Abstract
Analysis of human activity, e.g., by tracking and analyzing motion information or vital signs became lots of attention in medical as well as athletic appliances during the last years. Nonetheless, comprehensive and labeled datasets containing human motion information are only sparsely accessible to the public. Especially qualitatively labeled datasets are rare, although they are of great value for the development of concepts concerning qualitative motion assessment, e.g., to avoid injuries during athletic workouts or to optimize a training’s success.
Therefore, we provide an open and qualitative as well as quantitative labeled dataset containing acceleration and rotation data of 8 different body weight exercises, conducted by 26 study participants. It encompasses more than 11,000 exercise repetitions of which we extracted 8,576 into individual segments. We believe, that due to its structure and labeling our work is suitable to serve for development, benchmarking, and validation of new concepts for human activity recognition and qualitative motion assessment (Publication notes: The dataset will be published at http://github.com/andrebert/body-weight-exercises together with this paper’s presentation on the MobiHealth conference 2017, taking place in Vienna, 14–16 November.).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ebert, A., Marouane, C., Ungnadner, C., Klein, A. (2018). An Open, Labeled Dataset for Analysis and Assessment of Human Motion. In: Perego, P., Rahmani, A., TaheriNejad, N. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-98551-0_12
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DOI: https://doi.org/10.1007/978-3-319-98551-0_12
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