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Project | MILKI-PSY

Duration:
Multimodal immersive learning with artificial intelligence for psychomotor skills

Multimodal immersive learning with artificial intelligence for psychomotor skills

Development of psychomotor skills (e.g. in medicine, sports) requires practical exercise, direct feedback and reflection. In order to achieve the desired learning success, individual support is essential.

The BMBF project MILKI-PSY aims to create AI-supported, data-intensive, multimodal, immersive learning environments for the independent training of psychomotor skills. This creates a cross-domain approach that makes it possible to record the activities of experts in a multimodal manner and to use these recordings as blueprints for learners. With the help of AI-supported analyzes, the learning progress is to be supported by automated error detection and generated, individual feedback.

This creates a holistic, innovative learning environment for learning psychomotor skills, in which individual learning processes are personalized based on complex data analyzes and supported by AI.

Partners

Technische Hochschule Köln Deutsches Forschungszentrum für Künstliche Intelligenz Rheinisch-Westfälisch Technische Hochschule Leibniz Institut für Bildungsforschung und Bildungsinformation Deutsche Sporthochschule Köln

Publications

  1. Visual and Tactile Information Provision in Golf: Conceptualization of a Training Application Incorporating Wearable Devices and Extended Reality

    Mai Geisen; Abhishek Samanta; Khaleel Asyraaf Mat Sanusi; Nghia Duong-Trung; Milos Kravcik; Stefanie Klatt

    In: 2024 21st International Conference on Information Technology Based Higher Education and Training. International Conference on Information Technology Based Higher Education and Training (ITHET-2024), November 6-8, Paris, France, ISBN 979-8-3315-1663-5, IEEE, 11/2024.
  2. IMPECT-POSE: A Complete Front-end and Back-end Architecture for Pose Tracking and Feedback

    Abhishek Samanta; Hitesh Kotte; Patrick Handwerk; Khaleel Asyraaf Mat Sanusi; Mai Geisen; Milos Kravcik; Nghia Duong-Trung

    In: UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. International Conference on User Modeling, Adaptation, and Personalization (UMAP-2024), July 1-4, Cagliari, Italy, Pages 142-147, ISBN 979-8-4007-0466-6, ACM, New York, NY, United States, 6/2024.

Sponsors

BMBF - Federal Ministry of Education and Research

16DHB4014

BMBF - Federal Ministry of Education and Research