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In Human Activity Recognition (HAR) supervised and semi-supervised training are important tools for devising parametric activity models. For the best modelling performance, typically large amounts of annotated sample data are required. Annotating often represents the bottleneck in the overall modelling process as it usually involves retrospective analysis of experimental ground truth, like video footage. These approaches typically neglect that prospective users of HAR systems are themselves keydoi:10.1109/cit/iucc/dasc/picom.2015.170 dblp:conf/IEEEcit/MiuMP15 fatcat:id3fuxeo5bdvpe3wsbunqy55ba