Abstract
For the optimization of human-machine systems, it is necessary to gather knowledge about human-machine interaction. For power tools this knowledge is often built up in laboratory and field studies. A problem that often arises is the high amount of effort required for the attachment of measurement equipment, which makes it difficult to conduct large studies, especially in the field. The effort could be reduced by using a data logger, but it is not clear which measurement variables and record frequencies are relevant.
In this paper requirements for a data logger to capture the human-machine interaction are presented. Furthermore, a realization of a data logger and four suitable studies are presented. This allows large field studies to be carried out much more easily, making it possible to optimize power tools on the basis of big data collected in the field.
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Dörr, M., Peters, J., Matthiesen, S. (2021). Data-Driven Analysis of Human-Machine Systems – A Data Logger and Possible Use Cases for Field Studies with Cordless Power Tools. In: Ahram, T., Taiar, R., Langlois, K., Choplin, A. (eds) Human Interaction, Emerging Technologies and Future Applications III. IHIET 2020. Advances in Intelligent Systems and Computing, vol 1253. Springer, Cham. https://doi.org/10.1007/978-3-030-55307-4_9
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DOI: https://doi.org/10.1007/978-3-030-55307-4_9
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