Feasibility analysis of unsupervised industrial activity recognition based on a frequent micro action
Sprache des Titels:
Englisch
Original Buchtitel:
PETRA 2019: Proceedings of the 12th PErvasive Technologies Related to Assistive Environments Conference
Original Kurzfassung:
The ubiquity of wearable sensors has contributed a lot in human
activity recognition. Although activities of daily living have
been studied over the past decades, there is a lack of such
efforts on workers' activities in manufacturing industry. In
this paper, we look at a simple yet frequent task for
manufacturing, screwing, and investigate the ability to recognize
this repetitive task through wearable sensors and unsupervised
learning, by describing an experiment for industrial activity
recognition and showing how effective clustering analysis is in
detecting such a frequent micro action, by comparing different
techniques throughout a machine learning pipeline. The achieved
results demonstrated that unsupervised learning is a good
solution to deal with large amount of unlabelled sensory data. We
also show the different stages of pre-preprocessing, data
normalization, segmentation, dimensional reduction and algorithm
selection and how this affects the overall outcome by provide an
in depth view, on state of the art techniques, by applying it on
a relevant industrial problem.
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