Introduction: Since kinesthetic cues are not present in virtual environments, users have difficulty feeling the heaviness of virtual objects. To address this issue, pseudo-haptic approaches have been proposed to illusorily induce the weight of virtual objects through the user’s visual sensory system.Methods: In this paper, we used two methods to induce the impression of virtual objects’ heaviness. One relies on the direct modification of the control-display (C/D) ratio when lifting objects, and the other depends on controlling this ratio based on a velocity restriction. We innovatively measured each approach’s efficiency by analyzing physical work as an objective metric. In addition, we used Borg CR10 to measure users’ hand fatigue during the experimental phases.Results: Our findings are discussed in terms of individual lifting behavior in different pseudo-haptic methods. Furthermore, different virtual weight-lifting behaviors were compared to the same real-world weight-lifting behaviors.Discussion: According to our results, the direct control of the C/D ratio method provides virtual reality users with a more accurate weight perception than the velocity restriction one. Furthermore, with this first method, users’ lifting behavior was closer to the behavior when lifting real objects.

Worker safety and productivity are crucial for effective job management. Interruptions to an individual’s work environment and their impact on mental health can have adverse effects. One prospective instrument for assessing and calculating an individual’s mental state in an interrupted scenario and cognitive demand levels is the use of physiological computing devices in conjunction with behavioral and subjective measurements. This study sought to address how to gather and compute data on individuals’ cognitive states in interrupted work settings through critical analysis. Thirty-three papers were considered after the literature search and selection procedure. This descriptive study is conducted from three perspectives: parameter measurement, research design, and data analysis. The variables evaluated were working memory, stress, emotional state, performance, and resumption lag. The subject recruitment, experimental task design, and measurement techniques were examined from the standpoint of the experimental design. Data analysis included computing and cognitive pre-processing. Four future research directions are suggested to address the shortcomings of the present studies. This study offers suggestions for researchers on experiment planning and using computing to analyze individuals’ cognitive states during interrupted work scenarios. Additionally, it offers helpful recommendations for organizing and conducting future research.

This study investigates weight perception in virtual reality without kinesthetic feedback from the real world, by means of an illusory method called pseudo-haptic. This illusory model focuses on the dissociation of visual input and somatosensory feedback and tries to induce the sensation of virtual objects' loads in VR users by manipulating visual input. For that, modifications on the control-display ratio, i.e., between the real and virtual motions of the arm, can be used to produce a visual illusionary effect on the virtual objects' positions as well. Therefore, VR users perceive it as velocity variations in the objects' displacements, helping them achieve a better sensation of virtual weight. A primary contribution of this paper is the development of a novel, holistic assessment methodology that measures the sense of the presence in virtual reality contexts, particularly when participants are lifting virtual objects and experiencing their weight. Our study examined the effect of virtual object weight on the kinematic parameters and velocity profiles of participants' upward arm motions, along with a parallel experiment conducted using real weights. By comparing the lifting of real objects with that of virtual objects, it was possible to gain insight into the variations in kinematic features observed in participants' arm motions. Additionally, subjective measurements, utilizing the Borg CR10 questionnaire, were conducted to assess participants' perceptions of hand fatigue. The analysis of collected data, encompassing both subjective and objective measurements, concluded that participants experienced similar sensations of fatigue and changes in hand kinematics during both virtual object tasks, resulting from pseudo-haptic feedback, and real weight lifting tasks. This consistency in findings underscores the efficacy of pseudo-haptic feedback in simulating realistic weight sensations in virtual environments.

The use of virtual reality (VR) has made significant advancements, and now it is widely used across a range of applications. However, consumers’ capacity to fully enjoy VR experiences continues to be limited by a chronic problem known as cybersickness (CS). This study explores the feasibility of mitigating CS through geometric scene simplification combined with electroencephalography (EEG)-based monitoring. According to the sensory conflict theory, this issue is caused by the discrepancy between the visually induced self-motion (VIMS) through immersive displays and the real motion the vestibular system detects. While prior mitigation strategies have largely relied on hardware modifications or visual field restrictions, this paper introduces a novel framework that integrates geometric scene simplification with EEG-based neurophysiological activity to reduce VIMS during VR immersion. The proposed framework combines EEG neurophysiology, allowing us to monitor users’ brainwave activity and cognitive states during virtual immersion experience. The empirical evidence from our investigation shows a correlation between CS manifestation and neural activation in the parietal and temporal lobes. As an experiment with 15 subjects, statistical differences were significantly different with $P= 0.001$ and large effect size $\eta 2=0.28$, while preliminary trends suggest lower neural activation during simplified scenes. Notably, a decrease in neural activation corresponding to reduced optic flow (OF) suggests that VR environment simplification may help attenuate CS symptoms, providing preliminary support for the proposed strategy.

The success of virtual reality (VR) agents is not solely defined by task completion; adaptability and the ability to move beyond repetitive, robotic behavior are equally critical for embodiment, social interaction, rehabilitation, and training. To address this challenge, we propose a hybrid training methodology that integrates imitation learning and reinforcement learning to develop agents that both master tasks and generalize beyond demonstrations. Our framework combines Behavioral Cloning (BC) for rapid policy initialization, Proximal Policy Optimization (PPO) for stable reinforcement-driven learning, and Generative Adversarial Imitation Learning (GAIL) for imitation-based rewards, resulting in policies that converge efficiently while avoiding repetitive imitation. We implemented this framework in a Unity-based VR environment, where participants performed a single-joint cube pick-and-place task using a headset and controller. Human demonstrations were collected and used to train and evaluate agents against multiple motion metrics, including Dynamic Time Warping (DTW), Fréchet distance, minimum jerk cost, Fitts’s law, and curvature–velocity coupling. Experimental results showed that hybrid-trained agents achieved stable convergence, consistent task mastery, and motion trajectories comparable to human demonstrations across temporal, spatial, and smoothness dimensions. Notably, the agents exhibited adaptive, non-repetitive behavior without explicitly optimizing for biomechanical fidelity, suggesting that human-like variability can emerge naturally from our customized hybrid IL+RL training. These findings provide a foundation for scaling the methodology toward multi-joint and full-body motion learning, with promising implications for future VR applications in rehabilitation, training, and embodied interaction.

Introduction: Objectively quantifying emotional responses in Virtual Reality (VR) remains a challenging for affective computing and adaptive interface design, particularly during active motor task execution. Methods: This study presents a dual-layer emotion sensing framework that combines rule-based Facial Action Unit (AU) analysis with GPT-4 based semantic interpretation to characterize affective responses in immersive environments. As a pilot methodological validation, twenty-one healthy adults performed identical motor tasks in two VR settings (a Contentment Forest and a Horror Morgue) while facial expressions were monitored. Results: Co-activations of 63 AUs were classified into six basic emotions; five emotions were retained for statistical analysis, revealing significant differences in emotional distributions between environments (χ ² (4) = 69.92, p < 0.001). These emotion labels were subsequently reinterpreted into higher-level affective dimensions (Contentment and Anxiety). Discussion: The dual-layer framework enables continuous, emotion profiling in VR without intrusive sensors, suggesting potential applications for future affect-aware VR applications in rehabilitation, training, and entertainment.