PANDA: Parkinson's Assistance and Notification Driving AidParkinson's Disease (PD) significantly impacts driving abilities, often leading to early driving cessation or accidents due to reduced motor control and increasing reaction times. To diminish the impact of these symptoms, we developed PANDA (Parkinson's Assistance and Notification Driving Aid), a multi-modality real-time alert system designed to monitor driving patterns continuously and provide immediate alerts for irregular driving behaviors, enhancing driver safety of individuals with PD. The system was developed through a participatory design process with 9 people with PD and 13 non-PD individuals using a driving simulator, which allowed us to identify critical design characteristics and collect detailed data on driving behavior. A user study involving individuals with PD evaluated the effectiveness of PANDA, exploring optimal strategies for delivering alerts and ensuring they are timely and helpful. Our findings demonstrate that PANDA has the potential to enhance the driving safety of individuals with PD, offering a valuable tool for maintaining independence and confidence behind the wheel.2025TWTianyang Wen et al.Institude of Software, Chinese Academy of SciencesIn-Vehicle Haptic, Audio & Multimodal FeedbackMotor Impairment Assistive Input TechnologiesPrototyping & User TestingCHI
Emotionally Challenging Games Can Satisfy Older Adults' Psychological Needs: From Empirical Study to Design GuidelinesOlder adults often struggle to meet their psychological needs due to retirement and living alone. Recent studies suggest that games featuring emotional challenge (EC) can help fulfill basic psychological needs such as autonomy, competence, and relatedness by facilitating emotional exploration. However, it remains unclear whether older adults can benefit from EC games, whether they find this genre enjoyable, and how these games should be designed to better meet their needs. This work explores older adults’ experiences and perceptions of playing EC games through two studies. The first study involved playing Detroit: Become Human, revealing that older adults derived multifaceted psychological experiences from playing the game. The second study involved a custom-designed game scenario tailored to older adults, demonstrating that meaningful choices significantly influenced autonomy need satisfaction. Based on these findings, we offer five design guidelines for developing EC games that satisfy psychological needs of older adults.2025MZMin Zhou et al.Institute of Software, ChineseAging-Friendly Technology DesignSerious & Functional GamesCHI
TutorCraftEase: Enhancing Pedagogical Question Creation with Large Language ModelsPedagogical questions are crucial for fostering student engagement and learning. In daily teaching, teachers pose hundreds of questions to assess understanding, enhance learning outcomes, and facilitate the transfer of theory-rich content. However, even experienced teachers often struggle to generate a large volume of effective pedagogical questions. To address this, we introduce TutorCraftEase, an interactive generation system that leverages large language models (LLMs) to assist teachers in creating pedagogical questions. TutorCraftEase enables the rapid generation of questions at varying difficulty levels with a single click, while also allowing for manual review and refinement. In a comparative user study with 39 participants, we evaluated TutorCraftEase against a traditional manual authoring tool and a basic LLM tool. The results show that TutorCraftEase can generate pedagogical questions comparable in quality to those created by experienced teachers, while significantly reducing their workload and time.2025WKWenhui Kang et al.University of Chinese Academy of Sciences; Institute of Software, Chinese Academy of Sciences, Beijing Key Laboratory of Human-Computer InteractionHuman-LLM CollaborationOnline Learning & MOOC PlatformsIntelligent Tutoring Systems & Learning AnalyticsCHI
Exploring Experience Gaps Between Active and Passive Users During Multi-user Locomotion in VRMulti-user locomotion in VR has grown increasingly common, posing numerous challenges. A key factor contributing to these challenges is the gaps in experience between active and passive users during co-locomotion. Yet, there remains a limited understanding of how and to what extent these experiential gaps manifest in diverse multi-user co-locomotion scenarios. This paper systematically explores the gaps in physiological and psychological experience indicators between active and passive users across various locomotion situations. Such situations include when active users walk, fly by joystick, or teleport, and passive users stand still or look around. We also assess the impact of factors such as sub-locomotion type, speed/teleport-interval, motion sickness susceptibility, etc. Accordingly, we delineate acceptability disparities between active and passive users, offering insights into leveraging notable experimental findings to mitigate discomfort during co-locomotion through avoidance or intervention.2024TLTianren Luo et al.Institute of Software, College of Computer Science and TechnologySocial & Collaborative VRImmersion & Presence ResearchCHI
MathAssist: A Handwritten Mathematical Expression Autocomplete TechniqueWriting and editing mathematical expressions with complicated structures in computer system is difficult and time-consuming. To address this, we proposed MathAssist, a mathematical expression autocomplete technique that recommends full formulas in real-time based on the user's input strokes. Our technique identifies user's input purpose by matching the structure of the current user input to the structure of formulas in a database. To facilitate such process, we propose a novel tree-based formalization to represent formula. In comparison to a mathematical expression recognition algorithm (SRD) and a commercial MicroSoft Ink Equation (InkEqu), our approach outperformed both of them on task completion time (reduced by 37.14% and 37.58%) and accuracy (32.78% and 10.55% higher). We also discuss our findings in using autocomplete to assist formula editing.2024WKWenhui Kang et al.Generative AI (Text, Image, Music, Video)Programming Education & Computational ThinkingPrototyping & User TestingIUI
Shape-Adaptive Ternary-Gaussian Model: Modeling Pointing Uncertainty for Moving Targets of Arbitrary ShapesThis paper presents a Shape-Adaptive Ternary-Gaussian model for describing endpoint uncertainty when pointing at moving targets of arbitrary shapes. The basic idea of the model is to combine the uncertainty related to the target shape with the uncertainty caused by the target motion. First, we proposed a model to predict endpoint distribution on static targets based on a Dual-Space Decomposition (DUDE) algorithm. Then, we linearly combined a 2D Ternary-Gaussian model with the newly proposed DUDE-based model to make the 2D Ternary-Gaussian model adaptable to moving targets with random shapes. To verify the performance of our model, we compared it with the original 2D Ternary-Gaussian model and a recent proposed Inscribed Circle model in predicting endpoint distribution. The results show that the proposed model outperformed the two baseline models while maintaining good robustness across different shapes and moving speeds.2023HZHao Zhang et al.Chinese Academy of Sciences, School of Computer Science and Technology, University of Chinese Academy of SciencesKnowledge Worker Tools & WorkflowsComputational Methods in HCICHI
ChallengeDetect: Investigating the Potential of Detecting In-Game Challenge Experience from Physiological MeasuresChallenge is the core element of digital games. The wide spectrum of physical, cognitive, and emotional challenge experiences provided by modern digital games can be evaluated subjectively using a questionnaire, the CORGIS, which allows for a post hoc evaluation of the overall experience that occurred during game play. Measuring this experience dynamically and objectively, however, would allow for a more holistic view of the moment-to-moment experiences of players. This study, therefore, explored the potential of detecting perceived challenge from physiological signals. For this, we collected physiological responses from 32 players who engaged in three typical game scenarios. Using perceived challenge ratings from players and extracted physiological features, we applied multiple machine learning methods and metrics to detect challenge experiences. Results show that most methods achieved a detection accuracy of around 80%. We discuss in-game challenge perception, challenge-related physiological indicators and AI-supported challenge detection to inform future work on challenge evaluation.2023XPXiaolan Peng et al.Institute of software,Chinese Academy of SciencesAI-Assisted Decision-Making & AutomationGame UX & Player BehaviorCHI
Distractor Effects on Crossing-Based InteractionTask-irrelevant distractors affect visuo-motor control for target acquisition and studying such effects has already received much attention in human-computer interaction. However, there has been little research into distractor effects on crossing-based interaction. We thus conducted an empirical study on pen-based interfaces to investigate six crossing tasks with distractor interference in comparison to two tasks without it. The six distractor-related tasks differed in movement precision constraint (directional/amplitude), target size, target distance, distractor location and target-distractor spacing. We also developed and experimentally validated six quantitative models for the six tasks. Our results show that crossing targets with distractors had longer average times and similar accuracy than that without distractors. The effects of distractors varied depending on distractor location, target-distractor spacing and movement precision constraint. When spacing is smaller than 11.27 mm, crossing tasks with distractor interference can be regarded as pointing tasks or a combination of pointing and crossing tasks, which could be better fitted with our proposed models than Fitts' law. According to these results, we provide practical implications to crossing-based user interface design.2021HTHuawei Tu et al.La Trobe UniversityUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI
WeDA: Designing and Evaluating A Scale-driven Wearable Diagnostic Assessment System for Children with ADHDAttention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders affecting children. Because the etiology of ADHD is complex and its symptoms are not specific, there is a lack of feasible quantitative diagnostic methods. Pursuing objective and non-invasive detection methods and standards is of great practical significance to prevent the development of the disease. In this study, we aim to address one specific concern about the objectivity and quantification of ADHD diagnosis. Over a year, we iteratively designed and tested WeDA, a scale-driven wearable diagnostic assessment system. This system contains an Android computer machine with a large touchscreen, a suite of 3D printed interactive devices, and six wearable motion sensors. We implement ten diagnostic tasks drawing on the symptoms of ADHD based on DSM-5. The experimental results of classifying children with ADHD and typically developing children and subjective evaluations from doctors, parents, and children validate the effectiveness and acceptability of WeDA.2020XJXinlong Jiang et al.Institute of Computing Technology, CAS & Beijing Key Laboratory of Mobile Computing and Pervasive DeviceCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Special Education TechnologyBiosensors & Physiological MonitoringCHI
Get a Grip: Evaluating Grip Gestures for VR Input using a Lightweight PenThe use of Virtual Reality (VR) in applications such as data analysis, artistic creation, and clinical settings requires high precision input. However, the current design of handheld controllers, where wrist rotation is the primary input approach, does not exploit the human fingers' capability for dexterous movements for high precision pointing and selection. To address this issue, we investigated the characteristics and potential of using a pen as a VR input device. We conducted two studies. The first examined which pen grip allowed the largest range of motion---we found a tripod grip at the rear end of the shaft met this criterion. The second study investigated target selection via 'poking' and ray-casting, where we found the pen grip outperformed the traditional wrist-based input in both cases. Finally, we demonstrate potential applications enabled by VR pen input and grip postures.2020NLNianlong Li et al.Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of SciencesFull-Body Interaction & Embodied InputSocial & Collaborative VRCHI
A Palette of Deepened Emotions: Exploring Emotional Challenge in Virtual Reality GamesRecent work introduced the notion of 'emotional challenge' promising for understanding more unique and diverse player experiences (PX). Although emotional challenge has immediately attracted HCI researchers' attention, the concept has not been experimentally explored, especially in virtual reality (VR), one of the latest gaming environments. We conducted two experiments to investigate how emotional challenge affects PX when separately from or jointly with conventional challenge in VR and PC conditions. We found that relatively exclusive emotional challenge induced a wider range of different emotions in both conditions, while the adding of emotional challenge broadened emotional responses only in VR. In both experiments, VR significantly enhanced the measured PX of emotional responses, appreciation, immersion and presence. Our findings indicate that VR may be an ideal medium to present emotional challenge and also extend the understanding of emotional (and conventional) challenge in video games.2020XPXiaolan Peng et al.Chinese Academy of Sciences & University of Chinese Academy of SciencesImmersion & Presence ResearchGame UX & Player BehaviorCHI
Modeling the Endpoint Uncertainty in Crossing-based Moving Target SelectionModeling the endpoint uncertainty of moving target selection with crossing is essential to understand factors such as speed-accuracy trade-off and interaction efficiency in crossing-based user interfaces with dynamic contents. However, there have been few studies looking into this research topic in the HCI field. This paper presents a Quaternary-Gaussian model to quantitatively measure the endpoint uncertainty in crossing-based moving target selection. To validate this model, we conducted an experiment with discrete crossing tasks on five factors, i.e., initial distance, size, speed, orientation, and moving direction. Results showed that our model fit the data of ? and ? accurately with adjusted R2 of 0.883 and 0.920. We also demonstrated the validity of our model in predicting error rates in crossing-based moving target selection. We concluded with a set of implications for future designs.2020JHJin Huang et al.Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of SciencesEye Tracking & Gaze InteractionContext-Aware ComputingCHI
Modeling the Uncertainty in 2D Moving Target SelectionUnderstanding the selection uncertainty of moving targets is a fundamental research problem in HCI. However, the only few works in this domain mainly focus on selecting 1D moving targets with certain input devices, where the model generalizability has not been extensively investigated. In this paper, we propose a 2D Ternary-Gaussian model to describe the selection uncertainty manifested in endpoint distribution for moving target selection. We explore and compare two candidate methods to generalize the problem space from 1D to 2D tasks, and evaluate their performances with three input modalities including mouse, stylus, and finger touch. By applying the proposed model in assisting target selection, we achieved 56.7% improvement in selection speed and 78.8% improvement in pointing accuracy. In addition, we found that when predicting pointing errors, our model can fit the data of error rates with 0.94 R2.2019JHJin Huang et al.Visualization Perception & CognitionComputational Methods in HCIUIST
SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal NetworkInstant photo taking and sharing has become one of the most popular forms of social networking. However, taking high-quality photos is difficult as it requires knowledge and skill in photography that most non-expert users lack. In this paper we present SmartEye, a novel mobile system to help users take photos with good compositions in-situ. The back-end of SmartEye integrates the View Proposal Network (VPN), a deep learning based model that outputs composition suggestions in real time, and a novel, interactively updated module (P-Module) that adjusts the VPN outputs to account for personalized composition preferences. We also design a novel interface with functions at the front-end to enable real-time and informative interactions for photo taking. We conduct two user studies to investigate SmartEye qualitatively and quantitatively. Results show that SmartEye effectively models and predicts personalized composition preferences, provides instant high-quality compositions in-situ, and outperforms the non-personalized systems significantly.2019SMShuai Ma et al.Institute of Software, Chinese Academy of Sciences & University of Chinese Academy of SciencesGenerative AI (Text, Image, Music, Video)Graphic Design & Typography ToolsCHI
Understanding the Uncertainty in 1D Unidirectional Moving Target SelectionIn contrast to the extensive studies on static target pointing, much less formal understanding of moving target acquisition can be found in the HCI literature. We designed a set of experiments to identify regularities in 1D unidirectional moving target selection, and found a Ternary-Gaussian model to be descriptive of the endpoint distribution in such tasks. The shape of the distribution as characterized by μ and σ in the Gaussian model were primarily determined by the speed and size of the moving target. The model fits the empirical data well with 0.95 and 0.94 R2 values for μ and σ, respectively. We also demonstrated two extensions of the model, including 1) predicting error rates in moving target selection; and 2) a novel interaction technique to implicitly aid moving target selection. By applying them in a game interface design, we observed good performances in both predicting error rates (e.g., 2.7% mean absolute error) and assisting moving target selection (e.g., 33% or a greater increase in pointing accuracy).2018JHJin Huang et al.Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of SciencesHand Gesture RecognitionVoice User Interface (VUI) DesignGame UX & Player BehaviorCHI