Abstract
This study investigated activity during a joint Simon task in a VR environment to develop an analysis tool for collaboration teams using Metaverse software. In this pilot study, we distinguished between competition and cooperation during the task and explored the relationship between the classification model and performance on the joint Simon task. This study consisted of two phases: creation of the body movement classification model and adaptation of the model to the joint Simon task. In Phase 1, data from 6 participants (three pairs) were used to construct a machine-learning classification model. The other two participants provided test data for the model. Using random forest models, we classified two categories of pair movements: cooperation (synchronization) or competition. This model yielded an accuracy rate of 88.8% in classifying the test data. In Phase 2, as a case study, we applied this model to the joint Simon task. The results suggested that competition elicited better performance than cooperation. In conclusion, the classification model successfully distinguished subtle movements in the VR environment. This model could be used to analyze the state of pairs during collaborative tasks.
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Arima, Y., Harada, Y. (2023). Competition or Cooperation: Classification in a VR Environment Based on Sensor Data. In: Takada, H., Marutschke, D.M., Alvarez, C., Inoue, T., Hayashi, Y., Hernandez-Leo, D. (eds) Collaboration Technologies and Social Computing. CollabTech 2023. Lecture Notes in Computer Science, vol 14199. Springer, Cham. https://doi.org/10.1007/978-3-031-42141-9_10
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DOI: https://doi.org/10.1007/978-3-031-42141-9_10
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