Modeling User Performance in Multi-Lane Moving-Target AcquisitionModern video games often feature moving target acquisition (MTA) tasks, where users must press a button when a moving target reaches an acquisition line. User performance models in MTA are useful for quantitative skill analysis and computational game level design, but have so far been constructed only for cases where there is a single lane for a target to appear and follow. In this study, the first user performance model is presented and validated for an MTA task with multiple lanes. The model is built as an integration of the existing MTA model and the drift-diffusion model, a model of human decision-making process under time-pressure. In a user study, we showed that the model can fit lane recognition error rates and input timing distributions with significantly higher coefficients of determination ($R^2$) and accuracy than a baseline model.2025JKJonghyun Kim et al.Yonsei UniversityVisualization Perception & CognitionSerious & Functional GamesCHI
Hardware-Embedded Pointing Transfer Function Capable of Canceling OS GainsWhen using indirect pointing devices in modern operating systems (OS), users' perception of the pointing transfer function is easily influenced by the device's hardware or OS-native transfer function settings. This could hinder users from finding and fully adapting to the transfer function that is optimal for them. We propose a novel hardware-embedded transfer function technique that is expected to allow users to consistently experience the desired function even when device hardware or OS settings change. The technique (1) allows users to define the desired function within the device firmware in physical units and (2) enables the firmware to cancel out the influence of OS-native functions and hardware setting perturbations, so that the uploaded function can persist regardless of the external environment. Through technical evaluation including transfer functions of various shapes, we showed that the proposed technique has comparable robustness and accuracy to the conventional approach.2025SKSeonho Kim et al.Yonsei University, Computer Science/College of Computing/Yonsei Esports LabForce Feedback & Pseudo-Haptic WeightCircuit Making & Hardware PrototypingCHI
DataSentry: Building Missing Data Management System for In-the-Wild Mobile Sensor Data Collection through Multi-Year Iterative Design ApproachMobile sensor data collection in people’s daily lives is essential for understanding fine-grained human behaviors. However, in-the-wild data collection often results in missing data due to participant and system-related issues. While existing monitoring systems in the mobile sensing field provide an opportunity to detect missing data, they fall short in monitoring data across many participants and sensors and diagnosing the root causes of missing data, accounting for heterogeneous sensing characteristics of mobile sensor data. To address these limitations, we undertook a multi-year iterative design process to develop a system for monitoring missing data in mobile sensor data collection. Our final prototype, DataSentry, enables the detection, diagnosis, and addressing of missing data issues across many participants and sensors, considering both within- and between-person variability. Based on the iterative design process, we share our experiences, lessons learned, and design implications for developing advanced missing data management systems.2025YJYugyeong Jung et al.KAIST, School of ComputingUbiquitous ComputingField StudiesComputational Methods in HCICHI
Crafting Champions: An Observation Study of Esports Coaching ProcessesAs esports grows into a multi-million dollar industry of professional players and competitions, so too grows the interest in and need for professional coaching. Accordingly, there are increased demands and attempts to support and improve coaching for esports. A more comprehensive, granular understanding of the esports coaching process would provide a valuable foundation to inform opportunities to advance the domain via HCI theories and practices. However, in-depth studies of coaching practice, from the lens of HCI, are far less common in existing literature. In this paper, we take the first steps to provide such a foundation through an observation study conducted at an elite, award-winning League of Legends training academy. By analyzing 112 hours of dialogue and footage from coaching sessions, we identify pertinent activities and events that occur within the coaching process, which enable us to consider how esports coaching can be improved via theory, practice, and technology from HCI.2025HLHanbyeol Lee et al.Yonsei UniversityBrain-Computer Interface (BCI) & NeurofeedbackSerious & Functional GamesCHI
Characterizing and Quantifying Expert Input Behavior in League of LegendsTo achieve high performance in esports, players must be able to effectively and efficiently control input devices such as a computer mouse and keyboard (i.e., input skills). Characterizing and quantifying a player’s input skills can provide useful insights, but collecting and analyzing sufficient amounts of data in ecologically valid settings remains a challenge. Targeting the popular esports game, League of Legends, we go beyond the limitations of previous studies and demonstrate a holistic pipeline of input behavior analysis: from quantifying the quality of players’ input behavior (i.e., input skill) to training players based on the analysis. Based on interviews with five top-tier professionals and analysis of input behavior logs from 4,835 matches played freely at home collected from 193 players (including 18 professionals), we confirmed that players with higher ranks in the game implement eight different input skills with higher quality. In a three-week follow-up study using a training aid that visualizes a player’s input skill levels, we found that the analysis provided players with actionable lessons, potentially leading to meaningful changes in their input behavior.2024HLHanbyeol Lee et al.Yonsei UniversityGame UX & Player BehaviorSerious & Functional GamesRole-Playing & Narrative GamesCHI
User Performance in Consecutive Temporal Pointing: An Exploratory StudyA significant amount of research has recently been conducted on user performance in so-called temporal pointing tasks, in which a user is required to perform a button input at the timing required by the system. Consecutive temporal pointing (CTP), in which two consecutive button inputs must be performed while satisfying temporal constraints, is common in modern interactions, yet little is understood about user performance on the task. Through a user study involving 100 participants, we broadly explore user performance in a variety of CTP scenarios. The key finding is that CTP is a unique task that cannot be considered as two ordinary temporal pointing processes. Significant effects of button input method, motor limitations, and different hand use were also observed.2024DLDawon Lee et al.KAISTNotification & Interruption ManagementUser Research Methods (Interviews, Surveys, Observation)CHI
Quantifying Wrist-Aiming Habits with A Dual-Sensor Mouse: Implications for Player Performance and WorkloadComputer mice are widely used today as the primary input device in competitive video games. If a player exhibits more wrist rotation than other players when moving the mouse laterally, the player is said to have stronger wrist-aiming habits. Despite strong public interest, there has been no affordable technique to quantify the extent of a player's wrist-aiming habits and no scientific investigation into how the habits affect player performance and workload. We present a reliable and affordable technique to quantify the extent of a player's wrist-aiming habits using a mouse equipped with two optical sensors (i.e., a dual-sensor mouse). In two user studies, we demonstrate the reliability of the technique and examine the relationship between wrist-aiming habits and player performance or workload. In summary, player expertise and mouse sensitivity significantly impacted wrist-aiming habits; the extent of wrist-aiming showed a positive correlation with upper limb workload.2024DKDonghyeon Kang et al.YONSEI UniversityGame UX & Player BehaviorCHI
Amortized Inference with User SimulationsThere 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.2023HMHee-Seung Moon et al.Yonsei University, Aalto UniversityChronic Disease Self-Management (Diabetes, Hypertension, etc.)Knowledge Worker Tools & WorkflowsCHI
How AI-Based Training Affected Performance of Professional Go PlayersIn this study, we analyzed how the performance of professional Go players has changed since the advent of AlphaGo, the first artificial intelligence (AI) application to defeat a human world Go champion. We interviewed and surveyed professional Go players and found that AI has been actively introduced into the Go training process since the advent of AlphaGo. The significant impact of AI-based training was confirmed in a subsequent analysis of 6,292 games in Korean Go tournaments and Elo rating data of 1,362 Go players worldwide. Overall, the tendency of players to make moves similar to those recommended by AI has sharply increased since 2017. The degree to which players’ expected win rates fluctuate during a game has also decreased significantly since 2017. We also found that AI-based training has provided more benefits to senior players and allowed them to achieve Elo ratings higher than those of junior players.2022JKJimoon Kang et al.Yonsei UniversityGenerative AI (Text, Image, Music, Video)Explainable AI (XAI)Mental Health Apps & Online Support CommunitiesCHI
Speeding up Inference with User Simulators through Policy ModulationThe simulation of user behavior with deep reinforcement learning agents has shown some recent success. However, the inverse problem, that is, inferring the free parameters of the simulator from observed user behaviors, remains challenging to solve. This is because the optimization of the new action policy of the simulated agent, which is required whenever the model parameters change, is computationally impractical. In this study, we introduce a network modulation technique that can obtain a generalized policy that immediately adapts to the given model parameters. Further, we demonstrate that the proposed technique improves the efficiency of user simulator-based inference by eliminating the need to obtain an action policy for novel model parameters. We validated our approach using the latest user simulator for point-and-click behavior. Consequently, we succeeded in inferring the user’s cognitive parameters and intrinsic reward settings with less than 1/1000 computational power to those of existing methods.2022HMHee-Seung Moon et al.Yonsei UniversityHuman-LLM CollaborationCHI
Quantifying Proactive and Reactive Button InputWhen giving input with a button, users follow one of two strategies: (1) react to the output from the computer or (2) proactively act in anticipation of the output from the computer. We propose a technique to quantify reactiveness and proactiveness to determine the degree and characteristics of each input strategy. The technique proposed in this study uses only screen recordings and does not require instrumentation beyond the input logs. The likelihood distribution of the time interval between the button inputs and system outputs, which is uniquely determined for each input strategy, is modeled. Then the probability that each observed input/output pair originates from a specific strategy is estimated along with the parameters of the corresponding likelihood distribution. In two empirical studies, we show how to use the technique to answer questions such as how to design animated transitions and how to predict a player's score in real-time games.2022HKHyunchul Kim et al.KAISTPrototyping & User TestingCHI
A Simulation Model of Intermittently Controlled Point-and-Click BehaviourWe present a novel simulation model of point-and-click behaviour that is applicable both when a target is stationary or moving. To enable more realistic simulation than existing models, the model proposed in this study takes into account key features of the user and the external environment, such as intermittent motor control, click decision-making, visual perception, upper limb kinematics and the effect of input device. The simulated user's point-and-click behaviour is formulated as a Markov decision process (MDP), and the user's policy of action is optimised through deep reinforcement learning. As a result, our model successfully and accurately reproduced the trial completion time, distribution of click endpoints, and cursor trajectories of real users. Through an ablation study, we showed how the simulation results change when the model's sub-modules are individually removed. The implemented model and dataset are publicly available.2021SDSeungwon Do et al.KAISTEye Tracking & Gaze InteractionHuman Pose & Activity RecognitionComputational Methods in HCICHI
Secrets of Gosu: Understanding Physical Combat Skills of Professional Players in First-Person ShootersIn first-person shooters (FPS), professional players (a.k.a., Gosu) outperform amateur players. The secrets behind the performance of professional FPS players have been debated in online communities with many conjectures; however, attempts of scientific verification have been limited. We addressed this conundrum through a data-collection study of the gameplay of eight professional and eight amateur players in the commercial FPS Counter-Strike: Global Offensive. The collected data cover behavioral data from six sensors (motion capture, eye tracker, mouse, keyboard, electromyography armband, and pulse sensor) and in-game data (player data and event logs). We examined conjectures in four categories: aiming, character movement, physicality, and device and settings. Only 6 out of 13 conjectures were supported with statistically sufficient evidence.2021EPEunji Park et al.KAISTGame UX & Player BehaviorCHI