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Physiological Work Overload Assessment for Highly Flexible Fine-Motory Assembly Tasks Using Machine Learning

Markus BrillingerSamuel ManfrediDominik LederMartin BloderMarkus JägerKonrad DiwoldAmer KajmakovicMichael HaslgrüblerRudolf PichlerMartin BrunnerStefan MehrViktorijo Malisa

出版	SSRN, 2023
URL	http://books.google.com.hk/books?id=nRZO0AEACAAJ&hl=&source=gbs_api

註釋

The complexity and demands of assembly tasks in production have been found to increase cognitive load in assembly workers. This leads to physical stress effects induced by work overload. To determine how assembly tasks can be assessed for stress effects, the authors conducted a study using wearable sensors to measure heart rate and heart rate variability. The study showed that heart rate and heart rate variability, along with questioning of the assembly workers, is a valid process for stress detection and classification. The authors used the machine learning algorithms, Random Forest and K-NearestNeighbours, to analyze heart rate and heart rate variability. These algorithms were able to distinguish between assembly task and rest phase, as well as between an easy and hard type of assembly tasks, which is a significant novelty of this paper.