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The assessment of presence and performance in an AR environment for motor imitation learning: A case-study on violinists

Campo A., Michałko A., Van Kerrebroeck B., Stajic B., Pokric M., & Leman, M. (2023)

Computers in Human Behavior, 146


Abstract:

The acquisition of advanced gestures is a challenge in various domains of proficient sensorimotor performance. For example, orchestral violinists must move in sync with the lead violinist's gestures. To help train these gestures, an educational music play-back system was developed using a HoloLens 2 simulated AR environment and an avatar representation of the lead violinist. This study aimed to investigate the impact of using a 2D or 3D representation of the lead violinist's avatar on students' learning experience in the AR environment.

To assess the learning outcome, the study employed a longitudinal experiment design, in which eleven participants practiced two pieces of music in four trials, evenly spaced over a month. Participants were asked to mimic the avatar's gestures as closely as possible when it came to using the bow, including bowing, articulations, and dynamics. The study compared the similarities between the avatar's gestures and those of the participants at the biomechanical level, using motion capture measurements, as well as the smoothness of the participants' movements. Additionally, presence and perceived difficulty were assessed using questionnaires.

The results suggest that using a 3D representation of the avatar leads to better gesture resemblance and a higher experience of presence compared to a 2D representation. The 2D representation, however, showed a learning effect, but this was not observed in the 3D condition. The findings suggest that the 3D condition benefits from stereoscopic information that enhances spatial cognition, making it more effective in relation to sensorimotor performance. Overall, the 3D condition had a greater impact on performance than on learning.

This work concludes with recommendations for future efforts directed towards AR-based advanced gesture training to address the challenges related to measurement methodology and participants' feedback on the AR application.




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