. This allows the user to input touches or continuous scrolling actions by touching a sticker printed with stripes of conductive ink, without directly touching the touch panel. This method could be applied to the sides or back of a touch panel, or even the surface upon which a device is located as a touch interface, allowing us to freely construct interfaces in shapes matching the demands of the user. This paper also reports the results of evaluation experiments conducted to assess the recognition accuracy of scroll and tap actions using the proposed method.

We propose Vibkinesis, a smartphone that can control its angle and directions of movement and rotation. By separately controlling the vibration motors attached to it, the smartphone can move on a table in the direction it chooses. Vibkinesis can inform a user of a message received when the user is away from the smartphone by changing its orientation, e.g., the smartphone has rotated 90° to the left before the user returns to the smartphone. With this capability, Vibkinesis can notify the user of a message even if the battery is discharged. We also extend the sensing area of Vibkinesis by using an omni-directional lens so that the smartphone tracks the surrounding objects. This allows Vibkinesis to tap the user's hand. These novel interactions expand the mobile device's movement area, notification channels, and notification time span.

Electric taste is a characteristic taste produced when the tongue is electrically stimulated. We have proposed apparatuses to add electric taste to food and drink. An interactive system could be developed to synchronize video contents using the reversibility and instantaneity of electric taste. However, to do so, the presentation time must be determined based on the different latency for the perception of each sense. We measured the latencies for electric taste and visual stimuli as a basic evaluation for a content presentation system in which electric taste and visual content are synchronized.