Abstract
Divers methods to train motions exist in multiple domains such as sports, rehabilitation or in industrial use cases. With regard to findings considering imitation learning in real world scenarios and social interaction guidelines in extended realities (XR), in this paper the transferability of these real world effects to AR is investigated within an assembly scenario. On that basis, a comparative user study (N = 12) was conducted analysing implicit imitation learning as well as the impact on the performance. Therefore, besides the measurement of the completion time, motion data are captured and a first analysis of the data is conducted. Whereas in terms of the cumulative completion time, no significant differences between trained and untrained subjects can be measured, the avatar’s motion was imitated differently between the groups. More precise, for the similarity of motion pattern between the avatar’s motion and the motion of the subjects it was shown that the untrained imitated the motions significantly more often than the trained, but both groups imitated in general. The results provide a first insight of the possibilities and limits of imitation learning in AR. With respect to the specific assembly use case, an avatar assistance method could enhance the learning, considering performance parameters and could lead to an implicit imitation of ergonomic motion patterns while having free cognitive capacity for conducting the specific processes.
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The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany (MOSIM project, grant no. 01IS18060A-H).
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Lampen, E., Liersch, M., Lehwald, J. (2020). Towards Motor Learning in Augmented Reality: Imitating an Avatar. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-60703-6_23
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