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.

Pseudo-haptics techniques are interesting alternatives for inducing haptic perceptions, achieved by manipulating haptic perception through the appropriate alteration of primarily visual feedback in response to body movements. However, the use of pseudo-haptics techniques with a motion-input system can sometimes be limited. This paper investigates a novel approach for extending the potential of pseudo-haptics techniques in virtual reality (VR), focusing on pseudoweight perception as the target case. The proposed approach utilizes a reaction force from force-input as a substitution of haptic cue for the pseudo-haptic perception. The paper introduced a manipulation method in which the vertical acceleration of the virtual hand is controlled by the extent of push-in of a force sensor. Such a force-input manipulation of a virtual body cannot only present pseudo-haptics with smaller physical spaces and be used by various users including physically handicapped people, but can present the reaction force proportional to the user's input to the user. We hypothesized that such a haptic force cue would contribute to the pseudo-haptic, here, the pseudoweight perception. Therefore, the paper endeavors to investigate the force-input pseudo-haptic perception in comparison with the motion-input pseudo-haptics. The paper compared force-input and motion-input manipulation in a point of achievable range and resolution of pseudo-weight. The experimental results suggest that forceinput manipulation successfully extends the range of perceptible pseudo-weight by 80% in comparison to the motion-input manipulation. On the other hand, it is revealed that motion-input manipulation has 1 step larger number of distinguishable weight levels and is easier to operate.

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.

Pseudo-haptics alters vision to evoke haptic perception without dedicated hardware. We introduce a method that reproduces sticky-surface interactions during attaching-detaching 3D direct manipulation in VR by blending three cues—motion gain, surfacedeformation, and vibration—in various combinations. Accurately sensing how strongly a surface “clings” is vital for realistic grasping, adhesion training and material evaluation, yet it has been largely overlooked in pseudo-haptic research. Three studies evaluated these cues. The first experiment compared individual and combined cues on perceived stickiness, confirming that additional cues reliably strengthened perceived stickiness. The second experiment tested how cue number affects tolerance for visual-physical mismatch, indicating that they lowered the minimum detectable threshold though they did not widen the overall tolerated mismatch. The third experiment measured whether and how much added cues sharpen perceptual resolution, showing multiple cues improved perceptual resolution by reducing just noticeable differences by 44 % ($1.8 \times$finer), doubling discriminable levels from roughly eight with a single cue to sixteen with all cues.

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.

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.

We propose Selfrionette, a controller that uses fingertip force input to drive avatar movements in virtual reality (VR). This system enables users to interact with virtual objects and walk in VR using only fingertip force, overcoming physical and spatial constraints. Additionally, by fixing users’ fingers, it provides users with counterforces equivalent to the applied force, allowing for diverse and wide dynamic range haptic feedback by adjusting the relationship between force input and virtual movement. To evaluate the effectiveness of the proposed method, this paper focuses on hand interaction as a first step. In User Study 1, we measured usability and embodiment during reaching tasks under Selfrionette, body tracking, and finger tracking conditions. In User Study 2, we investigated whether users could perceive haptic properties such as weight, friction, and compliance under the same conditions as User Study 1. Selfrionette was found to be comparable to body tracking in realism of haptic interaction, enabling embodied avatar experiences even in limited spatial conditions.

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.

Pseudo-haptic techniques are used to modify haptic perception by appropriately changing visual feedback to body movements. Based on the knowledge that tendon vibration can affect our somatosensory perception, this article proposes a method for leveraging tendon vibration to enhance pseudo-haptics during free arm motion. Three experiments were performed to examine the impact of tendon vibration on the range and resolution of pseudo-haptics. The first experiment investigated the effect of tendon vibration on the detection threshold of the discrepancy between visual and physical motion. The results indicated that vibrations applied to the inner tendons of the wrist and elbow increased the threshold, suggesting that tendon vibration can augment the applicable visual motion gain by approximately 13% without users detecting the visual/physical discrepancy. Furthermore, the results demonstrate that tendon vibration acts as noise on haptic motion cues. The second experiment assessed the impact of tendon vibration on the resolution of pseudo-haptics by determining the just noticeable difference in pseudo-weight perception. The results suggested that the tendon vibration does not largely compromise the resolution of pseudo-haptics. The third experiment evaluated the equivalence between the weight perception triggered by tendon vibration and that by visual motion gain, that is, the point of subjective equality. The results revealed that vibration amplifies the weight perception and its effect was equivalent to that obtained using a gain of 0.64 without vibration, implying that the tendon vibration also functions as an additional haptic cue. Our results provide design guidelines and future work for enhancing pseudo-haptics with tendon vibration.

Walking-in-place (WIP) is a locomotion technique that enables users to "walk infinitely" through vast virtual environments using walking-like gestures within a limited physical space. This article investigates alternative interaction schemes for WIP, addressing successively the control, input, and output of WIP. First, we introduce a novel height-based control to increase advanced speed. Second, we introduce a novel input system for WIP based on elastic and passive strips. Third, we introduce the use of pseudo-haptic feedback as a novel output for WIP meant to alter walking sensations. The results of a series of user studies show that height and frequency based control of WIP can facilitate higher virtual speed with greater efficacy and ease than in frequency-based WIP. Second, using an upward elastic input system can result in a stable virtual speed control, although excessively strong elastic forces may impact the usability and user experience. Finally, using a pseudo-haptic approach can improve the perceived realism of virtual slopes. Taken together, our results suggest that, for future VR applications, there is value in further research into the use of alternative interaction schemes for walking-in-place.

Timbre is high-dimensional and sensuous, making it difficult for musical-instrument learners to improve their timbre. Although some systems exist to improve timbre, they require expert labeling for timbre evaluation; however, solely visualizing the results of unsupervised learning lacks the intuitiveness of feedback because human perception is not considered. Therefore, we employ crossmodal correspondences for intuitive visualization of the timbre. We designed TimToShape, a system that visualizes timbre with 2D shapes based on the user’s input of timbre–shape correspondences. TimToShape generates a shape morphed by linear interpolation according to the timbre’s position in the latent space, which is obtained by unsupervised learning with a variational autoencoder (VAE). We confirmed that people perceived shapes generated by TimToShape to correspond more to timbre than randomly generated shapes. Furthermore, a user study of six violin players revealed that TimToShape was well-received in terms of visual clarity and interpretability.

In Virtual Reality, self-touch (ST) stimulation is a promising method of sense of body ownership (SoBO) induction that does not require an external effector. However, its applicability to dysmorphic bodies has not been explored yet and remains uncertain due to the requirement to provide incongruent visuomotor sensations. In this, paper, we studied the effect of ST stimulation on dysmorphic hands via haptic retargeting, as compared to a classical external-touch (ET) stimulation, on the SoBO. Our results indicate that ST can induce similar levels of dysmorphic SoBO than ET stimulation, but that some types of dysmorphism might decrease the ST stimulation accuracy due to the nature of the re-targeting that they induce.

Timbre is a crucial element in playing musical instruments, and it is difficult for beginners to learn it independently. Therefore, external feedback (FB) is required. However, conventional FB methods lack intuitiveness in visualization. In this study, we propose a novel FB method that adopts crossmodal correspondence to enhance the intuitive visualization of timbre with visual shapes. Based on the experiments, it was inferred that the FB based on crossmodal correspondence prevents dependence on FB and promotes learning.

In this paper, we propose a method of alleviating a sense of unnaturalness and individual differences in pseudo-haptic experience in VR. The method is to use a non-isomorphic manipulation of a virtual body, where the actual and virtual body movement is decoupled. As an evaluation of this approach, two manipulation methods are compared in the task of pulling a virtual object in VR. In the first one, the user physically performs a pulling gesture, and in the second one, the user pulls with a controller's analog stick (Fig. 1). A temporal delay is added to the virtual object's motion for the pseudo-haptic effect. The results suggest a tendency where the individual differences in pseudo-haptic experience and unnaturalness were smaller with the analog stick manipulation, even with intensive pseudo-haptic expressions.