An Intermittent Click Planning ModelPointing is the task of tracking a target with a pointer and confirming the target selection through a click action when the pointer is positioned within the target. Little is known about the mechanism by which users plan and execute the click action in the middle of the target tracking process. The Intermittent Click Planning model proposed in this study describes the process by which users plan and execute optimal click actions, from which the model predicts the pointing error rates. In two studies in which users pointed to a stationary target and a moving target, the model proved to accurately predict the pointing error rates (R2 = 0.992 and 0.985, respectively). The model has also successfully identified differences in cognitive characteristics among first-person shooter game players.2020EPEunji Park et al.Korea Advanced Institute of Science and TechnologyGame UX & Player BehaviorGamification DesignChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
Improving Reliability of Virtual Collision Responses: A Cue Integration TechniqueIn virtual reality (VR), a user's virtual avatar can interact with a virtual object by colliding with it. If collision responses do not occur in the direction that the user expects, the user experiences degradation of accuracy and precision in applications such as VR sports games. In determining the response of a virtual collision, existing physics engines have not considered the direction in which the user perceived and estimated the collision. Based on the cue integration theory, this study presents a statistical model explaining how users estimate the direction of a virtual collision from their body's orientation and velocity vectors. The accuracy and precision of virtual collisions can be improved by 8.77% and 30.29%, respectively, by setting the virtual collision response in the direction that users perceive.2020SDSeungwon Do et al.Korea Advanced Institute of Science and TechnologyFull-Body Interaction & Embodied InputImmersion & Presence ResearchCHI
AutoGain: Gain Function Adaptation with Submovement Efficiency OptimizationA 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.2020BLByungjoo Lee et al.Korea Advanced Institute of Science and Technology & Aalto UniversityHand Gesture RecognitionComputational Methods in HCICHI
Button Simulation and Design via FDVV ModelsDesigning 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.2020YLYi-Chi Liao et al.Aalto UniversityForce Feedback & Pseudo-Haptic WeightPrototyping & User TestingCHI
Optimal Sensor Position for a Computer MouseComputer 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.2020SKSunjun Kim et al.Aalto University & Daegu Gyeongbuk Institute of Science and TechnologyContext-Aware ComputingPrototyping & User TestingCHI
Aero-plane: a Handheld Force-Feedback Device that Renders Weight Motion Illusion on a Virtual 2D PlaneForce feedback is said to be the next frontier in virtual reality (VR). Recently, with consumers pushing forward with unthethered VR, researchers turned away from solutions based on bulky hardware (e.g., exoskeletons and robotic arms) and started exploring smaller portable or wearable devices. However, when it comes to rendering inertial forces, such as when moving a heavy object around or when interacting with objects with unique mass properties, current ungrounded force feedback devices are unable to provide quick weight shifting sensations that can realistically simulate weight changes over 2D surfaces. In this paper we introduce Aero-plane, a force-feedback handheld controller based on two miniature jet-propellers that can render shifting weights of up to 14 N within 0.3 seconds. Through two user studies we: (1) characterize the users’ ability to perceive and correctly recognize different motion paths on a virtual plane while using our device; and, (2) tested the level of realism and immersion of the controller when used in two VR applications (a rolling ball on a plane, and using kitchen tools of different shape and size). Lastly, we present a set of applications that further explore different usage cases and alternative form-factors for our device.2019SJSeungwoo Je et al.Force Feedback & Pseudo-Haptic WeightUIST
Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection GamesEffects 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.2019ILInjung Lee et al.Korea Advanced Institute of Science and TechnologyGame UX & Player BehaviorSerious & Functional GamesCHI
Impact Activation Improves Rapid Button PressingThe 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.2018SKSunjun Kim et al.Aalto University, KAISTForce Feedback & Pseudo-Haptic WeightCHI
FDSense: Estimating Young's Modulus and Stiffness of End Effectors to Facilitate Kinetic Interaction on Touch SurfacesWe make touch input by physically colliding an end effector (e.g., a body part or a stylus) with a touch surface. Prior studies have examined the use of kinematic variables of collision between objects, such as position, velocity, force, and impact. However, the nature of the collision can be understood more thoroughly by considering the known physical relationships that exist between directly measurable variables (i.e., kinetics). Based on this collision kinetics, this study proposes a novel touch technique called FDSense. By simultaneously observing the force and contact area measured from the touchpad, FDSense allows estimation of the Young’s modulus and stiffness of the object being contacted. Our technical evaluation showed that FDSense could effectively estimate the Young’s modulus of end effectors made of various materials, and the stiffness of each part of the human hand. Two applications using FDSense were demonstrated, for digital painting and digital instruments, where the result of the expression varies significantly depending on the elasticity of the end effector. In a following informal study, participants assessed the technique positively.2018SHSanghwa Hong et al.Vibrotactile Feedback & Skin StimulationForce Feedback & Pseudo-Haptic WeightUIST
Moving Target Selection: A Cue Integration ModelThis 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.2018BLByungjoo Lee et al.KAISTGame UX & Player BehaviorGamification DesignCHI
Neuromechanics of a Button PressTo 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.2018AOAntti Oulasvirta et al.Aalto UniversityVibrotactile Feedback & Skin StimulationForce Feedback & Pseudo-Haptic WeightCHI