Correction of Avatar Hand Movements Supports Learning of a Motor Skill

Lilija, Klemen; Kyllingsbæk, Søren; Hornbæk, Kasper

doi:10.1109/vr50410.2021.00069

Cited by 14 publications

(12 citation statements)

References 26 publications

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“…In this study participants learned mid-air gestures with help of a novel guidance technique that corrected the avatar hand movements during training. The purpose of the study was to acquire evidence showing that correcting the avatar movements can be used to support motor learning (Lilija et al, 2021). The investigated technique was compared to a conventional hand guidance technique (ghosted hand) and a control condition (no guidance) in a within-subject study design.…”

Section: Study Ii: Hand Trackingmentioning

confidence: 99%

Conducting Unsupervised Virtual Reality User Studies Online

Mottelson

Petersen

Lilija

et al. 2021

Front. Virtual Real.

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Conducting user studies online and unsupervised instead of in laboratories gives quick access to a large and inexpensive participant pool. It is however unclear if data sourced this way is valid, and what the best practices for conducting unsupervised VR studies are. The restrictions on laboratory access experienced during COVID-19 further necessitate the development of valid procedures for remote data collection, especially for research fields such as VR that heavily rely on laboratory studies. In this paper we report our experiences with conducting two unsupervised VR studies amidst the pandemic, by recruiting participants online on relevant fora and employing participants’ own standalone VR equipment. We investigate whether it is feasible to collect valid data across in-VR survey responses and hand tracking. We report a good reliability of collected data, which requires only slightly more sanitation than a comparable laboratory study. We synthesize our experiences into practical recommendations for conducting unsupervised VR user studies using online recruitment, which can greatly reduce barriers to conducting empirical VR research and improve the quantity of VR user studies, regardless of laboratory availability.

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Section: Study Ii: Hand Trackingmentioning

confidence: 99%

Conducting Unsupervised Virtual Reality User Studies Online

Mottelson

Petersen

Lilija

et al. 2021

Front. Virtual Real.

Self Cite

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“…Start set B contains prominent publications (over 100 citations on Google Scholar) that fit our scope: Just Follow Me [58], ShadowGuides [18], LightGuide [51], YouMove [1] and Physio@Home [53]. Start set C contains publications within the past 5 years on XR-based motion guidance across a broad research spectrum, including: Kodama et al [32] on training using virtual co-embodiment, Zhou et al [61] on motion guidance with an MR mirror, Lilija et al [39] on correction on virtual hand avatar movements, Yu et al [59] on the influence of perspective in motion guidance, and Dürr et al [14] on the virtual appearance of feedforward.…”

Section: Methodology Of Literature Reviewmentioning

confidence: 99%

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

Yu,

Lee,

Sedlmair

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Figure 1: Schematic representations of motion feedforward. The leftmost column shows the complete motion sequence and its critical postures at four critical moments (Start, Point1, Point2, and End). The remaining columns show how the feedforward of this motion can be represented using different configurations of level of indirection (explicit, implicit and abstract) and interactive update strategy (discrete, continuous and autonomous). The visualizations in blue represent the feedforward; while the the black glyphs show the trainee's four moments during training from top to bottom (Start, reach Point 1, between Point 1 and Point 2, and between Point 2 and End); the green parts visualize the completed parts of a motion when using Implicit guidance (column 4 and 5). Discrete guidance (column 2, 4 and 6) presents cues for the next critical posture, which updates only when that posture is satisfied. Continuous guidance (column 3, 5 and 7) presents cues for the next frame in the motion sequence at all times. Autonomous guidance is not shown as it operates independently of the trainee's actions.

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“…For example, in an online study conducted using Unity WebGL3 (with desktop-only set-ups), to simulate AR visualisations on virtual globes, hardware screening criterion was used to ensure memory requirements [51]. Similarly, for HMD based studies, technical parameters of participants personal devices were screened [5,24,35]. For example, Lilija et al [35] quality checked personal Oculus Quest headsets for tracking fidelity, using jitter as a proxy for quality.…”

Section: Participantsmentioning

confidence: 99%

“…Similarly, for HMD based studies, technical parameters of participants personal devices were screened [5,24,35]. For example, Lilija et al [35] quality checked personal Oculus Quest headsets for tracking fidelity, using jitter as a proxy for quality.…”

Section: Participantsmentioning

confidence: 99%

“…Despite researchers sometimes conducting studies across multiple AR/VR headset platforms, with Marrinan and Papka [38] developing a WEB-XR application capable of running on all HMDs with a compatible browser, in many instances, recruiting methods were restricted to a single device. For example, HMD-based studies were often limited to Oculus HMDs [5,35,44,58], or; on a single occasion, a Magic Leap One [36]). Although limiting and screening participants personal devices will likely improve accuracy and consistency of the data captured, it may also reduce the number and variation of participants that can be included in the study.…”

Section: Participantsmentioning

confidence: 99%

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Socially Distanced: Have user evaluation methods for Immersive Technologies changed during the COVID-19 pandemic?

Spittle

Frutos-Pascual

et al. 2021

2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)

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Since the emergence of COVID-19 in late 2019, there has been a significant disturbance in human-to-human interaction that has changed the way we conduct user studies in the field of Human-Computer Interaction (HCI), especially for extended (augmented, mixed, and virtual) reality (XR). To uncover how XR research has adapted throughout the pandemic, this paper presents a review of user study methodology adaptations from a corpus of 951 papers. This corpus of papers covers CORE 2021 A* published conference submissions, from Q2 2020 through to Q1 2021 (IEEE ISMAR, ACM CHI, IEEE VR). The review highlights how methodologies were changed and reported; sparking discussions surrounding how methods should be conveyed and to what extent research should be contextualised, by drawing on external topical factors such as COVID-19, to maximise usefulness and perspective for future studies. We provide a set of initial guidelines based on our findings, posing key considerations for researchers when reporting on user studies during uncertain and unprecedented times.

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Correction of Avatar Hand Movements Supports Learning of a Motor Skill

Cited by 14 publications

References 26 publications

Conducting Unsupervised Virtual Reality User Studies Online

Conducting Unsupervised Virtual Reality User Studies Online

Design Space of Visual Feedforward And Corrective Feedback in XR-Based Motion Guidance Systems

Socially Distanced: Have user evaluation methods for Immersive Technologies changed during the COVID-19 pandemic?

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