Shaking a cup of wine or other fluids in virtual environments is engaging but has been limited by challenges in delivering real-time haptic feedback for liquid collisions. ShakeSense is a haptic rendering system that integrates electrotactile stimulation with physics-based simulation to deliver immersive feedback for liquid dynamics in handheld containers. It employs a high-density electrode array to deliver dynamic tactile sensations, conveying friction and pressure changes on the user’s fingerpad. A dedicated end-to-end pipeline computes fingerpad forces from liquid-container-finger interactions, ensuring feedback aligns with natural fluid movement. Two studies evaluated ShakeSense’s performance and user perception. Study 1 showed that electrotactile patterns were distinguishable across directions, and synchronizing container movement with stimulation enhanced perceived force changes. Study 2 demonstrated that ShakeSense effectively simulated liquid motion, capturing multidimensional, coordinated interactions, and outperformed conventional Center-of-Mass approaches. Overall, ShakeSense provides clear, fine-grained tactile feedback for fluid interactions.

Parkinson’s disease (PD) commonly leads to gait disorders that necessitate long-term rehabilitation dependent on specialists and clinic-based interventions. To reduce dependence on clinicians and investigate how wearable technology can provide continuous guidance for rehabilitation training. We distilled key design principles from patient–clinician interviews and co-designed a gait training system. The system employs inertial measurement units (IMUs) to capture kinematic data, then delivers multimodal cueing (visual, auditory, and somatosensory) aligned with walking features. Two user studies (N = 16 PD patients) evaluated the effectiveness of multimodal cueing, examining strategies for information delivery and gait correction. Results indicated that visual and auditory cueing were more effective for process-oriented adjustments, whereas somatosensory stimulation better supported periodic cueing. Moreover, a dissociation between performance outcomes and user preferences was observed. These findings highlight the potential of wearable technology to provide continuous, daily training guidance for PD patients.

Motion sickness, in addition to its persistent long-term effects, also exhibits short-term effects characterized as transient physiological discomfort, which changes rapidly with variations in locomotion. However, such discomforts are challenging to assess using current subjective scales and objective physiological measurements. To tackle this issue, this paper suggests continuous measurement methods designed specifically for evaluating transient physiological discomfort during VR locomotion. Through a user-elicitation study, three preferred measurement methods—’squeezing ball’, ’sliding thumb’, and ’rubbing thigh’—were identified. These techniques were then evaluated for reliability, validity, attention, presence, and workload, with ’sliding thumb’ identified as the most effective option. The paper expands traditional measurement methods to capture users’ physiological experiences in VR interactions, offering practical choices for researchers in this field along with an in-depth discussion of design considerations, detailed implementation guidelines, and potential ways to optimize the VR experiences utilizing the measurement data.

Grasping objects typically relies on visual input to pre-shape the hand and plan movement trajectories, a process often disrupted in visually impaired (VI) individuals. ElectroGrasp is a wearable electro-tactile system that delivers anticipatory proprioceptive and tactile information through three complementary modalities: Grasping Orientation, Size, and Shape. This system dynamically conveys spatial features-thereby enhancing anticipatory grasp planning and control through tactile perception. Three experiments were conducted to evaluate ElectroGrasp. The first examined tactile pattern discriminability, size perception thresholds, and the reliability of orientation encoding. The second assessed learning time with ElectroGrasp and its effectiveness in supporting spatial representation, demonstrating accurate spatial perception of objects from electrotactile input. The third compared grasp aperture under audio versus electrotactile cues, revealing that ElectroGrasp reduced hand overshoot and regrasp corrections. Overall, the results demonstrate that ElectroGrasp provides efficient tactile information, enables improved anticipatory grasp planning comparable to visual cues, and offers a novel assistive solution for VI users.

This paper presents an N-ary Gaussian Model for predicting endpoint distributions in pointing tasks across task scenarios. Built on the foundational principles of the Ternary Gaussian model series, our model framework allows researchers to define parameter constraints and automatically refine model combinations, eliminating the need for predefined equations based on data analysis. We utilize the Bayesian Information Criterion (BIC) for model selection, ensuring simplicity while maintaining predictive accuracy. We conducted a comparative analysis against published baselines across 7 diverse datasets, covering 1D, 2D, and 3D tasks, different input modalities, different display devices, and time-constrained scenarios, demonstrating the robustness and generalization of the N-ary Gaussian Model. The N-ary Gaussion model offers an automated solution for modeling pointing uncertainty, and also incorporates cross output device, input modality, and temporal constraint factors into spatial pointing uncertainty modeling for the first time.

Immersive creation tools enhance spatial understanding, yet they push animators to abandon mature 2D desktop practices; Pure desktop setups, however, lack embodied spatial manipulation, making tool switching cognitively costly. To address this issue, We present AniXDim, a cross-dimensional interaction system for 3D character animation that unifies precise planar editing and embodied spatial control without explicit mode toggles. A commodity mouse is augmented for on-demand 6DoF manipulation, and a VR controller is endowed with high-precision planar input through adaptive projection, forming bidirectional dimensional augmentation. Users simply raise or rest the device to enable near-frictionless transitions, while a single autostereoscopic desktop display adapts depth to the inferred interaction state, supporting keyframe, pose, and reference authoring within one consistent workspace. A controlled user study comparing AniXDim with pure desktop and pure VR baselines indicates significant performance gains over the desktop baseline and comparable efficiency to the VR baseline, and higher usability ratings while maintaining comparable workload. These findings suggest that near-frictionless dimensional switching can reconcile 2D precision and 3D embodiment, offering a low-learning-overhead pathway for future hybrid desktop animation pipelines and broader 2D–3D authoring domains.

Marine Animal Segmentation (MAS) aims at identifying and segmenting marine animals from complex marine environments. Most of previous deep learning-based MAS methods struggle with the long-distance modeling issue. Recently, Segment Anything Model (SAM) has gained popularity in general image segmentation. However, it lacks of perceiving fine-grained details and frequency information. To this end, we propose a novel learning framework, named Hierarchical Frequency Prompted SAM (HFP-SAM) for high-performance MAS. First, we design a Frequency Guided Adapter (FGA) to efficiently inject marine scene information into the frozen SAM backbone through frequency domain prior masks. Additionally, we introduce a Frequency-aware Point Selection (FPS) to generate highlighted regions through frequency analysis. These regions are combined with the coarse predictions of SAM to generate point prompts and integrate into SAM's decoder for fine predictions. Finally, to obtain comprehensive segmentation masks, we introduce a Full-View Mamba (FVM) to efficiently extract spatial and channel contextual information with linear computational complexity. Extensive experiments on four public datasets demonstrate the superior performance of our approach. We will make our code publicly available upon the acceptance.

Multi-Modal Image Fusion (MMIF) aims to combine images from different modalities to produce fused images, retaining texture details and preserving significant information. Recently, some MMIF methods incorporate frequency domain information to enhance spatial features. However, these methods typically rely on simple serial or parallel spatial-frequency fusion without interaction. In this paper, we propose a novel Interactive Spatial-Frequency Fusion Mamba (ISFM) framework for MMIF. Specifically, we begin with a Modality-Specific Extractor (MSE) to extract features from different modalities. It models long-range dependencies across the image with linear computational complexity. To effectively leverage frequency information, we then propose a Multi-scale Frequency Fusion (MFF). It adaptively integrates low-frequency and high-frequency components across multiple scales, enabling robust representations of frequency features. More importantly, we further propose an Interactive Spatial-Frequency Fusion (ISF). It incorporates frequency features to guide spatial features across modalities, enhancing complementary representations. Extensive experiments are conducted on six MMIF datasets. The experimental results demonstrate that our ISFM can achieve better performances than other state-of-the-art methods. The source code is available at https://github.com/Namn23/ISFM.

Talking head generation is increasingly important in virtual reality (VR), especially for social scenarios involving multi-turn conversation. Existing approaches face notable limitations: mesh-based 3D methods can model dual-person dialogue but lack realistic textures, large-model-based 2D methods produce natural appearances but incur prohibitive computational costs. Recently, 3D Gaussian Splatting (3DGS)-based methods achieve efficient and realistic rendering but remain speaker-only and ignore social relationships. We introduce RSATalker, the first framework that leverages 3DGS for realistic and socially-aware talking head generation, with support for multi-turn conversation. Our method first drives mesh-based 3D facial motion from speech, then binds 3D Gaussians to mesh facets to render high-fidelity 2D avatar videos. To capture interpersonal dynamics, we propose a socially-aware module that encodes social relationships, including blood and non-blood as well as equal and unequal, into high-level embeddings through a learnable query mechanism. We design a three-stage training paradigm and construct the RSATalker dataset with speech-mesh-image triplets annotated with social relationships. Our method supports applications such as VR telepresence, social VR, and embodied conversational agents. The socially-aware conditioning can also be extended to other human motion generation tasks. Extensive experiments demonstrate that RSATalker achieves state-of-the-art performance in both realism and social awareness. The code and dataset will be released.

Rendering photorealistic head avatars from arbitrary viewpoints is crucial for various applications like virtual reality. Although previous methods based on Neural Radiance Fields (NeRF) can achieve impressive results, they lack fidelity and efficiency. Recent methods using 3D Gaussian Splatting (3DGS) have improved rendering quality and real-time performance but still require significant storage overhead. In this paper, we introduce a method called GraphAvatar that utilizes Graph Neural Networks (GNN) to generate 3D Gaussians for the head avatar. Specifically, GraphAvatar trains a geometric GNN and an appearance GNN to generate the attributes of the 3D Gaussians from the tracked mesh. Therefore, our method can store the GNN models instead of the 3D Gaussians, significantly reducing the storage overhead to just 10MB. To reduce the impact of face-tracking errors, we also present a novel graph-guided optimization module to refine face-tracking parameters during training. Finally, we introduce a 3D-aware enhancer for post-processing to enhance the rendering quality. We conduct comprehensive experiments to demonstrate the advantages of GraphAvatar, surpassing existing methods in visual fidelity and storage consumption. The ablation study sheds light on the trade-offs between rendering quality and model size.

Parkinson's Disease (PD) significantly impacts driving abilities, often leading to early driving cessation or accidents due to reduced CHI '25, Yokohama, Japan

Older 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.

Gaze estimation methods typically encounter significant performance degradation in generalized tasks due to the domain mismatch between the source and target domains. Existing approaches attempt to utilize various domain generalization techniques. However, their generalization capabilities are limited since they are constrained to a single visual modality. Notably, large-scale contrastive language-image pre-training (CLIP) models have been widely applied to downstream visual tasks for their robust generalization capabilities, but the potential of CLIP for regression tasks has not been fully explored. To bridge this gap, we introduce a novel framework called CR-CLIP, which endows CLIP with the capability to generalize gaze estimation. Specifically, we convert gaze labels into textual descriptions and achieve alignment between images and text signals with gaze cues, thereby extracting generalized gaze-related features. To enhance the model’s understanding of the numerical relationships of gaze directions, we propose a novel regression loss function based on image-text similarity. Additionally, we fine-tune the model on the original gaze dataset, achieving high precision in generalized gaze estimation. Experimental results show that our proposed method achieves state-of-the-art performance on four generalized gaze estimation tasks.

Gaze estimation encounters generalization challenges when dealing with out-of-distribution data. To address this problem, recent methods use neural radiance fields (NeRF) to generate augmented data. However, existing methods based on NeRF are computationally expensive and lack facial details. 3D Gaussian Splatting (3DGS) has become the prevailing representation of neural fields. While 3DGS has been extensively examined in head avatars, it faces challenges with accurate gaze control and generalization across different subjects. In this work, we propose GazeGaussian, the first high-fidelity gaze redirection method that uses a two-stream 3DGS model to represent the face and eye regions separately. Leveraging the unstructured nature of 3DGS, we develop a novel representation of the eye for rigid eye rotation based on the target gaze direction. To enable synthesis generalization across various subjects, we integrate an expression-guided module to inject subject-specific information into the neural renderer. Comprehensive experiments show that GazeGaussian outperforms existing methods in rendering speed, gaze redirection accuracy, and facial synthesis across multiple datasets. The code is available at: https://ucwxb.github.io/GazeGaussian.

Real-time speech-driven 3D facial animation has been attractive in academia and industry. Traditional methods mainly focus on learning a deterministic mapping from speech to animation. Recent approaches start to consider the nondeterministic fact of speech-driven 3D face animation and employ the diffusion model for the task. Existing diffusion-based methods can improve the diversity of facial animation. However, personalized speaking styles conveying accurate lip language is still lacking, besides, efficiency and compactness still need to be improved. In this work, we propose DiffusionTalker to address the above limitations via personalizer-guided distillation. In terms of personalization, we introduce a contrastive personalizer that learns identity and emotion embeddings to capture speaking styles from audio. We further propose a personalizer enhancer during distillation to enhance the influence of embeddings on facial animation. For efficiency, we use iterative distillation to reduce the steps required for animation generation and achieve more than 8x speedup in inference. To achieve compactness, we distill the large teacher model into a smaller student model, reducing our model’s storage by 86.4% while minimizing performance loss. After distillation, users can derive their identity and emotion embeddings from audio to quickly create personal-ized animations that reflect specific speaking styles. Extensive experiments are conducted to demonstrate that our method outperforms state-of-the-art methods. The code is released at: https://github.com/ChenVoid/DiffusionTalker.

This work presents Dual-Stick, a novel controller with two sticks connected at the end that innovates a Dual-Ray interaction paradigm to enrich raycasting input in Virtual Reality (VR). Dual-Stick leverages the inherent human dexterity in using everyday tools such as clamps and tweezers to adjust the relative angle between two sticks. This design supports Dual-Ray interactions that provide with a heuristics-based enhanced mechanism. It also offers more flexible manipulation by taking advantages of additional degrees of freedom provided by clamping angle. We conducted two studies to evaluate the effectiveness of Dual-Ray in target selection and manipulation tasks. The results indicated that Dual-Ray significantly improved efficiency in target selection compared to single-ray input but did not outperform the enhanced single-ray technique. In terms of manipulation, Dual-Ray effectively reduced completion time and mode switching compared to single-ray input.

Automatic and precise medical image segmentation (MIS) is of vital importance for clinical diagnosis and analysis. Current MIS methods mainly rely on the convolutional neural network (CNN) or self-attention mechanism (Transformer) for feature modeling. However, CNN-based methods suffer from the inaccurate localization owing to the limited global dependency while Transformer-based methods always present the coarse boundary for the lack of local emphasis. Although some CNN-Transformer hybrid methods are designed to synthesize the complementary local and global information for better performance, the combination of CNN and Transformer introduces numerous parameters and increases the computation cost. To this end, this paper proposes a CNN-Transformer rectified collaborative learning (CTRCL) framework to learn stronger CNN-based and Transformer-based models for MIS tasks via the bi-directional knowledge transfer between them. Specifically, we propose a rectified logit-wise collaborative learning (RLCL) strategy which introduces the ground truth to adaptively select and rectify the wrong regions in student soft labels for accurate knowledge transfer in the logit space. We also propose a class-aware feature-wise collaborative learning (CFCL) strategy to achieve effective knowledge transfer between CNN-based and Transformer-based models in the feature space by granting their intermediate features the similar capability of category perception. Extensive experiments on three popular MIS benchmarks demonstrate that our CTRCL outperforms most state-of-the-art collaborative learning methods under different evaluation metrics. The source code will be publicly available athttps://github.com/LanhooNg/CTRCL.

Multi-Modal Image Fusion (MMIF) aims to integrate complementary image information from different modalities to produce informative images. Previous deep learning-based MMIF methods generally adopt Convolutional Neural Networks (CNNs) or Transformers for feature extraction. However, these methods deliver unsatisfactory performances due to the limited receptive field of CNNs and the high computational cost of Transformers. Recently, Mamba has demonstrated a powerful potential for modeling long-range dependencies with linear complexity, providing a promising solution to MMIF. Unfortunately, Mamba lacks full spatial and frequency perceptions, which are very important for MMIF. Moreover, employing Image Reconstruction (IR) as an auxiliary task has been proven beneficial for MMIF. However, a primary challenge is how to leverage IR efficiently and effectively. To address the above issues, we propose a novel framework named Spatial-Frequency Enhanced Mamba Fusion (SFMFusion) for MMIF. More specifically, we first propose a three-branch structure to couple MMIF and IR, which can retain complete contents from source images. Then, we propose the Spatial-Frequency Enhanced Mamba Block (SFMB), which can enhance Mamba in both spatial and frequency domains for comprehensive feature extraction. Finally, we propose the Dynamic Fusion Mamba Block (DFMB), which can be deployed across different branches for dynamic feature fusion. Extensive experiments show that our method achieves better results than most state-of-the-art methods on six MMIF datasets. The source code is available at https://github.com/SunHui1216/SFMFusion.

Parkinson's Disease (PD) is one of the most critical neurodegenerative diseases, yet there is no cure for it, and the state-of-the-art treatment is to slow its progression. Thus, the earlier a patient with PD is recognized, the better he can be treated. Our project joins the research effort that aims to support early PD diagnosis by designing a Virtual Reality (VR)-based system to monitor pupil diameter patterns as new biomarkers (e.g., Pupil Light Reflex and Task-evoked Pupil Response) and provide early warning of potential PD onset. A follow-up experiment with 55 participants shows that the accuracy of recognizing early PD from healthy controls could reach 0.8942. Our study shows early results of a promising research direction that leverages VR-based technology to non-intrusively recognize patterns and provide alerts to early PD patients who would otherwise not know their symptoms until much later.

This paper presents TacTex, a textile-based interface that provides high-resolution haptic feedback and touch-tracking capabilities. TacTex utilizes electrotactile stimulation, which has traditionally posed challenges due to limitations in textile electrode density and quantity. TacTex overcomes these challenges by employing a multi-layer woven structure that separates conductive weft and warp electrodes with non-conductive yarns. The driving system for TacTex includes a power supply, sensing board, and switch boards to enable spatial and temporal control of electrical stimuli on the textile, while simultaneously monitoring voltage changes. TacTex can stimulate a wide range of haptic effects, including static and dynamic patterns and different sensation qualities, with a resolution of 512 × 512 and based on linear electrodes spaced as closely as 2mm. We evaluate the performance of the interface with user studies and demonstrate the potential applications of TacTex interfaces in everyday textiles for adding haptic feedback.

Multi-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.

Sketching in Virtual Reality (VR) is challenging mainly due to the absence of physical surface support and virtual depth perception cues, which induce high cognitive and sensorimotor load. This paper presents WieldingCanvas, an interactive VR sketching platform that integrates canvas manipulations to draw lines and curves in 3D. Informed by real-life examples of two-handed creative activities, WieldingCanvas interprets users’ spatial gestures to move, swing, rotate, transform, or fold a virtual canvas, whereby users simply draw primitive strokes on the canvas, which are turned into finer and more sophisticated shapes via the manipulation of the canvas. We evaluated the capability and user experience of WieldingCanvas with two studies where participants were asked to sketch target shapes. A set of freehand sketches of high aesthetic qualities were created, and the results demonstrated that WieldingCanvas can assist users with creating 3D sketches.

Writing 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.

Virtual reality (VR) is found to present significant cognitive challenges due to its immersive nature and frequent sensory conflicts. This study systematically investigates the impact of sensory conflicts induced by VR remapping techniques on cognitive fatigue, and unveils their correlation. We utilized three remapping methods (haptic repositioning, head-turning redirection, and giant resizing) to create different types of sensory conflicts, and measured perceptual thresholds to induce various intensities of the conflicts. Through experiments involving cognitive tasks along with subjective and physiological measures, we found that all three remapping methods influenced the onset and severity of cognitive fatigue, with visual-vestibular conflict having the greatest impact. Interestingly, visual-experiential/memory conflict showed a mitigating effect on cognitive fatigue, emphasizing the role of novel sensory experiences. This study contributes to a deeper understanding of cognitive fatigue under sensory conflicts and provides insights for designing VR experiences that align better with human perceptual and cognitive capabilities.

Typing 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.

Target selection is a crucial task in augmented reality (AR). Recent evidence suggests that user motion can significantly influence target selection. However, no systematic research has been conducted on target selection within varied intensity user motions and AR settings. This paper was carried out to investigate the effects of four user motions (i.e., standing, walking, running, and jumping) and two viewing modes (i.e., viewpoint-dependent and viewpoint-independent) on user performance of target selection in AR. Two typical selection techniques (i.e., virtual hand and ray-casting) were utilized for short-range and long-range selection tasks, respectively. Our results indicate that the target selection performance decreased as the intensity of user motion increased, and users demonstrated better performance in the viewpoint-independent mode than in the viewpoint-dependent mode. We also observed that users took a longer amount of time to select targets when using the ray-casting technique than the virtual hand technique. We conclude with a set of design guidelines to improve the AR target selection performance of users while in motion.

The global trend of population aging poses significant challenges to society and healthcare systems, particularly because of neurocognitive disorders (NCDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD). In this context, artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of NCDs. Multimodal contactless screening technologies, such as speech-language processing, computer vision, and virtual reality, offer efficient and convenient methods for disease diagnosis and progression tracking. This paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms, feature extraction, and modeling approaches. Additionally, the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are explored. By providing a comprehensive summary and refinement of the extant theories, methodologies, and applications, this study aims to facilitate an in-depth understanding of these technologies for researchers, both within and outside the field. To the best of our knowledge, this is the first survey to cover the use of speech-language processing, computer vision, and virtual reality technologies for the detection of NSDs.

Challenge 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.

This 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.

Using VR in reclining & lying positions is getting common for users, but upward views caused by posture have to be redirected to be parallel to the ground as when users are standing. This affects users’ locomotion performances in VR due to potential physical restrictions, and the visual-vestibular-proprioceptive conflict. This paper is among the first to investigate the suited locomotion methods and how reclining & lying positions and redirection affect them in such conditions. A user-elicitation study was carried out to construct a set of locomotion methods based on users’ preferences when they were in different reclining & lying positions. A second study developed user-preferred ’tapping’ and ’chair rotating’ gestures, by evaluating their performances at various body reclining angles, we measured the general impacts of posture and redirection. The results showed that these methods worked effectively, but exposed some shortcomings, and users performed worst at 45° reclining angles. Finally, four upgraded methods were designed and verified to improve the locomotion performances.

As touch interactions become ubiquitous in the field of human computer interactions, it is critical to enrich haptic feedback to improve efficiency, accuracy, and immersive experiences. This paper presents HapTag, a thin and flexible actuator to support the integration of push button tactile renderings to daily soft surfaces. Specifically, HapTag works under the principle of hydraulically amplified electroactive actuator (HASEL) while being optimized by embedding a pressure sensing layer, and being activated with a dedicated voltage appliance in response to users’ input actions, resulting in fast response time, controllable and expressive push-button tactile rendering capabilities. HapTag is in a compact formfactor and can be attached, integrated, or embedded on various soft surfaces like cloth, leather, and rubber. Three common push button tactile patterns were adopted and implemented with HapTag. We validated the feasibility and expressiveness of HapTag by demonstrating a series of innovative applications under different circumstances.

When users use Virtual Reality (VR) in nontraditional postures, such as while reclining or lying in relaxed positions, their views lean upwards and need to be corrected, to make sure they see upright contents and perceive the interactions as if they were standing. Such upright redirection is excepted to cause visual-vestibular-proprioceptive conflict, affecting users’ internal perceptions (e.g., body ownership, presence, simulator sickness) and external perceptions (e.g., egocentric space perception) in VR. Different body reclining angles may affect vestibular sensitivity and lead to the dynamic weighting of multi-sensory signals in the sensory integration. In the paper, we investigated the impact of upright redirection on users’ perceptions, with users’ physical bodies tilted at various angles backward and views upright redirected accordingly. The results showed that upright redirection led to simulator sickness, confused self-awareness, weak upright illusion, and increased space perception deviations to various extents when users are at different reclining positions, and the situations were the worst at the 45° conditions. Based on these results, we designed some illusion-based and sensory-based methods, that were shown effective in reducing the impact of sensory conflict through preliminary evaluations.

Interacting with out of reach or occluded VR objects can be cumbersome. Although users can change their position and orientation, such as via teleporting, to help observe and select, doing so frequently may cause loss of spatial orientation or motion sickness. We present vMirror, an interactive widget leveraging reflection of mirrors to observe and select distant or occluded objects. We first designed interaction techniques for placing mirrors and interacting with objects through mirrors. We then conducted a formative study to explore a semi-automated mirror placement method with manual adjustments. Next, we conducted a target-selection experiment to measure the effect of the mirror’s orientation on users’ performance. Results showed that vMirror can be as efficient as direct target selection for most mirror orientations. We further compared vMirror with teleport technique in a virtual treasure hunt game and measured participants’ task performance and subjective experiences. Finally, we discuss vMirorr user experience and present future directions.

In this paper, we propose a reconfigurable electrode, RElectrode, using a microfluidic technique that can change the geometry and material properties of the electrode to satisfy the needs for sensing a variety of different types of user input through touch/touchless gestures, pressure, temperature, and distinguish between different types of objects or liquids. Unlike the existing approaches, which depend on the specific-shaped electrode for particular sensing (e.g., coil for inductive sensing), RElectrode enables capacity, inductance, resistance/pressure, temperature, pH sensings all in a single package. We demonstrate the design and fabrication of the microfluidic structure of our RElectrode, evaluate its sensing performance through several studies, and provide some unique applications. RElectrode demonstrates technical feasibility and application values of integrating physical and biochemical properties of microfluidics into novel sensing interfaces.

Task-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.

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system (“Brilliant Doctor”) and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as “a doctor’s AI assistant” to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement and sound data, respectively. TeethTap uses a support vector machine to classify gestures from noise by fusing acoustic and motion data, and implements K-Nearest-Neighbor (KNN) with a Dynamic Time Warping (DTW) distance measurement using motion data for gesture classification. A user study with 11 participants demonstrated that TeethTap could recognize 13 gestures with a real-time classification accuracy of 90.9% in a laboratory environment. We further uncovered the accuracy differences on different teeth gestures when having sensors on single vs. both sides. Moreover, we explored the activation gesture under real-world environments, including eating, speaking, walking and jumping. Based on our findings, we further discussed potential applications and practical challenges of integrating TeethTap into future devices.

ThumbTrak is a novel wearable input device that recognizes 12 micro-finger poses in real-time. Poses are characterized by the thumb touching each of the 12 phalanges on the hand. It uses a thumb-ring, built with a flexible printed circuit board, which hosts nine proximity sensors. Each sensor measures the distance from the thumb to various parts of the palm or other fingers. ThumbTrak uses a support-vector-machine (SVM) model to classify finger poses based on distance measurements in real-time. A user study with ten participants showed that ThumbTrak could recognize 12 micro finger poses with an average accuracy of 93.6%. We also discuss potential opportunities and challenges in applying ThumbTrak in real-world applications.

Modeling error rates in moving target selection is critical in guiding the design and improving user performance in the user interface with dynamic contents. Despite the high accuracy of existing models in predicting error rates for various inputs, they need to fit a large number of samples for a specific interaction scenario. This paper presents a scenario adaptive model that can quickly learn the characterizes of endpoints from small data in a specific scenario, and then accurately predict the error rates in this scenario. We report on two studies evaluating the adaptability of the model in specific users (children and the older user) and pointing postures (walking and one-handed) with a smartphone. Significant improvements in error rates prediction of our model were observed compared to the previous non-adaptive one. We conclude with the findings from our results for insights into future interface design.

In this paper, we present HoloBoard, an interactive large-format pseduo-holographic display system for lecture based classes. With its unique properties of immersive visual display and transparent screen, we designed and implemented a rich set of novel interaction techniques like immersive presentation, role-play, and lecturing behind the scene that are potentially valuable for lecturing in class. We conducted a controlled experimental study to compare a HoloBoard class with a normal class through measuring students’ learning outcomes and three dimensions of engagement (i.e., behavioral, emotional, and cognitive engagement). We used pre-/post- knowledge tests and multimodal learning analytics to measure students’ learning outcomes and learning experiences. Results indicated that the lecture-based class utilizing HoloBoard lead to slightly better learning outcomes and a significantly higher level of student engagement. Given the results, we discussed the impact of HoloBoard as an immersive media in the classroom setting and suggest several design implications for deploying HoloBoard in immersive teaching practices.

In this paper, we present ArmMenu, a command input approach for distant displays. ArmMenu has a circular interface like pie menus and menu selection is performed by proprioception-based lateral arm movements. We implemented ArmMenu with an off-the-shelf body tracking device (Kinect) and conducted two experiments to validate its efficacy. In the first experiment, we explored the design space of ArmMenu by varying the number of menu items, with exposed or hidden menu modes. Users can operate up to 8-item menus with high selection accuracy (>98%). ArmMenu was fast and accurate even with the hidden menu mode. The second experiment compared the performance of ArmMenu and touchless marking menus. While having similar selection accuracy, ArmMenu was faster and more preferable by users. Our studies consequently demonstrate ArmMenu's effectiveness for command input on distant displays.

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

Modeling 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 μ and σ 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.

On-skin haptic interfaces using soft elastomers which are thin and flexible have significantly improved in recent years. Many are focused on vibrotactile feedback that requires complicated parameter tuning. Another approach is based on mechanical forces created via piezoelectric devices and other methods for non-vibratory haptic sensations like stretching, twisting. These are often bulky with electronic components and associated drivers are complicated with limited control of timing and precision. This paper proposes HapBead, a new on-skin haptic interface that is capable of rendering vibration like tactile feedback using microfluidics. HapBead leverages a microfluidic channel to precisely and agilely oscillate a small bead via liquid flow, which then generates various motion patterns in channel that creates highly tunable haptic sensations on skin. We developed a proof-of-concept design to implement thin, flexible and easily affordable HapBead platform, and verified its haptic rendering capabilities via attaching it to users' fingertips. A study was carried out and confirmed that participants could accurately tell six different haptic patterns rendered by HapBead. HapBead enables new wearable display applications with multiple integrated functionalities such as on-skin haptic doodles, visuo-haptic displays and haptic illusions.

Providing users with rich sensations is beneficial to enhance their immersion in Virtual Reality (VR) environments. Wetness is one such imperative sensation that affects users' sense of comfort and helps users adjust grip force when interacting with objects. Researchers have recently begun to explore ways to create wetness illusions, primarily on a user's face or body skin. In this work, we extended this line of research by creating wetness illusion on users' fingertips. We first conducted a user study to understand the effect of thermal and tactile feedback on users' perceived wetness sensation. Informed by the findings, we designed and evaluated a prototype---Mouillé---that provides various levels of wetness illusions on fingertips for both hard and soft items when users squeeze, lift, or scratch it. Study results indicated that users were able to feel wetness with different levels of temperature changes and they were able to distinguish three levels of wetness for simulated VR objects. We further presented applications that simulated an ice cube, an iced cola bottle, and a wet sponge, etc, to demonstrate its use in VR.

The 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.

Recent 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.

Entering commands on touchscreens can be noisy, but existing interfaces commonly adopt deterministic principles for deciding targets and often result in errors. Building on prior research of using Bayes' theorem to handle uncertainty in input, this paper formalized Bayes' theorem as a generic guiding principle for deciding targets in command input (referred to as "BayesianCommand"), developed three models for estimating prior and likelihood probabilities, and carried out experiments to demonstrate the effectiveness of this formalization. More specifically, we applied BayesianCommand to improve the input accuracy of (1) point-and-click and (2) word-gesture command input. Our evaluation showed that applying BayesianCommand reduced errors compared to using deterministic principles (by over 26.9% for point-and-click and by 39.9% for word-gesture command input) or applying the principle partially (by over 28.0% and 24.5%).

Haptic simulation of hand tools like wrenches, pliers, scissors and syringes are beneficial for finely detailed skill training in VR, but designing for numerous hand tools usually requires an expert-level knowledge of specific mechanism and protocol. This paper presents HapLinkage, a prototyping framework based on linkage mechanism, that provides typical motion templates and haptic renderers to facilitate proxy design of virtual hand tools. The mechanical structures can be easily modified, for example, to scale the size, or to change the range of motion by selectively changing linkage lengths. Resistant, stop, release, and restoration force feedback are generated by an actuating module as part of the structure. Additional vibration feedback can be generated with a linear actuator. HapLinkage enables easy and quick prototypting of hand tools for diverse VR scenarios, that embody both of their kinetic and haptic properties. Based on interviews with expert designers, it was confirmed that HapLinkage is expressive in designing haptic proxy of hand tools to enhance VR experiences. It also identified potentials and future development of the framework.

Second language (L2) pronunciation training has been a worldwide task. Although computer technology makes it possible to develop a talking head to teach pronunciation like a real language teacher, little is known about how a talking head may act on L2 learners’ emotional and cognitive learning process. We investigate L2 learners’ achievement emotions, cognitive load, and pronunciation learning performance in a computer-assisted pronunciation training (CAPT) system embedded with four conditions: audio only (AU), a human face (HF), a 3D talking head with front view (3Df), and a 3D talking head with both front and profile views (3D). Results showed that, with learning time went on, participants’ perceived anxiety, boredom, and pride increased while shame and hopelessness decreased and enjoyment kept stable. With 3D, participants’ anxiety increased the most and boredom increased the least. Moreover, 3D group also perceived the highest germane load and got the highest pronunciation learning performance. Furthermore, anxiety and shame correlated with learning performance positively while boredom correlated with it negatively; enjoyment and pride correlated positively with performance on Mandarin tones. These findings significantly contribute to the efforts to design or select virtual characters for computer-aided language learning (CALL) and also provide a valuable reference to study achievement emotions in HCI systems.

Parkinson's disease (PD) is a chronic neurological disorder causing progressive disability that severely affects patients' quality of life. Although early interventions can provide significant benefits, PD diagnosis is often delayed due to both the mildness of early signs and the high requirements imposed by traditional screening and diagnosis methods. In this paper, we explore the feasibility and accuracy of detecting motor impairment in early PD via sensing and analyzing users' common touch gestural interactions on smartphones. We investigate four types of common gestures, including flick, drag, pinch, and handwriting gestures, and propose a set of features to capture PD motor signs. Through a 102-subject (35 early PD subjects and 67 age-matched controls) study, our approach achieved an AUC of 0.95 and 0.89/0.88 sensitivity/specificity in discriminating early PD subjects from healthy controls. Our work constitutes an important step towards unobtrusive, implicit, and convenient early PD detection from routine smartphone interactions.

Intensive exploration and navigation of hierarchical lists on smartphones can be tedious and time-consuming as it often requires users to frequently switch between multiple views. To overcome this limitation, we present PinchList, a novel interaction design that leverages pinch gestures to support seamless exploration of multi-level list items in hierarchical views. With PinchList, sub-lists are accessed with a pinch-out gesture whereas a pinch-in gesture navigates back to the previous level. Additionally, pinch and flick gestures are used to navigate lists consisting of more than two levels. We conduct a user study to refine the design parameters of PinchList such as a suitable item size, and quantitatively evaluate the target acquisition performance using pinch-in/out gestures in both scrolling and non-scrolling conditions. In a second study, we compare the performance of PinchList in a hierarchal navigation task with two commonly used touch interfaces for list browsing: pagination and expand-and-collapse interfaces. The results reveal that PinchList is significantly faster than other two interfaces in accessing items located in hierarchical list views. Finally, we demonstrate that PinchList enables a host of novel applications in list-based interaction?

Instant 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.

This paper presents the first investigation into using the goal-crossing paradigm for object selection with virtual reality (VR) head-mounted displays. Two experiments were carried out to evaluate ray-casting crossing tasks with target discs in 3D space and goal lines on 2D plane respectively in comparison to ray-casting pointing tasks. Five factors, i.e. task difficulty, the direction of movement constraint (collinear vs. orthogonal), the nature of the task (discrete vs. continuous), field of view of VR devices and target depth, were considered in both experiments. Our findings are: (1) crossing generally had shorter or no longer time, and higher or similar accuracy than pointing, indicating crossing can complement or substitute pointing; (2) crossing tasks can be well modelled with Fitts' Law; (3) crossing performance depended on target depth; (4) crossing target discs in 3D space differed from crossing goal lines on 2D plane in many aspects such as time and error performance, the effects of target depth and the parameters of Fitts' models. Based on these findings, we formulate a number of design recommendations for crossing-based interaction in VR.

Understanding 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 up to 4% improvement in pointing speed and 41% in pointing accuracy compared with two state-of-the-art selection technologies. In addition, when we tested our model to predict pointing errors in a realistic user interface, we observed high fit of 0.94 R2.

The degree and quality of instructor-student interactions are crucial for students' engagement, retention, and learning outcomes. However, such interactions are limited in live online lectures, where instructors no longer have access to important cues such as raised hands or facial expressions at the time of teaching. As a result, instructors cannot fully understand students' learning progresses. This paper presents an explorative study investigating how presenters perceive and react to audience flow prediction when giving live-stream lectures, which has not been examined yet. The study was conducted with an experimental system that can predict audience's psychological states (e.g., anxiety, flow, boredom) through real-time facial expression analysis, and can provide aggregated views illustrating the flow experience of the whole group. Through evaluation with 8 online lectures (N_instructors=8, N_learners=21), we found such real-time flow prediction and visualization can provide value to presenters. This paper contributes a set of useful findings regarding their perception and reaction of such flow prediction, as well as lessons learned in the study, which can be inspirational for building future AI-powered system to assist people in delivering live online presentations.

With the development of virtual reality (VR) and human-computer interaction technology, how to use natural and efficient interaction methods in the virtual environment has become a hot topic of research. Gesture is one of the most important communication methods of human beings, which can effectively express users’ demands. In the past few decades, gesture-based interaction has made significant progress. This article focuses on the gesture interaction technology and discusses the definition and classification of gestures, input devices for gesture interaction, and gesture interaction recognition technology. The application of gesture interaction technology in virtual reality is studied, the existing problems in the current gesture interaction are summarized, and the future development is prospected.

Background Owing to recent advances in virtual reality (VR) technologies, effective user interaction with dynamic content in 3D scenes has become a research hotspot. Moving target selection is a basic interactive task in which the user performance research in tasks is significant to user interface design in VR. Different from the existing static target selection studies, the moving target selection in VR is affected by the change in target speed, angle and size, and lack of research on some key factors. Methods This study designs an experimental scenario in which the users play badminton under the condition of VR. By adding seven kinds of modal clues such as vision, audio, haptics, and their combinations, five kinds of moving speed and four kinds of serving angles, and the effect of these factors on the performance and subjective feelings in moving target selection in VR, is studied. Results The results show that the moving speed of the shuttlecock has a significant impact on the user performance. The angle of service has a significant impact on hitting rate, but has no significant impact on the hitting distance. The acquisition of the user performance by the moving target is mainly influenced by vision under the combined modalities; adding additional modalities can improve user performance. Although the hitting distance of the target is increased in the trimodal condition, the hitting rate decreases. Conclusion This study analyses the results of user performance and subjective perception, and then provides suggestions on the combination of modality clues in different scenarios.

Background Crossing-based target selection motion may attain less error rates and higher interactive speed in some cases. Most of the research in target selection fields are focused on the analysis of the interaction results. Additionally, as trajectories play a much more important role in crossing-based target selection compared to the other interactive techniques, an ideal model for trajectories can help computer designers make predictions about interaction results during the process of target selection rather than at the end of the whole process. Methods In this paper, a trajectory prediction model for crossing-based target selection tasks is proposed by taking the reference of a dynamic model theory. Results Simulation results demonstrate that our model performed well with regard to the prediction of trajectories, endpoints and hitting time for target-selection motion, and the average error of trajectories, endpoints and hitting time values were found to be 17.28%, 2.73mm and 11.50%, respectively.

Many studies conducted in non-virtual activities have shown that flow significantly influences performance, yet studies in virtual activities often reveal only a weak association. This paper begins by building a theoretical explanatory model, and then conducts 3 empirical studies to explore this question. Study 1 exams the mechanism of weak association in two virtual activities. Study 2 tests the effectiveness of a potential approach to strengthen this association. In Study 3 we applied our proposed model and design approach to optimize a VR tennis game. Results show that the influence of flow on performance was not significant in those virtual activities where the primary task and the operation of interactive artifacts were less congruent such that the artifacts can lead to flow experience that is independently of the primary task. Our research offers a theoretical and empirical basis on how to optimize virtual environment design and maximize positive effect of the flow experience.

In 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).

Pointing and gesturing with the dominant thumb (DT) and the non-dominant thumb (NT) are two common tasks for bimanual tablet gripping interaction. Understanding the differences between DT and NT is important to pointing and gesturing based interface design on tablets, but is overlooked by previous studies. We therefore conducted two experiments. In the first experiment, participants carried out pointing tasks with DT and NT, respectively, on a tablet. We found DT and NT input differed in pointing time (DT had average shorter time than NT) and required target sizes for fast and accurate pointing (7.7 mm and 9.4 mm in diameter for DT and NT). On the other hand, DT and NT were alike in pointing accuracy. They performed about the same for target size larger than 9.6 mm and distance shorter than 21 mm. Both DT and NT pointing can be modeled by Fitts’ law. In the second experiment, participants performed gesturing tasks with DT and NT respectively on a tablet. The collected data were analyzed using a set of gesture features. Results showed DT and NT were different in features such as articulation time, size ratio and indicative angle difference, but similar in features like aperture, axial symmetry and shape distance. The differences of time and accuracy between DT and NT depended on gesture complexity but not gesture sizes. We discuss these findings with implications for future bimanual thumb interaction design and research.

Studies of the human brain network are becoming increasingly popular in the fields of neuroscience, computer science, and neurology. Despite this rapidly growing line of research, gaps remain on the intersection of data analytics, interactive visual representation, and the human intelligence—all needed to advance our understanding of human brain networks. This article tackles this challenge by exploring the design space of visual analytics. We propose an integrated framework to orchestrate computational models with comprehensive data visualizations on the human brain network. The framework targets two fundamental tasks: the visual exploration of multi-label brain networks and the visual comparison among brain networks across different subject groups. During the first task, we propose a novel interactive user interface to visualize sets of labeled brain networks; in our second task, we introduce sparse regression models to select discriminative features from the brain network to facilitate the comparison. Through user studies and quantitative experiments, both methods are shown to greatly improve the visual comparison performance. Finally, real-world case studies with domain experts demonstrate the utility and effectiveness of our framework to analyze reconstructions of human brain connectivity maps. The perceptually optimized visualization design and the feature selection model calibration are shown to be the key to our significant findings.

While instrumental ensemble playing can benefit children's music education and collaboration skill development, it requires extensive training on music and instruments, which many school children lack. To help children with limited music training experience instrumental ensemble playing, we created EnseWing, an interactive system that offers such an experience. In this paper, we report the design of the EnseWing experience and a two-month field study. Our results show that EnseWing preserves the music and ensemble skills from traditional instrumental ensemble and provides more collaboration opportunities for children.

Body-based motion gestures have been gaining popularity in designing interactive systems. However, theories and design guidelines on the use of body movement in design have not been fully evaluated. This article investigates human ability to perform discrete target selection tasks using stretching action through two controlled experiments. The experimental results indicate that: (1) the range of the discrete levels of depth that users can easily discriminate with arm stretching is up to 16 with full visual feedback, but is down to 4 without the feedback; (2) dwelling, a gesture with keeping a hand motionless and the cursor within a target area for a certain amount of time, may be the best gesture for confirmation command; (3) full visual feedback can improve the user performance; and (4) arm stretching action can be modeled using Fitts’ law. We also discuss the design potentials for Stretch Widgets based on these results.

Automatic motion estimation of multiple depth cameras has remained a challenging topic in computer vision due to its reliance on the image correspondence problem. In this paper, spherical objects are employed to estimate motion parameters between multiple depth cameras. We move a sphere several times in the common view of depth cameras. We fit the spherical point clouds to get the sphere centers in each depth camera system, and then introduce a factorization based approach to estimate motions between the depth cameras. Both simulated and real experiments show the robustness and effectiveness of our method.

This article experimentally investigates user performances with various surface textures in steering and gesture input tasks. Results reveal that (a) low friction material makes users spend more time on each task, and (b) although low friction material benefits the smoothness of trajectory, it causes more trajectory errors, and (c) users apply less force or pressure with slippery materials during the tasks. These findings are the more significant because they demonstrate that the common glass surface of most tablet surfaces is not the best kind of surface for optimum accuracy or for user satisfaction. The results suggest that users should be free to change the surface texture of the device in order to get natural and realistic haptic feedback according to different tasks and personal preferences.

This study examined the performance of two scrolling techniques (flick and ring) for document navigation in touch-based mobile phones using three input methods (index finger, pen, and thumb), with specific consideration given to two postures: sitting and walking. The findings are as follows: (a) in both sitting and walking postures, for the three input methods, flick resulted in shorter movement time and fewer crossings than ring, suggesting flick is superior to ring for document navigation; (b) for sitting posture, regarding pen and thumb input, ring led to shorter movement time than flick for large target distances, indicating ring has a potential interaction advantage; (c) regarding sitting and walking postures, both flick and ring document scrolling in touch-based mobile phones can be modeled by the Anderson model (Andersen, 2005). Designers of future scrolling techniques should consider these differences, as well as exploit the advantages and avoid the disadvantages of ring and flick scrolling.

In China, hundreds of millions of migrant workers have moved to cities or coastal regions for more or better-paid jobs and have left their children behind at their rural homes. Separated by thousands of kilometers, these "left-behind" children and their migrant parents use mobile phones as their primary - and often only - method of maintaining family connections. To better understand the use of technology in this long-distance communication, we conducted a multi-phased study using interviews and surveys in three different Chinese rural areas. In this paper, we report our findings on how these children communicate with their migrant parents and what information they exchange. We also discuss design implications derived from these findings that may improve communication between left-behind children and their parents.

Semi-Markov conditional random fields (semi-CRFs) are usually trained with maximum a posteriori (MAP) criterion which adopts the 0/1 cost for measuring the loss of misclassification. In this paper, based on our previous work on handwritten Chinese/Japanese text recognition (HCTR) using semi-CRFs, we propose an alternative parameter learning method by minimizing the risk, in which the misclassification costs are not equal, but different depending on the hypothesis and the ground-truth. The proposed method is lattice-based, i.e., the hypothesis space is the entire lattice on which the semi-CRF is defined. Experimental results on two online handwriting databases: CASIA-OLHWDB and TUAT Kondate demonstrate that minimum-risk training can yield superior string recognition rates compared to MAP training.

This paper proposes a method for handwritten Chinese/Japanese text (character string) recognition based on semi-Markov conditional random fields (semi-CRFs). The high-order semi-CRF model is defined on a lattice containing all possible segmentation-recognition hypotheses of a string to elegantly fuse the scores of candidate character recognition and the compatibilities of geometric and linguistic contexts by representing them in the feature functions. Based on given models of character recognition and compatibilities, the fusion parameters are optimized by minimizing the negative log-likelihood loss with a margin term on a training string sample set. A forward-backward lattice pruning algorithm is proposed to reduce the computation in training when trigram language models are used, and beam search techniques are investigated to accelerate the decoding speed. We evaluate the performance of the proposed method on unconstrained online handwritten text lines of three databases. On the test sets of databases CASIA-OLHWDB (Chinese) and TUAT Kondate (Japanese), the character level correct rates are 95.20 and 95.44 percent, and the accurate rates are 94.54 and 94.55 percent, respectively. On the test set (online handwritten texts) of ICDAR 2011 Chinese handwriting recognition competition, the proposed method outperforms the best system in competition.

When designers explain their early design ideas to others, they usually use face-to-face communication along with sketches. In practice, however, sometimes face-to-face meetings are not possible, and designers have to rely on asynchronous communication. Important contextual information that is available in face-to-face meetings often becomes missing in such asynchronous communications, which can lead to confusion and misunderstanding. To address this challenge, we present SketchComm: an enhanced tool to support rich and flexible asynchronous communication of early design ideas. The key of the system is to allow designers to capture and communicate important contextual information to the audience in addition to sketches. A user study with designers and audience demonstrated effectiveness of asynchronous early design communication using SketchComm.

Gesture inputs on multi-touch tabletops usually involve multiple fingers (more than two) and casual touchdowns or liftoffs of fingers. This flexibility of touch gestures allows more natural user interaction, but also poses new challenges for accurate recognition of multi-touch gestures. To address these challenges, we propose a new approach to recognize flexible multi-touch stroke gestures on tabletops. Based on a user study on multi-touch unistroke gestures, we develop a gesture recognition method by extracting key strokes embedded in flexible multi-touch input. Our evaluation study result shows that this method can greatly improve the recognition accuracy of flexible multi-touch unistroke gestures on tabletops.

With the fast economic growth and urbanization of many developing countries come concerns that their children now have fewer opportunities to express creativity and develop collaboration skills, or to experience their local cultural heritage. We propose to address these concerns by creating technologies inspired by traditional arts, and allowing children to create and collaborate through playing with them. ShadowStory is our first attempt in this direction, a digital storytelling system inspired by traditional Chinese shadow puppetry. We present the design and implementation of ShadowStory and a 7-day field trial in a primary school. Findings illustrated that ShadowStory promoted creativity, collaboration, and intimacy with traditional culture among children, as well as interleaved children's digital and physical playing experience.

In this paper, we describe CoolMag, a tangible interaction tool to enable children to create different instruments collaboratively in music education. With CoolMag, children could learn the basic playing methods of different instruments. It also has the potential to inspire children's creativity, because children could adopt objects in daily life (broom, cup, pen etc.) as the carrier of their novel instruments whose appearance may differ from the traditional one.

Recently, pen tilting has been explored in pen-based user interfaces and has shown potential to improve user interaction in various tasks (e.g., menu selection, modeless object manipulation). However, some basic questions concerning pen tilting behaviors, such as the ideal range, azimuth size, and direction of pen tilting, have not been thoroughly investigated. In this paper, we report our empirical studies on user performances in basic pen tilting tasks. First, we conducted a baseline study, which helps us to determine tilting directions, tilting ranges, and the thresholds that separate incidental pen tilting actions from intentional actions used for interaction. Based on the results from the baseline study, we designed an experiment to investigate user performances in goal tilting in different tilting ranges, azimuth sizes, and directions. Drawing on the results of our data analyses on task completion time, error rate, and pen tip movements, we discussed values of tilting parameters like titling range, minimal azimuth size, and tilting direction.

Concept maps are an important tool to organize, represent, and share knowledge. Building a concept map involves creating text-based concepts and specifying their relationships with line-based links. Current concept map tools usually impose specific task structures for text and link construction, and may increase cognitive burden to generate and interact with concept maps. While pen-based devices (e.g., tablet PCs) offer users more freedom in drawing concept maps with a pen or stylus more naturally, the support for hand-drawn concept map creation and manipulation is still limited, largely due to the lack of methods to recognize the components and structures of hand-drawn concept maps. This article proposes a method to understand hand-drawn concept maps. Our algorithm can extract node blocks, or concept blocks, and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning, recognize the text content of each concept node, and build a concept-map structure by relating concepts and links. We also design an algorithm for concept map retrieval based on hand-drawn queries. With our algorithms, we introduce structure-based intelligent manipulation techniques and ink-based retrieval techniques to support the management and modification of hand-drawn concept maps. Results from our evaluation study show high structure recognition accuracy in real time of our method, and good usability of intelligent manipulation and retrieval techniques.

In many developing countries such as India and China, low educational levels often hinder economic empowerment. In this paper, we argue that mobile learning games can play an important role in the Chinese literacy acquisition process. We report on the unique challenges in the learning Chinese language, especially its logographic writing system. Based on an analysis of 25 traditional Chinese games currently played by children in China, we present the design and implementation of two culturally inspired mobile group learning games, Multimedia Word and Drumming Strokes. These two mobile games are designed to match Chinese children's understanding of everyday games. An informal evaluation reveals that these two games have the potential to enhance the intuitiveness and engagement of traditional games, and children may improve their knowledge of Chinese characters through group learning activities such as controversy, judgments and self-correction during the game play.

Computer-based geometry systems have been widely used for teaching and learning, but largely based on mouse-and-keyboard interaction, these systems usually require users to draw figures by following strict task structures defined by menus, buttons, and mouse and keyboard actions. Pen-based designs offer a more natural way to develop geometry theorem proofs with hand-drawn figures and scripts. This paper describes a pen-based geometry theorem proving system that can effectively recognize hand-drawn figures and hand-written proof scripts, and accurately establish the correspondence between geometric components and proof steps. Our system provides dynamic and intelligent visual assistance to help users understand the process of proving and allows users to manipulate geometric components and proof scripts based on structures rather than strokes. The results from evaluation study show that our system is well perceived and users have high satisfaction with the accuracy of sketch recognition, the effectiveness of visual hints, and the efficiency of structure-based manipulation.

This article presents UCFM, a user-centered developing framework for interactive temporal data visualization. UCFM is comprised mainly by software architecture and software development methods. The software architecture describes modules in interactive temporal data visualization system and their relationships. And based on the software architecture and development practice, the software development methods summarize these specific steps to design and develop interactive temporal data visualization system. To demonstrate UCFM's validity, the development process of an interactive temporal data visualization application is illustrated.

Concept maps are an important tool to knowledge organization, representation, and sharing. Most current concept map tools do not provide full support for hand-drawn concept map creation and manipulation, largely due to the lack of methods to recognize hand-drawn concept maps. This paper proposes a structure recognition method. Our algorithm can extract node blocks and link blocks of a hand-drawn concept map by combining dynamic programming and graph partitioning and then build a concept-map structure by relating extracted nodes and links. We also introduce structure-based intelligent manipulation technique of hand-drawn concept maps. Evaluation shows that our method has high structure recognition accuracy in real time, and the intelligent manipulation technique is efficient and effective.

We present a new technique called 'Tilt Menu' for better extending selection capabilities of pen-based interfaces. The Tilt Menu is implemented by using 3D orientation information of pen devices while performing selection tasks. The Tilt Menu has the potential to aid traditional one-handed techniques as it simultaneously generates the secondary input (e.g., a command or parameter selection) while drawing/interacting with a pen tip without having to use the second hand or another device. We conduct two experiments to explore the performance of the Tilt Menu. In the first experiment, we analyze the effect of parameters of the Tilt Menu, such as the menu size and orientation of the item, on its usability. Results of the first experiment suggest some design guidelines for the Tilt Menu. In the second experiment, the Tilt Menu is compared to two types of techniques while performing connect-the-dot tasks using freeform drawing mechanism. Results of the second experiment show that the Tilt Menu perform better in comparison to the Tool Palette, and is as good as the Toolglass.

Chinese text entry is challenging on mobile devices which rely on keypad input. Entering one character may require many key presses. This paper proposes a multimodal text entry technique for Chinese. In this method, Chinese user can enter Chinese text by simultaneously using the simplified phonemic input method named Jianpin with keypad and speech utterance. The key of the technique is a multimodal fusion algorithm, which synchronizes speech and keypad input and fuses redundant information from two modalities to get the best candidate. A preliminary evaluation shows that users appreciate this technique and it could reduce key presses and enhance the input efficiency.

In order to improve stimulus-response compatibility of touchpad in pen-based user interface, we present the tilt cursor, i.e. a cursor dynamically reshapes itself to providing the 3D orientation cue of pen. We also present two experiments that evaluate the tilt cursor's performance in circular menu selection and specific marking menu selection tasks. Results show that in a specific marking menu selection task, the tilt cursor significantly outperforms the shape-fixed arrow cursor and the live cursor [4]. In addition, results show that by using the tilt cursor, the response latencies for adjusting drawing directions are smaller than that by using the other two kinds of cursors.

In recognition-based user interface, users' satisfaction is determined not only by recognition accuracy but also by effort to correct recognition errors. In this paper, we introduce a crossmodal error correction technique, which allows users to correct errors of Chinese handwriting recognition by speech. The focus of the paper is a multimodal fusion algorithm supporting the crossmodal error correction. By fusing handwriting and speech recognition, the algorithm can correct errors in both character extraction and recognition of handwriting. The experimental result indicates that the algorithm is effective and efficient. Moreover, the evaluation also shows the correction technique can help users to correct errors in handwriting recognition more efficiently than the other two error correction techniques.

Co-CreativePen toolkit is a pen-based 3D toolkit for children cooperatively designing virtual environment. This toolkit is used to construct different applications involved with distributed pen-based 3D interaction. In this toolkit, sketch method is encapsulated as kinds of interaction techniques. Children can use pen to construct 3D and IBR objects, to navigate in the virtual world, to select and manipulate virtual objects, and to communicate with other children. Children can use pen to select other children in the virtual world, and use pen to write message to children selected. The distributed architecture of Co-CreativePen toolkit is based on the CORBA. A common scene graph is managed in the server with several copies of this graph are managed in every client. Every changes of the scene graph in client will cause the change in the server and other client