GLITTER: An AI-assisted Platform for Material-Grounded Asynchronous Discussion in Flipped LearningFlipped classrooms promote active learning by having students engage with materials independently before class, allowing in-class time for collaborative problem-solving. During this pre-class phase, asynchronous online discussions help students build knowledge and clarify concepts with peers. However, it remains difficult to engage with temporally dispersed peer contributions, connect discussions with static learning materials, and prepare for in-class sessions based on their self-learning outcome. Our formative study identified cognitive challenges students encounter, including navigation barriers, reflection gaps, and contribution difficulty and anxiety. We present GLITTER, an AI-assisted discussion platform for pre-class learning in flipped classrooms. GLITTER helps students identify posts with shared conceptual dimensions, scaffold knowledge integration through conceptual blending, and enhance metacognition via personalized reflection reports. A lab study within subjects (n = 12) demonstrates that GLITTER improves discussion engagement, sparks new ideas, supports reflection, and increases preparedness for in-class activities.2025WPWeirui Peng et al.K-12 Digital Education ToolsOnline Learning & MOOC PlatformsCollaborative Learning & Peer TeachingUIST
A Dynamic Bayesian Network Based Framework for Multimodal Context-Aware InteractionsMultimodal context-aware interactions integrate multiple sensory inputs, such as gaze, gestures, speech, and environmental signals, to provide adaptive support across diverse user contexts. Building such systems is challenging due to the complexity of sensor fusion, real-time decision-making, and managing uncertainties from noisy inputs. To address these challenges, we propose a hybrid approach combining a dynamic Bayesian network (DBN) with a large language model (LLM). The DBN offers a probabilistic framework for modeling variables, relationships, and temporal dependencies, enabling robust, real-time inference of user intent, while the LLM incorporates world knowledge for contextual reasoning beyond explicitly modeled relationships. We demonstrate our approach with a tri-level DBN implementation for tangible interactions, integrating gaze and hand actions to infer user intent in real time. A user evaluation with 10 participants in an everyday office scenario showed that our system can accurately and efficiently infer user intentions, achieving 0.83 per frame accuracy, even in complex environments. These results validate the effectiveness of the DBN+LLM framework for multimodal context-aware interactions.2025VHViolet Yinuo Han et al.Context-Aware ComputingComputational Methods in HCIIUI
LADICA: A Large Shared Display Interface for Generative AI Cognitive Assistance in Co-located Team CollaborationLarge shared displays, such as digital whiteboards, are useful for supporting co-located team collaborations by helping members perform cognitive tasks such as brainstorming, organizing ideas, and making comparisons. While recent advancement in Large Language Models (LLMs) has catalyzed AI support for these displays, most existing systems either only offer limited capabilities or diminish human control, neglecting the potential benefits of natural group dynamics. Our formative study identified cognitive challenges teams encounter, such as diverse ideation, knowledge sharing, mutual awareness, idea organization, and synchronization of live discussions with the external workspace. In response, we introduce LADICA, a large shared display interface that helps collaborative teams brainstorm, organize, and analyze ideas through multiple analytical lenses, while fostering mutual awareness of ideas and concepts. Furthermore, LADICA facilitates the real-time extraction of key information from verbal discussions and identifies relevant entities. A lab study confirmed LADICA's usability and usefulness.2025ZZZheng Zhang et al.University of Notre Dame, Department of Computer Science and EngineeringHuman-LLM CollaborationRemote Work Tools & ExperienceCHI
Designing Health Technologies for Immigrant Communities: Exploring Healthcare Providers’ Communication Strategies with PatientsPatient-provider communication is an important aspect of successful healthcare, as it can directly lead to positive health outcomes. Previous studies examined factors that facilitate communication between healthcare providers and patients in socially marginalized communities, especially developing countries, and applied identified factors to technology development. However, there is limited understanding of how providers work with patients from immigrant populations in a developed country. By conducting semi-structured interviews with 15 providers working with patients from an immigrant community with unique cultural characteristics, we identified providers’ effective communication strategies, including acknowledgment, community involvement, gradual care, and adaptive communication practices (i.e., adjusting the communication style). Based on our findings, we highlight cultural competence and discuss design implications for technologies to support health communication in immigrant communities. Our suggestions propose approaches for HCI researchers to identify practical, contextualized cultural competence for their health technology design.2025ZCZhanming Chen et al.University of Minnesota, College of DesignMental Health Apps & Online Support CommunitiesCommunity Engagement & Civic TechnologyEmpowerment of Marginalized GroupsCHI
Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing: A Case Study with Patients Undergoing Lumbar Spine SurgeryXu 等人利用多模态移动传感技术收集术前患者活动、睡眠和生理数据，预测腰椎手术后的疼痛、功能恢复和住院时长等结局指标。2024ZXZiqi Xu et al.Mental Health Apps & Online Support CommunitiesBiosensors & Physiological MonitoringUbiComp
EyeGesener: Eye Gesture Listener for Smart Glasses Interaction using Acoustic SensingSun 等人提出 EyeGesener 系统，利用声学感知技术识别智能眼镜用户的眼球手势，实现无需触控的自然交互方式，识别准确率达 95% 以上。2024TSTao Sun et al.Haptic WearablesEye Tracking & Gaze InteractionUbiComp
DeepBreath: Breathing Exercise Assessment with a Depth CameraXie 等人开发 DeepBreath 深度摄像系统，通过分析胸腹部轮廓变化自动评估呼吸练习，为用户提供实时反馈和指导。2024WXWentao Xie et al.Vibrotactile Feedback & Skin StimulationBiosensors & Physiological MonitoringUbiComp
AquaKey: Exploiting the Randomness of the Underwater Visible Light Communication Channel for Key ExtractionZhang 等人提出 AquaKey 系统，利用水下可见光通信信道的随机性提取加密密钥，为水下网络安全通信提供新方案。2024LZLupeng Zhang et al.Passwords & AuthenticationUbiComp
Evaluating the Privacy Valuation of Personal Data on SmartphonesFan 等人研究智能手机用户对个人数据隐私的价值评估问题。2024LFLihua Fan et al.Privacy Perception & Decision-MakingUbiComp
SmallMap: Low-cost Community Road Map Sensing with Uncertain Delivery BehaviorHong 等人提出 SmallMap 低成本社区道路地图感知方案，解决不确定数据交付问题。2024ZHZhiqing Hong et al.Geospatial & Map VisualizationUncertainty VisualizationUbiComp
mmArrhythmia: Contactless Arrhythmia Detection via mmWave SensingZhao 等人提出 mmArrhythmia 系统，利用毫米波雷达实现无接触式心律失常检测，无需佩戴任何传感器即可监测心脏节律异常2024LZLangcheng Zhao et al.Biosensors & Physiological MonitoringUbiComp
RDGait: A mmWave Based Gait Recognition System for Complex Indoor Environments Using Single-chip RadarWang等人提出RDGait毫米波步态识别系统，利用单芯片雷达和优化算法在复杂室内环境中实现高精度身份验证。2024DWDequan Wang et al.Human Pose & Activity RecognitionContext-Aware ComputingUbiComp
SF-Adapter: Computational-Efficient Source-Free Domain Adaptation for Human Activity RecognitionKang 等人提出 SF-Adapter 框架，实现计算高效的无源域自适应人体活动识别，在不访问源域数据的情况下降低领域偏移的影响。2024HKHua Kang et al.Human Pose & Activity RecognitionUbiComp
Push the Limit of Highly Accurate Ranging on Commercial UWB DevicesMa 等人提出针对商业 UWB 设备的高精度测距优化方案，突破现有技术极限，提升室内定位精度。2024JMJunqi Ma et al.Context-Aware ComputingUbiquitous ComputingUbiComp
GrainSense: A Wireless Grain Moisture Sensing System based on Wi-Fi SignalsWang 等人开发 GrainSense 系统，利用 Wi-Fi 信号无接触测量谷物水分，实现仓储谷物湿度的实时无线监测。2024ZWZhu Wang et al.Context-Aware ComputingUbiquitous ComputingEcological Design & Green ComputingUbiComp
Embracing Distributed Acoustic Sensing in Car Cabin for Children Presence DetectionSu 等人利用分布式声学传感技术检测车内儿童存在状态，通过声学特征识别防止儿童被遗忘在车内，提升乘车安全。2024YSYuqi Su et al.In-Vehicle Haptic, Audio & Multimodal FeedbackMotion Sickness & Passenger ExperienceV2X (Vehicle-to-Everything) Communication DesignUbiComp
WiFi-CSI Difference Paradigm: Achieving Efficient Doppler Speed Estimation for Passive TrackingLi等人提出WiFi-CSI差异范式，通过分析信道状态信息的时变特性实现高效的被动目标多普勒速度估计，为低成本无线追踪系统提供新方案。2024WLWenwei Li et al.Biosensors & Physiological MonitoringContext-Aware ComputingUbiComp
UniFi: A Unified Framework for Generalizable Gesture Recognition with Wi-Fi Signals Using Consistency-guided Multi-View NetworksLiu 等人提出 UniFi 框架，利用一致性引导多视图网络实现基于 Wi-Fi 信号的通用手势识别。2024YLYan Liu et al.Hand Gesture RecognitionUbiComp
MIMOSA: Human-AI Co-Creation of Computational Spatial Audio Effects on VideosSpatial audio offers more immersive video consumption experiences to viewers; however, creating and editing spatial audio often expensive and requires specialized equipment and skills, posing a high barrier for amateur video creators. We present MIMOSA, a human-AI co-creation tool that enables amateur users to computationally generate and manipulate spatial audio effects. For a video with only monaural or stereo audio, MIMOSA automatically grounds each sound source to the corresponding sounding object in the visual scene and enables users to further validate and fix the errors in the locations of sounding objects. Users can also augment the spatial audio effect by flexibly manipulating the sounding source positions and creatively customizing the audio effect. The design of MIMOSA exemplifies a human-AI collaboration approach that, instead of utilizing state-of-art end-to-end "black-box" ML models, uses a multistep pipeline that aligns its interpretable intermediate results with the user’s workflow. A lab user study with 15 participants demonstrates MIMOSA’s usability, usefulness, expressiveness, and capability in creating immersive spatial audio effects in collaboration with users.2024ZNZheng Ning et al.Generative AI (Text, Image, Music, Video)Music Composition & Sound Design ToolsCreative Collaboration & Feedback SystemsC&C
CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language ModelsCollaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself can be both complex and costly. To lower this bar, we take a theoretical perspective to design a one-stop, end-to-end workflow, CollabCoder, that integrates Large Language Models (LLMs) into key inductive CQA stages. In the independent open coding phase, CollabCoder offers AI-generated code suggestions and records decision-making data. During the iterative discussion phase, it promotes mutual understanding by sharing this data within the coding team and using quantitative metrics to identify coding (dis)agreements, aiding in consensus-building. In the codebook development phase, CollabCoder provides primary code group suggestions, lightening the workload of developing a codebook from scratch. A 16-user evaluation confirmed the effectiveness of CollabCoder, demonstrating its advantages over the existing CQA platform. All related materials of CollabCoder, including code and further extensions, will be included in: https://gaojie058.github.io/CollabCoder/.2024JGJie Gao et al.Singapore University of Technology and DesignHuman-LLM CollaborationUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingCHI