Behavioral Differences between Tap and Swipe: Observations on Time, Error, Touch-point Distribution, and Trajectory for Tap-and-swipe Enabled TargetsExisting guidelines for designing targets on smartphones often focus on single-tap operations for accurate selection. However, smartphone interfaces can support both tap and swipe actions. We explored user-performance differences between tap and swipe in two crowdsourced experiments using bar and square targets. Results indicated longer operation times, higher error rates, and significantly shifted touch points for swipe compared to tap. Our findings imply that current target-size guidelines may not apply to swipe-operated targets, and they reveal new research opportunities for swipeable-target designs.2024SYShota Yamanaka et al.Yahoo Japan CorporationUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
The Effect of Latency on Movement Time in Path-steeringIn current graphical user interfaces, there exists a (typically unavoidable) end-to-end latency from each pointing-device movement to its corresponding cursor response on the screen, which is known to affect user performance in target selection, e.g., in terms of movement time (MT). Previous work also reported that a long latency increases MTs in path-steering tasks, but the quantitative relationship between latency and MT had not been previously investigated for path-steering. In this work, we derive models to predict MTs for path-steering and evaluate them with five tasks: goal crossing as a preliminary task for model derivation, linear-path steering, circular-path steering, narrowing-path steering, and steering with target pointing. The results show that the proposed models yielded an adjusted R^2 > 0.94, with lower AICs and smaller cross-validation RMSEs than the baseline models, enabling more accurate prediction of MTs.2024SYShota Yamanaka et al.Yahoo Japan CorporationUser Research Methods (Interviews, Surveys, Observation)Computational Methods in HCICHI
Better Definition and Calculation of Throughput and Effective Parameters for Steering to Account for Subjective Speed-accuracy TradeoffsIn 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.2024NKNobuhito Kasahara et al.Meiji UniversityUser Research Methods (Interviews, Surveys, Observation)CHI
Single-tap Latency Reduction with Single- or Double- tap PredictionTouch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the {\it single-tap latency problem}, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce {\it the single-tap latency}, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the {\it single-tap latency} from 150--500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.2023NNNaoto Nishida et al.Head-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)Explainable AI (XAI)MobileHCI
Tuning Endpoint-variability Parameters by Observed Error Rates to Obtain Better Prediction Accuracy of Pointing MissesError rates (ERs) in target-pointing tasks are typically modelled in two steps: predicting the click-point variability (sigma) based on target sizes and then computing the probability that a click falls outside a target. This is an indirect approach if the researcher's purpose is to achieve the accurate prediction of ERs because the model coefficients are optimized to predict sigma accurately in the first step. We compared the prediction accuracies of this method with a more direct technique in which the coefficients used for sigma are determined in such a way as to optimize the closeness between observed and predicted ERs. Our re-analysis of eight datasets from mouse- and touch-based pointing studies showed that the latter approach consistently outperforms the conventional one if the starting values for the parameter search are appropriate (which can be achieved by hyperparameter optimization), thus enabling the interface configuration on the basis of accurately predicted ERs.2023SYShota Yamanaka et al.Yahoo Japan CorporationUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
Varying Subjective Speed-accuracy Biases to Evaluate the Generalizability of Experimental Findings on Pointing-facilitation TechniquesIn 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.2023SYShota Yamanaka et al.Yahoo Japan CorporationVoice User Interface (VUI) DesignComputational Methods in HCICHI
Bivariate Effective Width Method to Improve the Normalization Capability for Subjective Speed-accuracy Biases in Rectangular-target PointingThe 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.2022SYShota Yamanaka et al.Yahoo Japan CorporationPrototyping & User TestingComputational Methods in HCICHI
Servo-Gaussian Model to Predict Success Rates in Manual Tracking: Path Steering and Pursuit of 1D Moving TargetWe 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.2020SYShota Yamanaka et al.Human Pose & Activity RecognitionComputational Methods in HCIUIST
Modeling Fully and Partially Constrained Lasso Movements in a Grid of IconsLassoing objects is a basic function in illustration software and presentation tools. Yet, for many common object arrangements lassoing is sometimes time-consuming to perform and requires precise pen operation. In this work, we studied lassoing movements in a grid of objects similar to icons. We propose a quantitative model to predict the time to lasso such objects depending on the margins between icons, their sizes, and layout, which all affect the number of stopping and crossing movements. Results of two experiments showed that our models predict fully and partially constrained movements with high accuracy. We also analyzed the speed profiles and pen stroke trajectories and identified deeper insights into user behaviors, such as that an unconstrained area can induce higher movement speeds even in preceding path segments.2019SYShota Yamanaka et al.Yahoo Japan CorporationPrototyping & User TestingComputational Methods in HCICHI
Steering Performance with Error-accepting DelaysIn steering law tasks, deviating from the path is immediately considered an error operation. However, in navigating a hierarchical menu item, which is a representative application of the law, a deviation within a short duration is sometimes permitted. We tested the validity of the steering law model with various durations of such error-accepting delays and found that it showed high fits for each delay condition (R^(2) > 0.96) but poor fits if the delay values were not separated (R^(2) = 0.58). Because the average movement speed linearly increased as the delay increased, we refined the model by taking the delay into account, and the fitness was significantly improved (R^(2) = 0.97). Our model will help GUI designers estimate the average operational time on the basis of the menu item length, width, and error-accepting delay.2019SYShota YamanakaYahoo Japan CorporationPrototyping & User TestingComputational Methods in HCICHI
Steering through Successive ObjectsWe 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.2018SYShota Yamanaka et al.Yahoo Japan CorporationPrototyping & User TestingCHI