A concept for acquiring clinical expert motion data for digital twins and humanoid robots

Abstract

This work presents a novel concept for capturing and curating expert clinical motion intelligence (“kinesthetics”) to enhance nursing education and training. By recording optimal movement sequences using motion capture suits and wireless body area sensor networks with synchronized IMUs, essential tacit knowledge is preserved. These data are condensed into digital motion twins, creating virtual models that simulate expert actions and support dynamic, person-specific training. Humanoid robots can then use these motion twins to provide realistic, responsive practice environments. The approach emphasizes the importance of relational, context-aware movements and aims to make education more accessible and effective. Ensuring data protection, ethical standards, and solutions for individual variability is crucial. Ultimately, this concept supports the responsible integration of technology into nursing, strengthening bodily relational practice rather than replacing it