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In manufacturing sectors such as textiles and electronics, manual processesare a fundamental part of production. The analysis and monitoring of theprocesses is necessary for efficient production design. Traditional methods foranalyzing manual processes are complex, expensive, and inflexible. Compared toestablished approaches such as Methods-Time-Measurement (MTM), machine learning(ML) methods promise: Higher flexibility, self-sufficient & permanent use,lower costs. In this work, based on a video stream, the current motion class ina manual assembly process is detected. With information on the current motion,Key-Performance-Indicators (KPIs) can be derived easily. A skeleton-basedaction recognition approach is taken, as this field recently shows majorsuccess in machine vision tasks. For skeleton-based action recognition inmanual assembly, no sufficient pre-work could be found. Therefore, a MLpipeline is developed, to enable extensive research on different (pre-)processing methods and neural nets. Suitable well generalizing approaches arefound, proving the potential of ML to enhance analyzation of manual processes.Models detect the current motion, performed by an operator in manual assembly,but the results can be transferred to all kinds of manual processes.

Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes