Hiroki Usuba

schoolLY Corporationbar_chart#2380 Global Rank

Total Papers

HCI Rank

#2380

Research Areas

Years Active

Recent Publications

Verifying Finger-Fitts Models for Normalizing Subjective Speed-Accuracy Biases

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 th…

CHI '242nd AuthorUser Research Methods (Interviews, Surveys, Observation) +1

Behavioral Differences between Tap and Swipe: Observations on Time, Error, Touch-point Distribution, and Trajectory for Tap-and-swipe Enabled Targets

Existing 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. Resul…

CHI '23CorrespondingUser Research Methods (Interviews, Surveys, Observation) +1

Tuning Endpoint-variability Parameters by Observed Error Rates to Obtain Better Prediction Accuracy of Pointing Misses

Error 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…

CHI '222nd AuthorPrototyping & User Testing +1

Bivariate Effective Width Method to Improve the Normalization Capability for Subjective Speed-accuracy Biases in Rectangular-target Pointing

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 wit…

UIST '202nd AuthorHuman Pose & Activity Recognition +1

Servo-Gaussian Model to Predict Success Rates in Manual Tracking: Path Steering and Pursuit of 1D Moving Target

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…

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