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Teaching psychomotor skills using machine learning for error detection

Benjamin Paaßen; Milos Kravcik
In: Roland Klemke; Khaleel Asyraaf Mat Sanusi; Daniel Majonica; Anja Richert; Valérie Varney; Tobias Keller; Jan Schneider; Daniele Di Mitri; George-Petru Ciordas-Hertel; Fernando P. Cardenas-Hernandez; Gianluca Romano; Milos Kravcik; Benjamin Paaßen; Ralf Klamma; Michal Slupczynski; Stefanie Klatt; Mai Geisen; Tobias Baumgartner; Nina Riedl (Hrsg.). Proceedings of the 1st International Workshop on Multimodal Immersive Learning Systems (MILeS 2021). International Workshop on Multimodal Immersive Learning Systems (MILeS-2021), located at 16th European Conference on Technology Enhanced Learning (EC-TEL 2021), September 20-24, virtual (Bozen-Bolzano), Italy, Pages 8-14, CEUR Workshop Proceedings, 10/2021.


Learning psychomotor skills is challenging because motion is fast, relies strongly on subconscious mechanisms, and instruction typically disrupts the activity. As such, learners would profit from mechanisms that can react swiftly, raise subtle mistakes to the conscious level, and do not disrupt the activity. In this paper, we sketch a machine learning-supported approach to provide feedback in two example scenarios: running, and interacting with a robot. For the running case, we provide an evaluation how motions can be compared to highlight deviations between student and expert motion.


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