Automatic generation of methods-time measurement analyses for assembly tasks from motion capture data using convolutional neuronal networks - A proof of concept

Abstract

This paper describes the research hypothesis that motion data can be utilized to derive MTM analyses. As a first step, manual assembly tasks are recorded with motion capture systems to generate motion data. These motion data are used as a training data set for an end-to-end deep learning architecture for motion classification. The result of this classification is the assignment of data sequences to corresponding basic motions of MTM-1. The paper also describes the prerequisites for an automatic generation of MTM analyses by considering an adaptation of the original MTM methodology to fit for an automatic approach, the acquisition of motion capture data and the automatic annotation of motion data.