Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

Angesichts des zunehmenden Fachkräftemangels sind Gesundheit und Zufriedenheit der Beschäftigten von immer größerer Bedeutung für produzierende Unternehmen. Insbesondere bei variantenreichen Produkten müssen Werker und Werkerinnen komplexe Aufgaben erledigen. Diese Variantenvielfalt ist auch für die Produktionsfeinplanung eine Herausforderung. Die vorgestellte Heuristik hilft in einem realen Anwendungsfall Belastungsspitzen an getakteten Linien zu reduzieren und verbessert so die betriebliche Gesundheit. Given a shortage of skilled workers, employee health and satisfaction are of increasing importance to manufacturing companies. Particularly in the case of products with many variants, workers have to perform complex tasks. This diversity of variants is also a challenge for production planning. New planning paradigms help to reduce peaks of stress on flow production and thus contribute to occupational health.