In this paper, we propose two new instability features, a data pre-processing method, and a new evaluation method for skeleton clustering by autonomous mobile robots for subtle fall risk discovery. We had proposed an autonomous mobile robot which clusters skeletons of a monitored person for distinct fall risk discovery and achieved promising results. A more natural setting posed us problems such as ambiguities in class labels and low discrimination power of our original instability features between safe/unsafe skeletons. We validate our three new proposals through evaluation by experiments. © 2014 Springer International Publishing.
CITATION STYLE
Deguchi, Y., & Suzuki, E. (2014). Skeleton clustering by autonomous mobile robots for subtle fall risk discovery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8502 LNAI, pp. 500–505). Springer Verlag. https://doi.org/10.1007/978-3-319-08326-1_51
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