Razeen Hussain

Mixed reality (MR) has proved to be a useful tool to enhance the physical environment with virtual content. However, significant challenges related to perceptual aspects in MR visualizations still remain. Inaccurate depth perception in MR can lead to errors in tasks such as reaching, object manipulation, and spatial alignment, ultimately reducing usability and immersion. To address this, we introduce a visualization method that provides additional focus cues that can potentially improve depth perception in mixed reality environments. Specifically, we introduce a technique to incorporate gaze-contingent deconvolution-based focus cues into commercial immersive media. We support the effectiveness of our technique in improving depth perception by experimental validation through a user study. Participants were asked to reach a physical position indicated by a virtual object on two types of mixed reality devices. The user study showed an error reduction of 33.3% for the 3D monitor and 53.1% for the Microsoft Hololens 2, compared to standard MR visualizations that lack auxiliary focus cues. Our work shows that auxiliary gaze-contingent focus cues can provide better depth and distance perception while providing an adequate immersive experience.

Virtual reality (VR) systems aim to create immersive experiences by blending real and virtual worlds. However, accurately perceiving distances in VR remains a challenge due to mismatches between real-world and simulated spatial cues. This study compares the effects of different locomotion methods—teleport, joystick, arms swinging, and real walking—on perceived traveled distance and spatial orientation in VR. An exploratory within-subject user study revealed that while arm swinging was the least preferred method, it yielded the most accurate distance estimations and superior spatial orientation. These findings underscore the importance of locomotion methods in enhancing spatial awareness in VR environments.

Deep learning has advanced rapidly in medical image segmentation, yet hepatopancreatic tumor delineation remains challenging due to low contrast, small lesion size, organ variability, and limited high-quality annotations. Existing reviews are outdated or overly broad, leaving recent architectural developments, training strategies, and dataset limitations insufficiently synthesized. To address this gap, we conducted a PRISMA 2020 systematic literature review of studies published between 2021 and 2026 on deep learning-based liver and pancreatic tumor segmentation. From 2307 records, 84 studies met inclusion criteria. U-Net variants continue to dominate, achieving strong liver segmentation but inconsistent tumor accuracy, while transformer-based and hybrid models improve global context modeling at higher computational cost. Attention mechanisms, boundary-refinement modules, and semi-supervised learning offer incremental gains, yet pancreatic tumor segmentation remains notably difficult. Persistent issues, including domain shift, class imbalance, and limited generalization across datasets, underscore the need for more robust architectures, standardized benchmarks, and clinically oriented evaluation. This review consolidates recent progress and highlights key challenges that must be addressed to advance reliable hepatopancreatic tumor segmentation.

Dementia is often accompanied by impairments in social cognition, such as reduced empathy and difficulties in emotion recognition, which negatively impact daily functioning and increase caregiver burden. Early detection of these deficits is crucial, yet traditional assessments relying on paper-and-pencil tests or actor-based stimuli face limitations in scalability and standardization. Advances in digital technologies, particularly high-fidelity digital characters such as MetaHumans, offer a more flexible and ecologically valid alternative. This pilot study compared MetaHuman digital characters with a validated set of human actor videos in an emotion recognition task involving 20 participants. Six basic emotions (Joy, Sadness, Anger, Fear, Surprise, and Disgust) were presented, and performance was evaluated through recognition accuracy, response times, perceived naturalness, and subjective ratings. MetaHumans achieved significantly higher accuracy and comparable response times, although actors were rated slightly more natural. These findings suggest that MetaHumans can provide standardized, reproducible, and cost-effective stimuli for assessing social cognition.

The authors present a solution for the visually impaired and blind people that incorporates various sensors and a sound/vibration system to notify the user about the potential presence of obstacles. Comments: * The authors state that the device i...

Recently, immersive media devices have seen a boost in popularity. However, many problems still remain. Depth perception is a crucial part of how humans behave and interact with their environment. Convergence and accommodation are two physiological mechanisms that provide important depth cues. However, when humans are immersed in virtual environments, they experience a mismatch between these cues. This mismatch causes users to feel discomfort while also hindering their ability to fully perceive object distances. To address the conflict, we have developed a technique that encompasses inverse blurring into immersive media devices. For the inverse blurring, we utilize the classical Wiener deconvolution approach by proposing a novel technique that is applied without the need for an eye-tracker and implemented in a commercial immersive media device. The technique's ability to compensate for the vergence-accommodation conflict was verified through two user studies aimed at reaching and spatial awareness, respectively. The two studies yielded a statistically significant 36% and 48% error reduction in user performance to estimate distances, respectively. Overall, the work done demonstrates how visual stimuli can be modified to allow users to achieve a more natural perception and interaction with the virtual environment.

Cybersickness is one of the major roadblocks in the widespread adoption of mixed reality devices. Prolonged exposure to these devices, especially virtual reality devices, can cause users to feel discomfort and nausea, spoiling the immersive experience. Incorporating spatial blur in stereoscopic 3D stimuli has shown to reduce cybersickness. In this paper, we develop a technique to incorporate spatial blur in VR systems inspired by the human physiological system. The technique makes use of concepts from foveated imaging and depth-of-field. The developed technique can be applied to any eye tracker equipped VR system as a post-processing step to provide an artifact-free scene. We verify the usefulness of the proposed system by conducting a user study on cybersickness evaluation. We used a custom-built rollercoaster VR environment developed in Unity and an HTC Vive Pro Eye headset to interact with the user. A Simulator Sickness Questionnaire was used to measure the induced sickness while gaze and heart rate data were recorded for quantitative analysis. The experimental analysis highlighted the aptness of our foveated depth-of-field effect in reducing cybersickness in virtual environments by reducing the sickness scores by approximately 66%.

Walking machines have proved to be an important invention as they do not require any prepared surface, making them ideal for applications involving unexplored environments. They are equipped with a large number of actuators and sensors to achieve a robust locomotion; thus, a systematic approach to designing their control system is required. This article presents a functional control structure based on the logic-labelled finite state automaton approach developed for walking machines. A general control structure is presented and a hexapod robot is used to verify the practicability of the design.