Adults with intellectual disabilities (ID) face systemic social exclusion that narrows autonomy and life opportunities. While social virtual reality (VR) offers a powerful medium for identity expression and community belonging, research often adopts a remedial paradigm, focusing on training functional skills in scripted environments. This paper challenges this deficit-based model by treating social VR as an open world for participation. Following 11 adults with ID across multi-session engagements with VRChat, we employed an adaptive, relational method to scaffold participant leadership. Findings reveal that participants used the platform for interest-driven discovery, sustained through interdependent care webs. Crucially, the study demonstrates how social VR supports transferable confidence and emerging digital citizenship, enabling some users to transition from novices to community leaders. We contribute six Disability Justice-aligned design principles articulating a world-making paradigm that reorients Human-Computer Interaction toward supporting personhood and self-determination in mainstream digital publics.

Early detection of visual impairments remains a persistent challenge, especially due to the subtle and often unnoticed nature of early-stage symptoms. Recent works have attempted to transition clinical tests to home-based services or develop innovative diagnostic methods, but most approaches remain self-initiated and discrete. In this study, we focused on refractive disorders and explored the feasibility of using electrooculography (EOG) to detect changes in refractive power passively. Thirty-nine participants used optometry trial lenses to simulate different refractive conditions. Participants performed a series of visual tasks while their EOG signals were recorded. We trained classification models to predict simulated refractive power levels relative to baseline visual condition across multiple evaluation settings, including within-subject, temporal generalization, and across-subject scenarios. The findings reveal that refractive power classification models achieve a mean accuracy of $0.950 \pm 0.034$ in within-subject, within-condition scenarios. Within-subject models tested on data from a different time point showed highly variable performance. While some participants achieved promising results, overall accuracy remained low, with a mean of $0.159 \pm 0.285$. We employed three strategies to evaluate the across-subject models. Naive models performed poorly ($0.161 \pm 0.063$) and linear normalization provided limited improvement ($0.175 \pm 0.062$). However, the fine-tuning strategy substantially improved the model's performance ($0.785 \pm 0.123$). EOG signals contain useful information for refractive power classification, particularly in personalized contexts. However, generalizing across time and individuals remains challenging. Overall, this work offers valuable insights for advancing EOG-based systems aimed at passive, real-time monitoring of visual conditions.

Users are often seated in the real environment, while their virtual avatars either remain standing stationary or move in virtual reality (VR). This creates posture inconsistencies between the real and virtual embodiment representations. The relationship between posture consistency in locomotion techniques and sense of presence in VR is still unclear. This study investigates how visual and somatosensory integration affects the sense of standing (SoSt) and the sense of self-motion (SoSm) when the sitting posture is varied slightly, including highlighting the importance of sitting posture for locomotion design in VR. The degree and occurrence of SoSt and SoSm were assessed by subjective experiments, and it was found that higher sitting and lower sitting postures present higher SoSt and lower SoSm, respectively. Invocation of SoSt also influences postural perception. Perception of travel distance varied according to the posture condition when identical visual flow was presented. The findings suggest that visual and somatosensory integration related to posture enhances SoSt and SoSm, and a sitting posture with a higher seating position is recommended in seated VR locomotion design.

Augmented Reality (AR) can enhance accessibility by anchoring virtual windows to the user's body. Among common approaches, head-following windows help maintain floating virtual windows within the user's field of view. Previous studies have actively explored this new design space to improve user experience and efficiency. In contrast, this study focuses on the perceived resistance of head-following windows in AR, despite their lack of physical mass. We conducted a within-subject experiment with 24 participants, manipulating Follow-Up Delay, Window Size, and UI Type. We measured subjective resistance ratings, NASA-TLX (Raw TLX Scores), and the gaze-head angular offset. The results showed that both a certain level of Follow-Up Delay and the Tag-Along elicited significantly stronger perceived resistance as well as task load. Although Window Size alone did not show a significant effect on resistance ratings, we observed an interaction between the size and UI Type. These findings extend existing pseudo-haptics research by revealing the previously unexplored domain of resistance in head-based interactions with head-following virtual windows. We further provide design implications for head-following windows in AR.

Sparse wearable inertial measurement units (IMUs) have gained popularity for estimating 3D human motion. However, challenges such as pose ambiguity, data drift, and limited adaptability to diverse bodies persist. To address these issues, we propose UMotion, an uncertainty-driven, online fusing-all state estimation framework for 3D human shape and pose estimation, supported by six integrated, body-worn ultra-wideband (UWB) distance sensors with IMUs. UWB sensors measure inter-node distances to infer spatial relationships, aiding in resolving pose ambiguities and body shape variations when combined with anthropometric data. Unfortunately, IMUs are prone to drift, and UWB sensors are affected by body occlusions. Consequently, we develop a tightly coupled Unscented Kalman Filter (UKF) framework that fuses uncertainties from sensor data and estimated human motion based on individual body shape. The UKF iteratively refines IMU and UWB measurements by aligning them with uncertain human motion constraints in real-time, producing optimal estimates for each. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of UMotion in stabilizing sensor data and the improvement over state of the art in pose accuracy. Code is available at: https://github.com/kk9six/umotion.

Redirected Walking (RDW) techniques allow users to explore virtually infinite environments within constrained physical spaces. However, achieving precise alignment between physical and virtual targets remains a significant challenge. In particular, when both position and orientation of the targets need to align in order to get a proper haptic feedback like siting on a virtual chair. This paper introduces a revised version of the Reactive Alignment (REA) controller that simultaneously minimizes the Angular and Positional Distance Errors between a physical and a virtual target. The proposed method enhances spatial alignment and optimizes user navigation using a novel rotation gain control algorithm that takes angular misalignment into account. In addition, a new metric,$\Delta p$, is proposed to quantify the angular alignment, complementing the redefined Physical Distance Error (PDE) for positional accuracy. We implemented the algorithm on Oculus Quest head-mounted display and utilized the HMD's physical space tracking to locate the physical prop's location without the need of any external tracking. We also incorporated saccadic redirection by utilizing the HMD's eyetracking functionality to complement the revised REA approach. A user study demonstrates that the revised REA controller outperforms the original REA by reducing Physical Distance Error, angular error, and reset counts. It also enhanced user interaction with physical props by enabling users to successfully sit on a physical chair 60% of the time compared to 0% with the original REA when$\Delta p$is zero.

Awe is a multifaceted emotion often associated with the perception of vastness, that challenges existing mental frameworks. Despite its growing relevance in affective computing and psychological research, awe remains difficult to elicit and measure. This raises the research questions of how awe can be effectively elicited, which factors are associated with the experience of awe, and whether it can reliably be measured using biosensors. For this study, we designed 10 immersive Virtual Reality (VR) scenes with dynamic transitions from narrow to vast environments. These scenes were used to explore how awe relates to environmental features (abstract, human-made, nature), personality traits, and country of origin. We collected skin conductance, respiration, self-reported awe and presence data from participants from Germany, Japan, and Jordan. Our results indicate that self-reported awe varies significantly across countries and scene types. In particular, a scene depicting outer space elicited the strongest awe. Scenes that elicited high selfreported awe also induced a stronger sense of presence. However, we found no evidence that awe ratings are correlated with physiological responses. These findings challenge the assumption that awe is reliably reflected in autonomic arousal and underscore the importance of cultural and perceptual context. Our study offers new insights into how immersive VR can be designed to elicit awe, and suggests that subjective reports - rather than physiological signals - remain the most consistent indicators of emotional impact.

Refractive errors are among the most common visual impairments globally, yet their diagnosis often relies on active user participation and clinical oversight. This study explores a passive method for estimating refractive power using two eye movement recording techniques: electrooculography (EOG) and video-based eye tracking. Using a publicly available dataset recorded under varying diopter conditions, we trained Long Short-Term Memory (LSTM) models to classify refractive power from unimodal (EOG or video-based eye tracking) and multimodal configurations. In the context of eye movement analysis, EOG captures fine-grained electrical signals, while video-based tracking provides rich features such as pupil dynamics and gaze behavior, making the two modalities complementary. We assess performance in both subject-dependent and subject-independent settings to evaluate model personalization and generalizability across individuals. Results show that the multimodal model consistently outperforms unimodal models, achieving the highest average accuracy in both settings: 96.568% in the subject-dependent scenario and 9.344% in the subject-independent scenario. Statistical comparisons in the subject-dependent setting confirmed that both unimodal and multimodal models significantly exceeded the chance level. Among them, the multimodal model significantly outperformed the EOG and eye-tracking models. The strong performance of subject-dependent models highlights the potential for developing personalized models tailored to the target user for refractive power monitoring. However, generalization remains limited, with classification accuracy only marginally above chance in the subject-independent evaluations. Our findings demonstrate both the potential and current limitations of eye movement data-based refractive error estimation, contributing to the development of continuous, non-invasive screening methods using EOG signals and eye-tracking data.

Systems with occlusion capabilities, such as those used in vision augmentation, image processing, and optical see-through head-mounted display (OST-HMD), have gained popularity. Achieving precise (hard-edge) occlusion in these systems is challenging, often requiring complex optical designs and bulky volumes. On the other hand, utilizing a single transparent liquid crystal display (LCD) is a simple approach to create occlusion masks. However, the generated mask will appear defocused (soft-edge) resulting in insufficient blocking or occlusion leakage. In our work, we delve into the perception of soft-edge occlusion by the human visual system and present a preference-based optimal expansion method that minimizes perceived occlusion leakage. In a user study involving 20 participants, we made a noteworthy observation that the human eye perceives a sharper edge blur of the occlusion mask when individuals see through it and gaze at a far distance, in contrast to the camera system's observation. Moreover, our study revealed significant individual differences in the perception of soft-edge masks in human vision when focusing. These differences may lead to varying degrees of demand for mask size among individuals. Our evaluation demonstrates that our method successfully accounts for individual differences and achieves optimal masking effects at arbitrary distances and pupil sizes.

Virtual co-embodiment enables two users to share a single avatar in Virtual Reality (VR). During such experiences, the illusion of shared motion control can break during joint-action activities, highlighting the need for position-aware feedback mechanisms. Drawing on the perceptual crossing paradigm, we explore how haptics can enable non-verbal coordination between co-embodied participants. In a within-subjects study (20 participant pairs), we examined the effects of vibrotactile haptic feedback (None, Present) and avatar control distribution (25-75%, 50-50%, 75-25%) across two VR reaching tasks (Targeted, Free-choice) on participants’ Sense of Agency (SoA), co-presence, body ownership, and motion synchrony. We found (a) lower SoA in the free-choice with haptics than without, (b) higher SoA during the shared targeted task, (c) co-presence and body ownership were significantly higher in the free-choice task, (d) players’ hand motions synchronized more in the targeted task. We provide cautionary considerations when including haptic feedback mechanisms for avatar co-embodiment experiences.

In underwater environments, the reflected ultrasonic waves from objects generally provide more than just information about their color and shape for object recognition. Previous studies have overlooked the influence of object pose on these wave components. It is crucial to investigate how these poses affect the reflected wave components because object poses can vary widely and are often unpredictable in real-world scenarios. In this work, we introduce a novel dataset comprising reflected wave components collected from objects made of various materials and observed from various angles. We also show the preliminary evaluations on the performance of machine learning-based material classification on object pose. Our results indicate that the accuracy is consistently high (≥ 91%) for known angles but significantly drops (< 60%) when dealing with unknown angles in most cases. Based on these evaluations, we suggest several directions for future research. Our dataset is available at https://github.com/Nyamotaro/U2R.

Awe is a complex emotion described as a perception of vastness and a need for accommodation to integrate new, overwhelming experiences. Virtual Reality (VR) has recently gained attention as a convenient means to facilitate experiences of awe. In VR, a first-person perspective might increase awe due to its immersive nature, while a third-person perspective might enhance the perception of vastness. However, the impact of VR perspectives on experiencing awe has not been thoroughly examined. We created two types of VR scenes: one with elements designed to induce high awe, such as a snowy mountain, and a low awe scene without such elements. We compared first-person and third-person perspectives in each scene. Forty-two participants explored the VR scenes, with their physiological responses captured by electrocardiogram (ECG) and face tracking (FT). Subsequently, participants self-reported their experience of awe (AWE-S) and presence (IPQ) within VR. The results revealed that the first-person perspective induced stronger feelings of awe and presence than the third-person perspective. The findings of this study provide useful guidelines for designing VR content that enhances emotional experiences.

Hap’n’Roll is a wearable device that leverages the concept of a scroll to present, with a single motor, tactile sensations of various sizes, shapes, and textures. Hap’n’Roll is composed of two axes, a sheet, and one motor. By changing the number of sheet wraps, the thickness within the user’s hand can be adjusted. Additionally, using holes on the sheet to secure the fingertips, it can present a wide range of sizes and shapes. Unlike typical existing handheld shape-changing devices, Hap’n’Roll is not limited to cylindrical forms. Furthermore, by moving different materials attached on the sheet to the fingertips, it can also express different textures. A user study showed that Hap’n’Roll can convey at least three sizes (small, medium, and large) and four types of shapes (a cylinder, a rectangle, a cone, and a cup), with a shape and size identification accuracy of approx. 76.1%. The identification accuracy for shape alone was approx. 98.5%. Moreover, several applications were developed to showcase the effectiveness of Hap’n’Roll’s mechanism for various haptic feedback.

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced in/about LATAM; a set of driving forces and tensions with a heavy reliance on diasporic dynamics; and a set of professional demands and associated structural limitations. We also offer a roadmap towards building connections across HCI communities, in an attempt to rebuild HCI as a pluriverse.

Various studies have suggested that altering the appearance of food can impact multisensory flavor perception. The cross-modal effect of such visual changes on gustation may allow for the presentation of food tastes that are difficult to express with simple combinations of taste stimuli. This cross-modal effect of visual changes on gustation holds potential for applications in gustatory displays. However, the current limitation of existing Head-Mounted Displays (HMDs) is their restricted vertical Field of View (FoV), which prohibits the display of images near the mouth while eating. This limitation may impede the cross-modal effect of visual changes on multisensory flavor perception. Additionally, the lack of visibility around the mouth area challenges the ease of eating. To address these issues, we design a Video See-Through (VST)-HMD with an expanded vertical FoV (approx. 100 [deg]). Using the HMD, we investigated how presenting visual information near the mouth affects the cross-modal effects of flavor perception and ease of eating. In our experiment, machine learning techniques were utilized to alter the appearance of food. However, the result showed no significant differences in the amount of cross-modal effects or the ease of eating between the groups with and without visual information near the mouth. As a discussion of this result, the participants may not direct their visual attention to the food when they put the food in their mouths. The experiment also examined whether visual changes alter the taste as well as the smell and texture of the food. The findings demonstrated that visual changes could present the smell and texture of the food following the modifications. This result was confirmed irrespective of the visibility near the mouth.

Although a variety of haptic devices are used for virtual reality (VR) and augmented reality (AR) experiences, few can present a wide range of softness-hardness of the surface of the virtual objects. We propose a haptic device that can present a wide range of softness-hardness by using a two-layered structure consisting of a pneumatic balloon and a mechanical piston. Through a series of user studies, we confirmed that the prototype can present five levels of softness and three levels of hardness, and that the prototype device improves the VR experience in terms of realism, enjoyment, and comfort for virtual objects with a variety of softness/hardness.

Virtual avatars are more and more often featured in Virtual Reality (VR) and Augmented Reality (AR) applications. When embodying a virtual avatar, one may desire to change of appearance over the course of the embodiment. However, switching suddenly from one appearance to another can break the continuity of the user experience and potentially impact the sense of embodiment (SoE), especially when the new appearance is very different. In this paper, we explore how applying smooth visual transitions at the moment of the change can help to maintain the SoE and benefit the general user experience. To address this, we implemented an AR system allowing users to embody a regular-shaped avatar that can be transformed into a muscular one through a visual effect. The avatar's transformation can be triggered either by the user through physical action (“active” transition), or automatically launched by the system (“passive” transition). We conducted a user study to evaluate the effects of these two types of transformations on the SoE by comparing them to control conditions where there was no visual feedback of the transformation. Our results show that changing the appearance of one's avatar with an active transition (with visual feedback), compared to a passive transition, helps to maintain the user's sense of agency, a component of the SoE. They also partially suggest that the Proteus effects experienced during the embodiment were enhanced by these transitions. Therefore, we conclude that visual effects controlled by the user when changing their avatar's appearance can benefit their experience by preserving the SoE and intensifying the Proteus effects.

Smiling plays a crucial role in human communication. It is the most frequent expression shown in daily life. Smile analysis usually employs computer vision-based methods that use data sets annotated by experts. However, cameras have space constraints in most realistic scenarios due to occlusions. Wearable electromyography is a promising alternative; however, issue of user comfort is a barrier to long-term use. Other wearable-based methods can detect smiles, but they lack consistency because they use subjective criteria without expert annotation. We investigate a wearable-based method that uses optical sensors for consistent smile intensity estimation while reducing manual annotation cost. First, we use a state-of-art computer vision method (OpenFace) to train a regression model to estimate smile intensity from sensor data. Then, we compare the estimation result to that of OpenFace. We also compared their results to human annotation. The results show that the wearable method has a higher matching coefficient (r=0.67) with human annotated smile intensity than OpenFace (r=0.56). Also, when the sensor data and OpenFace output were fused, the multimodal method produced estimates closer to human annotation (r=0.74). Finally, we investigate how the synchrony of smile dynamics among subjects and their average smile intensity are correlated to assess the potential of wearable smile intensity estimation.

Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalises on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction of the facial expression production, whilst allowing EMG measurements. However, measuring EMG distally entails that the exact source of the facial movement is unknown. Therefore, we investigated whether we could identify specific Facial Action Units (AUs) from distal facial EMG after an initial calibration phase with Computer Vision (CV). We compared Support Vector Machines (SVM) and Random Forest (RF) with several types of feature engineering and early fusion of the two modalities. The detection performance for AU6 (Orbicularis Oculi) and AU12 (Zygomaticus Major) was estimated by calculating the agreement with Facial Action Coding System (FACS) certified coders. The best results were achieved using Random Forest. Using a fusion of CV and EMG features resulted in F1 scores of 0.83 for AU6; and the fusion of engineered EMG plus CV returned an F1 score of 0.81 for AU12. Both these results are well above the CV baseline that shows F1 scores of 0.56 and 0.62 for AU6 and AU12 respectively. This demonstrates the potential of distal EMG to detect individual facial movements. It also enables researchers to compare the results measured with this wearable device to psychological research on facial expressions using FACS. Using a wearable enables measurements with higher ecological validity. Finally, we observed that EMG activity starts before the onset of visually perceived movement. Because of this, the agreement between EMG-based methods and FACS coders might be underestimating the ground truth.

Self-tracking aims to increase awareness, decrease undesired behaviors, and ultimately lead towards a healthier lifestyle. However, inappropriate communication of self- tracking results might cause the opposite effect. Subtle self- tracking feedback is an alternative that can be provided with the aid of an artificial agent representing the self. Hence, we propose a wearable pet that reflects the user's affective states through visual and haptic feedback. By eliciting empathy and fostering helping behaviors towards it, users would indirectly help themselves. A wearable prototype was built, and three user studies performed to evaluate the appropriateness of the proposed affective representations. Visual representations using facial and body cues were clear for valence and less clear for arousal. Haptic interoceptive patterns emulating heart-rate levels matched the desired feedback urgency levels with a saturation frequency. The integrated visuo-haptic representations matched to participants own affective experience. From the results, we derived three design guidelines for future robot mirroring wearable systems: physical embodiment, interoceptive feedback, and customization.

Affective communication technologies are designed to enhance awareness, social connectedness, and affectivity. Design strategies involve alternative methods to convey affection in computer-mediated scenarios, emphasizing on the importance of mediated physical contact. Therefore, we proposed a huggable interface to mediate social touch by sensing the user's hug gestures, transferring them to a paired device, and delivering them as simple cues. We investigated the effect of the huggable interface as a mediator with a physical embodiment and compared it with a similar communication interface represented by an agent with a virtual embodiment on a touch screen. During the experiments, we set up a scenario in which individuals with a close relationship watched movies and communicated with each other. Results showed the effect of both interfaces in terms of perceived social connectedness and stress levels. The discussion pointed out the potential and limitations of the proposed evaluation method, as well as of each type of interface as affective communication technology.

Electro-Dermal Activity (EDA)and head movement have been shown to correlate with felt affect. Given the easiness to measure them, they are suitable as a wearable affective assessment tool. Arguably, autonomic affective responses such as EDA are less affected by volition than the production of other embodied cues of affect such as facial expressions. Moreover, head movement has been shown as a reliable source of information about the intention behind a facial expression. Therefore, we explored the feasibility of using EDA measured from the neck and head movement to make inferences about the nature of facial expressions, in particular, smiles. EDA was measured simultaneously from the hand and the neck of participants displaying spontaneous and posed smiles. Our results show that both measurement locations are highly correlated. Furthermore, EDA signals carry information about the spontaneity of the measured smiles, as shown by a classification accuracy of about 90%. Finally, head movement turned out to be rather revealing, with classification accuracy reaching about 99 %.

Positive experiences are a success metric in product and service design. Quantifying smiles is a method of assessing them continuously. Smiles are usually a cue of positive affect, but they can also be fabricated voluntarily. Automatic detection is a promising complement to human perception in terms of identifying the differences between smile types. Computer vision (CV) and facial distal electromyography (EMG) have been proven successful in this task. This is the first study to use a wearable EMG that does not obstruct the face to compare the performance of CV and EMG measurements in the task of distinguishing between posed and spontaneous smiles. The results showed that EMG has the advantage of being able to identify covert behavior not available through vision. Moreover, CV appears to be able to identify visible dynamic features that human judges cannot account for. This sheds light on the role of non-observable behavior in distinguishing affect-related smiles from polite positive affect displays.

Facial expressions are one of the most salient cues of affect. However, they are also a communication tool that can be expressed voluntarily. Spontaneous and posed facial expressions have different characteristics. In this study, we show the potential of using Electromyography (EMG) in a wearable device to automatically differentiate between posed and spontaneous smiles. This classification is moderately successful when using spatial and magnitude features, and increases significantly if temporal features are included. Hence, the high temporal resolution of EMG-based detection is advantageous in this task.

Facial expressions are usually linked to emotional states of a person, and are among the most salient cues for automatic emotion recognition. They are an indispensable social communication tool, and therefore, they can also be fabricated to face complex situations in social interaction. Despite efforts to either deliberately or unconsciously conceal an emotion, micro-expressions are usually leaked. Because of their revealing nature, potential applications can arise from automatically detecting them, both at a personal and inter-personal levels. Therefore, we explored micro-expression detection using a wearable device that detects distal facial EMG signals unobtrusively, as an alternative to Computer Vision-based detection. EMG recognition is advantageous over commonly used video recognition techniques because of its robustness against occlusion and light changes, good temporal resolution, and independence of movement. We evaluated the performance of the wearable device on micro-smile recognition. The results show the potential of EMG to detect such fast and subtle spontaneous expressions. Finally, we evaluated the device as a tool to provide affective annotations of Advertisement Videos, and social interaction in a face-to-face context while watching these stimuli.