Rule-Driven Feedback Engine for Psychomotor Learning: From Motion Distance to Error Detection

October 19th, 2023

This master’s thesis aims to advance the field of psychomotor learning within the context of Project MILKI-PSY, a research initiative focusing on multimodal immersive mentoring to facilitate Self-Regulated Learning (SRL) for psychomotor training. Psychomotor learning involves the acquisition of skills through the integration of physical and cognitive processes. In this project, we are developing an innovative framework that leverages immersive technologies to enhance SRL, offering a unique opportunity to revolutionize the way individuals acquire psychomotor skills.

Thesis Type
  • Master
Aliaksandra Nekhviadovich
Presentation room
Seminar room I5 6202
Stefan Decker
Michal Slupczynski

Project MILKI-PSY stands at the forefront of technology-driven pedagogy, using immersive experiences to mentor learners. Our approach incorporates diverse sensor technologies, including motion capture and AI-driven analytics, to create a holistic learning environment. One crucial component of this project is the development of a rule-driven feedback engine to provide learners with immediate, personalized guidance during psychomotor tasks. This thesis contributes to this aspect of the project by focusing on extending the feedback mechanism.

Existing research in psychomotor learning has primarily concentrated on motion analysis, quantifying differences in expert and novice performance. However, integrating this information into a real-time feedback system that considers “motion distance” and “error in feedback” presents an exciting challenge. Various studies have explored elements of this problem, but there is a clear need for a comprehensive rule-driven feedback system, aligning with self-regulated learning principles.

  • Unity (C#)
  • Python
  • (optional) Kubernetes