The Evaluation of User Experience and Functional Workload of a Physically Inter- active Virtual Reality System

Mubarrat, Syed T.; Opafunso, Oluwatosin; Chowdhury, S.

doi:10.1177/1071181320641505

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Instead of matching virtual objects with their real-world equivalents, it employed a robotic arm to proactively align physical items with their virtual analogs. Utilizing various combinations of physical props, These methods have proven successful in VR training systems by offering users direct experience with real-world tasks and aiding them in maintaining pertinent motor skills, safety consciousness, and operational work ability [32], [38], [39], [40].…”

Section: B Physics-based Hapticsmentioning

confidence: 99%

“…In recent years, the development of VR trackers and the integration of VR and motion capture systems have aided researchers in developing high-fidelity virtual replications of real-world objects (i.e., physics-based modeling), in which users are provided with real cutaneous (object geometries) and kinesthetic (force/vibration) feedback of the virtual objects (i.e., physics-based haptics) [32], [33], [34], [35], [36], [37]. Using different hardware (such as VR trackers or motion capture markers) or software to track objects in the real world and simulate them in the VE, these methods have proven to be effective in simulating high-fidelity VR training systems [32], [38], [39], [40].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Visual-Spatiotemporal Co-Registration of a Physics-Based Virtual Reality Haptic Interface

Mubarrat,

Chowdhury,

Fernandes

2024

IEEE Access

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This study aimed to evaluate the visual-spatiotemporal co-registration of the real and virtual objects' movement dynamics in a low-cost physics-based virtual reality (VR) system that provides real cutaneous and kinesthetic haptic feedback of the objects instead of computer-generated haptic feedback. Twelve healthy participants performed three human-robot collaborative (HRC) sequential pick-and-place lifting tasks while both motion capture and VR systems respectively traced the movement kinematics of the real and virtual objects. We used an iterative closest point algorithm to transform the 3D spatial point clouds of the VR system into the motion capture system. We employed a novel algorithm and principal component analysis to respectively calculate visual and spatiotemporal co-registration precisions between virtual and real objects. Results showed a high correlation (r > 0.96) between real and virtual objects' movement dynamics and linear and angular co-registration errors of less than 5 cm and 8°, respectively. The trend also revealed a low temporal registration error of <12 ms and was only found along the vertical axis. The visual registration data indicated that the real cutaneous and kinesthetic haptics provided by the physical objects in the virtual environment enhanced proprioception and visuomotor functions of the users.

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Section: B Physics-based Hapticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Visual-Spatiotemporal Co-Registration of a Physics-Based Virtual Reality Haptic Interface

Mubarrat,

Chowdhury,

Fernandes

2024

IEEE Access

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“…The NASA-TLX [27] based on a weighted average of ratings on six subscales: 'mental demand,' 'physical demand,' 'temporal demand,' 'performance,' 'effort,' and 'frustration' was used to measure the overall workload (a score from 0 to 100) of the subjects for the successful completion of each task. Based on previous literature [28], [29], [30] and our previous work [31], we developed a novel subjective questionnaire to evaluate the perceived ease-of-use, usefulness, and ease-of-learning of our PhyVirtual system along with the comparison between VET and RET (Table I). We asked these structured questionnaires after each VET.…”

Section: Data Collectionmentioning

confidence: 99%

A Physics-based Virtual Reality System Design and Evaluation by Simulating Human-Robot Collaboration

Chowdhury¹,

Mubarrat²,

Fernandes³

2022

Preprint

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Recent advancements in VR technology facilitate tracking real-world objects and users' movements in the virtual environment (VE) and inspire researchers to develop a physics-based haptic system (i.e., real object haptics) instead of computer-generated haptic feedback. However, there is limited research on the efficacy of such VR systems in enhancing operators’ sensorimotor learning for tasks that require high motor and physical demands. Therefore, this study aimed to design and evaluate the efficacy of a physics-based virtual reality (VR) system that provides users realistic cutaneous and kinesthetic haptic feedback. We designed a physics-based VR system, named PhyVirtual, and simulated human-robot collaborative (HRC) sequential pick-and-place lifting tasks in the VE. Participants performed the same tasks in the real environment (RE) with human-human collaboration instead of human-robot collaboration. We used a custom-designed questionnaire, the NASA-TLX, and electromyography activities from biceps, middle and anterior deltoid muscles to determine user experience, workload, and neuromuscular dynamics, respectively. Overall, the majority of responses (>65%) demonstrated that the system is easy-to-use, easy-to-learn, and effective in improving motor skill performance. While compared to tasks performed in the RE, the PhyVirtual system placed significantly lower physical demand (124.90%; p < 0.05) on the user. The electromyography data exhibited similar trends (p > 0.05; r > 0.89) for both environments. These results show that the PhyVirtual system is an effective tool to simulate safe human-robot collaboration commonly seen in many modern warehousing settings. Moreover, it can be used as a viable replacement for live sensorimotor training in a wide range of fields.

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“…In terms of mode of learning, researchers suggest that virtual reality (VR) enhances learning by increasing engagement and immersion [4,8,[18][19][20]30]. VR's spatial navigation reduces the cognitive load in programming learning, surpassing traditional text-based methods [10].…”

Section: Introductionmentioning

confidence: 99%

GeoBotsVR: A Robotics Learning Game for Beginners with Hands-on Learning Simulation

Mubarrat

2024

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

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The Evaluation of User Experience and Functional Workload of a Physically Inter- active Virtual Reality System

Cited by 4 publications

References 8 publications

Evaluating Visual-Spatiotemporal Co-Registration of a Physics-Based Virtual Reality Haptic Interface

Evaluating Visual-Spatiotemporal Co-Registration of a Physics-Based Virtual Reality Haptic Interface

A Physics-based Virtual Reality System Design and Evaluation by Simulating Human-Robot Collaboration

GeoBotsVR: A Robotics Learning Game for Beginners with Hands-on Learning Simulation

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