Malleable Embodiment

Kasahara, Shunichi; Konno, Keina; Owaki, Richi; Nishi, Tsubasa; Takeshita, Akiko; Itō, Takayuki; Kasuga, Shoko; Ushiba, Junichi

doi:10.1145/3025453.3025962

Cited by 45 publications

(14 citation statements)

References 29 publications

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“…Using a system that captures human body movement and generates an immersive 1PP view of one's body position with a small (forward or backward) shift in time, it was found that spatiotemporal deformations of the body representation induced distinct physical sensations (Kasahara et al, 2017). Viewing a prediction of one's body image in the future induced lighter, nimble and vitalized sensations, whereas past views evoked heavier, dull, and diminished sensations.…”

Section: Visual Perspectivementioning

confidence: 99%

Toward Enhanced Teleoperation Through Embodiment

et al. 2020

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Telerobotics aims to transfer human manipulation skills and dexterity over an arbitrary distance and at an arbitrary scale to a remote workplace. A telerobotic system that is transparent enables a natural and intuitive interaction. We postulate that embodiment (with three sub-components: sense of ownership, agency, and self-location) of the robotic system leads to optimal perceptual transparency and increases task performance. However, this has not yet been investigated directly. We reason along four premises and present findings from the literature that substantiate each of them: (1) the brain can embody non-bodily objects (e.g., robotic hands), (2) embodiment can be elicited with mediated sensorimotor interaction, (3) embodiment is robust against inconsistencies between the robotic system and the operator's body, and (4) embodiment positively correlates to dexterous task performance. We use the predictive encoding theory as a framework to interpret and discuss the results reported in the literature. Numerous previous studies have shown that it is possible to induce embodiment over a wide range of virtual and real extracorporeal objects (including artificial limbs, avatars, and android robots) through mediated sensorimotor interaction. Also, embodiment can occur for non-human morphologies including for elongated arms and a tail. In accordance with the predictive encoding theory, none of the sensory modalities is critical in establishing ownership, and discrepancies in multisensory signals do not necessarily lead to loss of embodiment. However, large discrepancies in terms of multisensory synchrony or visual likeness can prohibit embodiment from occurring. The literature provides less extensive support for the link between embodiment and (dexterous) task performance. However, data gathered with prosthetic hands do indicate a positive correlation. We conclude that all four premises are supported by direct or indirect evidence in the literature, suggesting that embodiment of a remote manipulator may improve dexterous performance in telerobotics. This warrants further implementation testing of embodiment in telerobotics. We formulate a first set of guidelines to apply embodiment in telerobotics and identify some important research topics.

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Section: Visual Perspectivementioning

confidence: 99%

Toward Enhanced Teleoperation Through Embodiment

et al. 2020

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“…Numerous studies have explored people's experience in VR with the goal of designing better systems that enhance interaction. These largely consist of self-report questionnaires, qualitative analysis or performance measures to evaluate the effectiveness of VR interaction (e.g., [10,19,20,35]). These measures give information about the system's general usability and the impressions of immersion, but do not consider whether a particular tool or controls realistically reproduce interaction in the real world, much less tool embodiment.…”

Section: Virtual Realitymentioning

confidence: 99%

“…Research in virtual reality has a long tradition striving to reproduce the sensations, perceptions and experience of being elsewhere (this is often referred to as presence [39,43], immersion [19] or spatial-temporal deformation [1]). An important part of this presence is interaction; people in an immersive VR environment need to be able to interact realistically with the objects in the environment in order not to break the illusion of immersion in an alternate reality [37].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Measurement of Tool Embodiment for Virtual Reality Input Alternatives

Alzayat

Hancock

Nacenta

2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

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Virtual reality (VR) strives to replicate the sensation of the physical environment by mimicking people's perceptions and experience of being elsewhere. These experiences are often mediated by the objects and tools we interact with in the virtual world (e.g., a controller). Evidence from psychology posits that when using the tool proficiently, it becomes embodied (i.e., an extension of one's body). There is little work, however, on how to measure this phenomenon in VR, and on how different types of tools and controllers can affect the experience of interaction. In this work, we leverage cognitive psychology and philosophy literature to construct the Locusof-Attention Index (LAI), a measure of tool embodiment. We designed and conducted a study that measures readiness-tohand and unreadiness-to-hand for three VR interaction techniques: hands, a physical tool, and a VR controller. The study shows that LAI can measure differences in embodiment with working and broken tools and that using the hand directly results in more embodiment than using controllers. CCS CONCEPTS• Human-centered computing → HCI design and evaluation methods; Interaction devices; Empirical studies in HCI .

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“…Due to the immense consequences of VR motion sickness, researchers and developers seek to reduce delays and to predict head [2,39,50] and body [22,31,42] movements to improve subjective experiences and objective measures. Wu and Ming demonstrated that compensating delays using head movement extrapolation techniques in VR can improve human performance in spatial tasks [50].…”

Section: Movement Predictions In Vrmentioning

confidence: 99%

“…However, the effects of machine learning (ML)-based algorithms predicting real-time or future movements of the own body particularly in the context of time critical input performance or immersive experiences are currently unknown. Previous research of linearly extrapolated movements indicates that induced a "lighter weight" sensation [22], however, objective findings about using neural networks able to predict the own movements in VR or input measures quantifying the input performance in a gaming scenario are currently unknown.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Full-Body Avatar Movement Predictions in Virtual Reality using Neural Networks

Schwind

Fehle

Sasse

et al. 2020

26th ACM Symposium on Virtual Reality Software and Technology

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Motion tracking technologies and avatars in virtual reality (VR) showing the movements of the own body enable high levels of presence and a strong illusion of body ownership (IBO)-key features of immersive systems and gaming experiences in virtual environments. Previous work suggests using software-based algorithms that can not only compensate system latency but also predict future movements of the user to increase input performance. However, the effects of movement prediction in VR on input performance are largely unknown. In this paper, we investigate neural networkbased predictions of full-body avatar movements in two scenarios: In the first study, we used a standardized 2D Fitts' Law task to examine the information throughput in VR. In the second study, we utilized a full-body VR game to determine the users' performance. We found that both performance and subjective measures in a standardized 2D Fitts' law task could not benefit from the predicted avatar movements. In an immersive gaming scenario, however, the perceived accuracy of the own body location improved. Presence and body assessments remained more stable and were higher than during the Fitts' task. We conclude that machine-learning-based predictions could be used to compensate system-related latency but participants only subjectively benefit under certain conditions.

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Malleable Embodiment

Cited by 45 publications

References 29 publications

Toward Enhanced Teleoperation Through Embodiment

Toward Enhanced Teleoperation Through Embodiment

Quantitative Measurement of Tool Embodiment for Virtual Reality Input Alternatives

The Effects of Full-Body Avatar Movement Predictions in Virtual Reality using Neural Networks

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