Abstract
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.
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Deguchi, Y., Suzuki, E. (2014). Skeleton Clustering by Autonomous Mobile Robots for Subtle Fall Risk Discovery. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_51
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DOI: https://doi.org/10.1007/978-3-319-08326-1_51
Publisher Name: Springer, Cham
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