Browsing by Author "Lee, Byungjoo"

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    Amortized Inference with User Simulations
    (2023-04-19) Moon, Hee Seung; Oulasvirta, Antti; Lee, Byungjoo; Department of Information and Communications Engineering; User Interfaces; Helsinki Institute for Information Technology (HIIT)
    There have been significant advances in simulation models predicting human behavior across various interactive tasks. One issue remains, however: identifying the parameter values that best describe an individual user. These parameters often express personal cognitive and physiological characteristics, and inferring their exact values has significant effects on individual-level predictions. Still, the high complexity of simulation models usually causes parameter inference to consume prohibitively large amounts of time, as much as days per user. We investigated amortized inference for its potential to reduce inference time dramatically, to mere tens of milliseconds. Its principle is to pre-train a neural proxy model for probabilistic inference, using synthetic data simulated from a range of parameter combinations. From examining the efficiency and prediction performance of amortized inference in three challenging cases that involve real-world data (menu search, point-and-click, and touchscreen typing), the paper demonstrates that an amortized-inference approach permits analyzing large-scale datasets by means of simulation models. It also addresses emerging opportunities and challenges in applying amortized inference in HCI.
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    AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization
    (2020-04-21) Lee, Byungjoo; Nancel, Mathieu; Kim, Sunjun; Oulasvirta, Antti; Department of Communications and Networking; User Interfaces; Helsinki Institute for Information Technology (HIIT)
    A well-designed control-to-display gain function can improve pointing performance with indirect pointing devices like trackpads. However, the design of gain functions is challenging and mostly based on trial and error. AutoGain is a novel method to individualize a gain function for indirect pointing devices in contexts where cursor trajectories can be tracked. It gradually improves pointing efficiency by using a novel submovement-level tracking+optimization technique that minimizes aiming error (undershooting/overshooting) for each submovement. We first show that AutoGain can produce, from scratch, gain functions with performance comparable to commercial designs, in less than a half-hour of active use. Second, we demonstrate AutoGain’s applicability to emerging input devices (here, a Leap Motion controller) with no reference gain functions. Third, a one-month longitudinal study of normal computer use with AutoGain showed performance improvements from participants’ default functions.
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    Boxer
    (2017-11-03) Lee, Byungjoo; Deng, Qiao; Hoggan, Eve; Oulasvirta, Antti; Department of Communications and Networking; School common, SCI
    Virtual collision techniques are interaction techniques for invoking discrete events in a virtual scene, e.g. throwing, pushing, or pulling an object with a pointer. The conventional approach involves detecting collisions as soon as the pointer makes contact with the object. Furthermore, in general, motor patterns can only be adjusted based on visual feedback. The paper presents a multimodal technique based on the principle that collisions should be aligned with the most salient sensory feedback. Boxer (1) triggers a collision at the moment where the pointer's speed reaches a minimum after first contact and (2) is synchronized with vibrotactile stimuli presented to the hand controlling the pointer. Boxer was compared with the conventional technique in two user studies (with temporal pointing and virtual batting). Boxer improved spatial precision in collisions by 26.7% while accuracy was compromised under some task conditions. No difference was found in temporal precision. Possibilities for improving virtual collision techniques are discussed.
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    Button Simulation and Design via FDVV Models
    (2020-04) Liao, Yi-Chi; Kim, Sunjun; Lee, Byungjoo; Oulasvirta, Antti; Department of Communications and Networking; User Interfaces; Helsinki Institute for Information Technology (HIIT); Korea Advanced Institute of Science and Technology
    Designing a push-button with desired sensation and performance is challenging because the mechanical construction must have the right response characteristics. Physical simulation of a button’s force-displacement (FD) response has been studied to facilitate prototyping; however, the simulations’ scope and realism have been limited. In this paper, we extend FD modeling to include vibration (V) and velocity-dependence characteristics (V). The resulting FDVV models better capture tactility characteristics of buttons, including snap. They increase the range of simulated buttons and the perceived realism relative to FD models. The paper also demonstrates methods for obtaining these models, editing them, and simulating accordingly. This end-to-end approach enables the analysis, prototyping, and optimization of buttons, and supports exploring designs that would be hard to implement mechanically.
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    Geometrically compensating effect of end-to-end latency in moving-target selection games
    (2019-05-02) Lee, Injung; Kim, Sunjun; Lee, Byungjoo; Department of Communications and Networking; User Interfaces; Korea Advanced Institute of Science and Technology
    Effects of unintended latency on gamer performance have been reported. End-to-end latency can be corrected by post-input manipulation of activation times, but this gives the player unnatural gameplay experience. For moving-target selection games such as Flappy Bird, the paper presents a predictive model of latency on error rate and a novel compensation method for the latency effects by adjusting the game’s geometry design – e.g., by modifying the size of the selection region. Without manipulation of the game clock, this can keep the user’s error rate constant even if the end-to-end latency of the system changes. The approach extends the current model of moving-target selection with two additional assumptions about the effects of latency: (1) latency reduces players’ cue-viewing time and (2) pushes the mean of the input distribution backward. The model and method proposed have been validated through precise experiments.
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    Impact Activation Improves Rapid Button Pressing
    (ACM, 2018) Kim, Sunjun; Lee, Byungjoo; Oulasvirta, Antti; Department of Communications and Networking; Korea Advanced Institute of Science and Technology
    The activation point of a button is defined as the depth at which it invokes a make signal. Regular buttons are activated during the downward stroke, which occurs within the first 20 ms of a press. The remaining portion, which can be as long as 80 ms, has not been examined for button activation for reason of mechanical limitations. The paper presents a technique and empirical evidence for an activation technique called Impact Activation, where the button is activated at its maximal impact point. We argue that this technique is advantageous particularly in rapid, repetitive button pressing, which is common in gaming and music applications. We report on a study of rapid button pressing, wherein users’ timing accuracy improved significantly with use of Impact Activation. The technique can be implemented for modern push-buttons and capacitive sensors that generate a continuous signal.
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    Modelling error rates in temporal pointing
    (2016) Lee, Byungjoo; Oulasvirta, Antti; Department of Communications and Networking; Helsinki Institute for Information Technology (HIIT); User Interfaces
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    Moving Target Selection: A Cue Integration Model
    (ACM SIGCHI, 2018) Lee, Byungjoo; Kim, Sunjun; Oulasvirta, Antti; Lee, Jong-In; Park, Eunji; Department of Communications and Networking; Helsinki Institute for Information Technology (HIIT); User Interfaces; Korea Advanced Institute of Science and Technology
    This paper investigates a common task requiring temporal precision: the selection of a rapidly moving target on display by invoking an input event when it is within some selection window. Previous work has explored the relationship between accuracy and precision in this task, but the role of visual cues available to users has remained unexplained. To expand modeling of timing performance to multimodal settings, common in gaming and music, our model builds on the principle of probabilistic cue integration. Maximum likelihood estimation (MLE) is used to model how different types of cues are integrated into a reliable estimate of the temporal task. The model deals with temporal structure (repetition, rhythm) and the perceivable movement of the target on display. It accurately predicts error rate in a range of realistic tasks. Applications include the optimization of difficulty in game-level design.
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    Neuromechanics of a Button Press
    (ACM, 2018) Oulasvirta, Antti; Kim, Sunjun; Lee, Byungjoo; Department of Communications and Networking; Helsinki Institute for Information Technology (HIIT); User Interfaces; Korea Advanced Institute of Science and Technology
    To press a button, a finger must push down and pull up with the right force and timing. How the motor system succeeds in button-pressing, in spite of neural noise and lacking direct access to the mechanism of the button, is poorly understood. This paper investigates a unifying account based on neuromechanics. Mechanics is used to model muscles controlling the finger that contacts the button. Neurocognitive principles are used to model how the motor system learns appropriate muscle activations over repeated strokes though relying on degraded sensory feedback. Neuromechanical simulations yield a rich set of predictions for kinematics, dynamics, and user performance and may aid in understanding and improving input devices. We present a computational implementation and evaluate predictions for common button types.
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    Optimal Sensor Position for a Computer Mouse
    (2020-04-21) Kim, Sunjun; Lee, Byungjoo; van Gemert, Thomas; Oulasvirta, Antti; Department of Communications and Networking; Department of Computer Science; User Interfaces; Helsinki Institute for Information Technology (HIIT); Department of Computer Science; Korea Advanced Institute of Science and Technology
    Computer mice have their displacement sensors in various locations (center, front, and rear). However, there has been little research into the effects of sensor position or on engineering approaches to exploit it. This paper first discusses the mechanisms via which sensor position affects mouse movement and reports the results from a study of a pointing task in which the sensor position was systematically varied. Placing the sensor in the center turned out to be the best compromise: improvements over front and rear were in the 11–14% range for throughput and 20–23% for path deviation. However, users varied in their personal optima. Accordingly, variable-sensor position mice are then presented, with a demonstration that high accuracy can be achieved with two static optical sensors. A virtual sensor model is described that allows software-side repositioning of the sensor. Individual-specific calibration should yield an added 4% improvement in throughput over the default center position.
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    Real-time 3D Target Inference via Biomechanical Simulation
    (2024-05-11) Moon, Hee-Seung; Liao, Yi-Chi; Li, Chenyu; Lee, Byungjoo; Oulasvirta, Antti; Department of Information and Communications Engineering; Department of Computer Science; Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Toups Dugas, Phoebe; Shklovski, Irina; User Interfaces; Helsinki Institute for Information Technology (HIIT)
    Selecting a target in a 3D environment is often challenging, especially with small/distant targets or when sensor noise is high. To facilitate selection, target-inference methods must be accurate, fast, and account for noise and motor variability. However, traditional data-free approaches fall short in accuracy since they ignore variability. While data-driven solutions achieve higher accuracy, they rely on extensive human datasets so prove costly, time-consuming, and transfer poorly. In this paper, we propose a novel approach that leverages biomechanical simulation to produce synthetic motion data, capturing a variety of movement-related factors, such as limb configurations and motor noise. Then, an inference model is trained with only the simulated data. Our simulation-based approach improves transfer and lowers cost; variety-rich data can be produced in large quantities for different scenarios. We empirically demonstrate that our method matches the accuracy of human-data-driven approaches using data from seven users. When deployed, the method accurately infers intended targets in challenging 3D pointing conditions within 5–10 milliseconds, reducing users’ target-selection error by 71% and completion time by 35%.
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    Spotlights
    (2016) Lee, Byungjoo; Savisaari, Olli; Oulasvirta, Antti; Department of Communications and Networking