ZuantuSet: A Collection of Historical Chinese Visualizations and IllustrationsHistorical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions.2025XMXiyao Mei et al.Peking University, National Key Laboratory of General Artificial Intelligence and School of Intelligence Science and TechnologyInteractive Data VisualizationMuseum & Cultural Heritage DigitizationCHI
Beyond Explicit and Implicit: How Users Provide Feedback to Shape Personalized Recommendation ContentAs personalized recommendation algorithms become integral to social media platforms, users are increasingly aware of their ability to influence recommendation content. However, limited research has explored how users provide feedback through their behaviors and platform mechanisms to shape the recommendation content. We conducted semi-structured interviews with 34 active users of algorithmic-driven social media platforms (e.g., Xiaohongshu, Douyin). In addition to explicit and implicit feedback, this study introduced intentional implicit feedback, highlighting the actions users intentionally took to refine recommendation content through perceived feedback mechanisms. Additionally, choices of feedback behaviors were found to align with specific purposes. Explicit feedback was primarily used for feed customization, while unintentional implicit feedback was more linked to content consumption. Intentional implicit feedback was employed for multiple purposes, particularly in increasing content diversity and improving recommendation relevance. This work underscores the user intention dimension in the explicit-implicit feedback dichotomy and offers insights for designing personalized recommendation feedback that better responds to users' needs.2025WLWenqi Li et al.Peking University, Department of Information ManagementExplainable AI (XAI)Recommender System UXCHI
EchoSight: Streamlining Bidirectional Virtual-physical Interaction with In-situ Optical TetheringEmerging AR applications require seamless integration of the virtual and physical worlds, which calls for tools that support both passive perception and active manipulation of the environment, enabling bidirectional interaction. We introduce EchoSight, a system for AR glasses that enables efficient look-and-control bidirectional interaction. EchoSight exploits optical wireless communication to instantaneously connect virtual data with its physical counterpart. EchoSight's unique dual-element optical design leverages beam directionality to automatically align the user's focus with target objects, reducing the overhead in both target identification and subsequent communication. This approach streamlines user interaction, reducing cognitive load and enhancing engagement. Our evaluations demonstrate EchoSight's effectiveness for room-scale communication, achieving distances up to 5 m and viewing angles up to 120 degrees. A study with 12 participants confirms EchoSight's improved efficiency and user experience over traditional methods, such as QR Code scanning and voice control, in AR IoT applications.2025JLJingyu Li et al.Peking University, SCSAR Navigation & Context AwarenessContext-Aware ComputingSmart Home Interaction DesignCHI
CrowdBot: An Open-Environment Robot Management System for On-Campus ServicesWang 等人设计 CrowdBot 开放环境机器人管理系统，实现校园场景下机器人的自主导航与任务调度，为校园服务机器人的高效管理提供解决方案。2024YWYufei Wang et al.Domestic RobotsSocial Robot InteractionUbiComp
Waffle: A Waterproof mmWave-based Human Sensing System inside Bathrooms with Running WaterZhang 等人开发 Waffle 防水毫米波传感系统，专门解决浴室有流水环境中的人体感知难题，实现全天候室内监测。2024XZXusheng Zhang et al.Human Pose & Activity RecognitionContext-Aware 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
Understanding the Effects of Restraining Finger Coactivation in Mid-Air Typing: from a Neuromechanical PerspectiveTyping in mid-air is often perceived as intuitive yet presents challenges due to finger coactivation, a neuromechanical phenomenon that involves involuntary finger movements stemming from the lack of physical constraints. Previous studies were used to examine and address the impacts of finger coactivation using algorithmic approaches. Alternatively, this paper explores the neuromechanical effects of finger coactivation on mid-air typing, aiming to deepen our understanding and provide valuable insights to improve these interactions. We utilized a wearable device that restrains finger coactivation as a prop to conduct two mid-air studies, including a rapid finger-tapping task and a ten-finger typing task. The results revealed that restraining coactivation not only reduced mispresses, which is a classic coactivated error always considered as harm caused by coactivation. Unexpectedly, the reduction of motor control errors and spelling errors, thinking as non-coactivated errors, also be observed. Additionally, the study evaluated the neural resources involved in motor execution using functional Near Infrared Spectroscopy (fNIRS), which tracked cortical arousal during mid-air typing. The findings demonstrated decreased activation in the primary motor cortex of the left hemisphere when coactivation was restrained, suggesting a diminished motor execution load. This reduction suggests that a portion of neural resources is conserved, which also potentially aligns with perceived lower mental workload and decreased frustration levels.2024HZHechuan Zhang et al.Full-Body Interaction & Embodied InputUIST
Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona DescriptionsLarge language models (LLMs) can generate personas based on prompts that describe the target user group. To understand what kind of personas LLMs generate, we investigate the diversity and bias in 450 LLM-generated personas with the help of internal evaluators (n=4) and subject-matter experts (SMEs) (n=5). The research findings reveal biases in LLM-generated personas, particularly in age, occupation, and pain points, as well as a strong bias towards personas from the United States. Human evaluations demonstrate that LLM persona descriptions were informative, believable, positive, relatable, and not stereotyped. The SMEs rated the personas slightly more stereotypical, less positive, and less relatable than the internal evaluators. The findings suggest that LLMs can generate consistent personas perceived as believable, relatable, and informative while containing relatively low amounts of stereotyping.2024JSJoni Salminen et al.University of VaasaHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityCHI
A Systematic Review of Ability-diverse Collaboration through Ability-based Lens in HCI In a world where diversity is increasingly recognised and celebrated, it is important for HCI to embrace the evolving methods and theories for technologies to reflect the diversity of its users and be ability-centric. Interdependence Theory, an example of this evolution, highlights the interpersonal relationships between humans and technologies and how technologies should be designed to meet shared goals and outcomes for people, regardless of their abilities. This necessitates a contemporary understanding of "ability-diverse collaboration," which motivated this review. In this review, we offer an analysis of 117 papers sourced from the ACM Digital Library spanning the last two decades. We contribute (1) a unified taxonomy and the Ability-Diverse Collaboration Framework, (2) a reflective discussion and mapping of the current design space, and (3) future research opportunities and challenges. Finally, we have released our data and analysis tool to encourage the HCI research community to contribute to this ongoing effort.2024LXLan Xiao et al.University College London, University College LondonCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Universal & Inclusive DesignInclusive DesignCHI
Make Interaction Situated: Designing User Acceptable Interaction for Situated Visualization in Public EnvironmentsSituated visualization blends data into the real world to fulfill individuals’ contextual information needs. However, interacting with situated visualization in public environments faces challenges posed by users’ acceptance and contextual constraints. To explore appropriate interaction design, we first conduct a formative study to identify users’ needs for data and interaction. Informed by the findings, we summarize appropriate interaction modalities with eye-based, hand-based and spatially-aware object interaction for situated visualization in public environments. Then, through an iterative design process with six users, we explore and implement interactive techniques for activating and analyzing with situated visualization. To assess the effectiveness and acceptance of these interactions, we integrate them into an AR prototype and conduct a within-subjects study in public scenarios using conventional hand-only interactions as the baseline. The results show that participants preferred our prototype over the baseline, attributing their preference to the interactions being more acceptable, flexible, and practical in public.2024QZQian Zhu et al.The Hong Kong University of Science and Technology, The Hong Kong University of Science and TechnologyAR Navigation & Context AwarenessContext-Aware ComputingField StudiesCHI
Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older AdultsMusic-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults’ attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.2024YJYucheng Jin et al.Hong Kong Baptist UniversityGenerative AI (Text, Image, Music, Video)Mental Health Apps & Online Support CommunitiesReproductive & Women's HealthCHI
StarRescue: the Design and Evaluation of A Turn-Taking Collaborative Game for Facilitating Autistic Children's Social SkillsAutism Spectrum Disorder (ASD) presents challenges in social interaction skill development, particularly in turn-taking. Digital interventions offer potential solutions for improving autistic children's social skills but often lack addressing specific collaboration techniques. Therefore, we designed a prototype of a turn-taking collaborative tablet game, StarRescue, which encourages children's distinct collaborative roles and interdependence while progressively enhancing sharing and mutual planning skills. We further conducted a controlled study with 32 autistic children to evaluate StarRescue's usability and potential effectiveness in improving their social skills. Findings indicated that StarRescue has great potential to foster turn-taking skills and social communication skills (e.g., prompting, negotiation, task allocation) within the game and also extend beyond the game. Additionally, we discussed implications for future work, such as including parents as game spectators and understanding autistic children's territory awareness in collaboration. Our study contributes a promising digital intervention for autistic children's turn-taking social skill development via a scaffolding approach and valuable design implications for future research.2024RBRongqi Bei et al.University of MichiganCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Serious & Functional GamesCHI
LT-Fall: The Design and Implementation of a Life-threatening Fall Detection and Alarming SystemFalls are the leading cause of fatal injuries to elders in modern society, which has motivated researchers to propose various fall detection technologies. We observe that most of the existing fall detection solutions are diverging from the purpose of fall detection: timely alarming the family members, medical staff or first responders to save the life of the human with severe injury caused by fall. Instead, they focus on detecting the behavior of human falls, which does not necessarily mean a human is in real danger. The real critical situation is when a human cannot get up without assistance and is thus lying on the ground after the fall because of losing consciousness or becoming incapacitated due to severe injury. In this paper, we define a life-threatening fall as a behavior that involves a falling down followed by a long-lie of humans on the ground, and for the first time point out that a fall detection system should focus on detecting life-threatening falls instead of detecting any random falls. Accordingly, we design and implement LT-Fall, a mmWave-based life-threatening fall detection and alarming system. LT-Fall detects and reports both fall and fall-like behaviors in the first stage and then identifies life-threatening falls by continuously monitoring the human status after fall in the second stage. We propose a joint spatio-temporal localization technique to detect and locate the micro-motions of the human, which solves the challenge of mmWave's insufficient spatial resolution when the human is static, i.e., lying on the ground. Extensive evaluation on 15 volunteers demonstrates that compared to the state-of-the-art work (92% precision and 94% recall), LT-Fall achieves zero false alarms as well as a precision of 100% and a recall of 98.8%. https://dl.acm.org/doi/10.1145/35808352023DZDuo Zhang et al.Elderly Care & Dementia SupportBiosensors & Physiological MonitoringUbiComp
WiMeasure: Millimeter-level Object Size Measurement with Commodity WiFi DevicesIn the past few years, a large range of wireless signals such as WiFi, RFID, UWB and Millimeter Wave were utilized for sensing purposes. Among these wireless sensing modalities, WiFi sensing attracts a lot of attention owing to the pervasiveness of WiFi infrastructure in our surrounding environments. While WiFi sensing has achieved a great success in capturing the target's motion information ranging from coarse-grained activities and gestures to fine-grained vital signs, it still has difficulties in precisely obtaining the target size owing to the low frequency and small bandwidth of WiFi signals. Even Millimeter Wave radar can only achieve a very coarse-grained size measurement. High precision object size sensing requires using RF signals in the extremely high-frequency band (e.g., Terahertz band). In this paper, we utilize low-frequency WiFi signals to achieve accurate object size measurement without requiring any learning or training. The key insight is that when an object moves between a pair of WiFi transceivers, the WiFi CSI variations contain singular points (i.e., singularities) and we observe an exciting opportunity of employing the number of singularities to measure the object size. In this work, we model the relationship between the object size and the number of singularities when an object moves near the LoS path, which lays the theoretical foundation for the proposed system to work. By addressing multiple challenges, for the first time, we make WiFi-based object size measurement work on commodity WiFi cards and achieve a surprisingly low median error of 2.6 mm. We believe this work is an important missing piece of WiFi sensing and opens the door to size measurement using low-cost low-frequency RF signals. https://dl.acm.org/doi/10.1145/35962502023XWXuanzhi Wang et al.Context-Aware ComputingUbiquitous ComputingComputational Methods in HCIUbiComp
BLEselect: Gestural IoT Device Selection via Bluetooth Angle of Arrival Estimation from Smart Glasses"Spontaneous selection of IoT devices from the head-mounted device is key for user-centered pervasive interaction. BLEselect enables users to select an unmodified Bluetooth 5.1 compatible IoT device by nodding at, pointing at, or drawing a circle in the air around it. We designed a compact antenna array that fits on a pair of smart glasses to estimate the Angle of Arrival (AoA) of IoT and wrist-worn devices' advertising signals. We then developed a sensing pipeline that supports all three selection gestures with lightweight machine learning models, which are trained in real-time for both hand gestures. Extensive characterizations and evaluations show that our system is accurate, natural, low-power, and privacy-preserving. Despite the small effective size of the antenna array, our system achieves a higher than 90% selection accuracy within a 3 meters distance in front of the user. In a user study that mimics real-life usage cases, the overall selection accuracy is 96.7% for a diverse set of 22 participants in terms of age, technology savviness, and body structures. https://dl.acm.org/doi/10.1145/3569482"2023TZTengxiang Zhang et al.On-Skin Display & On-Skin InputContext-Aware ComputingUbiquitous ComputingUbiComp
Understanding Disclosure and Support in Social Music Communities for Youth Mental HealthOnline music platforms that embed social features are enabling the creation of supportive social communities where many young people disclose their distressing feelings and seek support. However, there is a limited understanding of the content young people disclose or the support they may provide in such social music communities. In this work, using a large online music platform as our research site, we employed mixed methods to analyze users' comments (N=163) and the associated replies (N=2,732) related to young people's psychological distress (e.g., depression, anxiety, stress, and loneliness). We found that experience sharing dominates the types of comments, which often invokes peers' support in the form of encouragement, caring, or self-disclosure. Furthermore, we conducted an interview study with 13 young people to understand their perceptions of and motives for disclosure and support on our research site. The interviewees expressed that music-induced and comment-induced emotional resonance is the main drive for their disclosure and support. Finally, we discuss design implications for a supportive social music community that could benefit youth mental health.2023YJYucheng Jin et al.Mental Health IICSCW
Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social NetworksWith the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.2021LWLiang Wang et al.Crowds and CollaborationCSCW
IGScript: An Interaction Grammar for Scientific Data PresentationMost of the existing scientific visualizations toward interpretive grammar aim to enhance customizability in either the computation stage or the rendering stage or both, while few approaches focus on the data presentation stage. Besides, most of these approaches leverage the existing components from the general-purpose programming languages (GPLs) instead of developing a standalone compiler, which pose a great challenge about learning curves for the domain experts who have limited knowledge about programming. In this paper, we propose IGScript, a novel script-based interaction grammar tool, to help build scientific data presentation animations for communication. We design a dual-space interface and a compiler which converts natural language-like grammar statements or scripts into a data story animation to make an interactive customization on script-driven data presentations, and then develop a code generator (decompiler) to translate the interactive data exploration animations back into script codes to achieve statement parameters. IGScript makes the presentation animations editable, e.g., it allows to cut, copy, paste, append, or even delete some animation clips. We demonstrate the usability, customizability, and flexibility of IGScript by a user study, four case studies conducted by using four types of commonly-used scientific data, and performance evaluations.2021RLRichen Liu et al.Nanjing Normal UniversityInteractive Data VisualizationData StorytellingCHI
Understanding and Predicting the Burst of Burnout via Social MediaJob burnout is a special type of work-related stress that is prevalent in our modern society, and constant burnout is extremely harmful for people’s physical health and emotional wellbeing. Traditional studies for burnout mainly rely on surveys/questionnaires, which have revealed several interesting findings but are of high cost and very time consuming. With the prevalence of social networking applications, we aim to re-investigate the burnout phenomenon in a novel perspective. In this paper, we collected a dataset consisting of 1532 burnout Weibo users with their postings. Based on the previous literature, we propose a number of hypotheses about what might be the burst signal of the burnout from the perspective of language, time and interaction. Furthermore, extensive correlation analysis is conducted to investigate if these hypotheses are supported, which leads to a number of interesting findings. Finally, we develop machine learning models to predict the burst of burnout based on extracted features and achieve a relatively high accuracy, which reveals potential implications in early-stage intervention.2020JWJue Wu et al.Data and Social Media for HealthCSCW