Abstract
We propose a multi-view onboard clustering of skeleton data for fall risk discovery. Clustering by an autonomous mobile robot opens the possibility for monitoring older adults from the most appropriate positions, respecting their privacies, and adapting to various changes. Since the data that the robot observes is a data stream and communication network can be unreliable, the clustering method in this case should be onboard. Motivated by the rapid increase of older adults in number and the severe outcomes of their falls, we adopt Kinect equipped robots and focus on gait skeleton analysis for fall risk discovery. Our key contributions are new between-skeleton distance measures for risk discovery and two series of experiments with our onboard clustering. The experiments revealed several key findings for the method and the application as well as interesting outcomes such as clusters which consist of unexpected risky postures.
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Acknowledgments
A part of this research was supported by a Bilateral Joint Research Project between Japan and France funded by JSPS and CNRS (CNRS/JSPS PRC 0672), and JSPS KAKENHI 24650070 and 25280085.
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Takayama, D., Deguchi, Y., Takano, S., Scuturici, VM., Petit, JM., Suzuki, E. (2014). Multi-view Onboard Clustering of Skeleton Data for Fall Risk Discovery. In: Aarts, E., et al. Ambient Intelligence. AmI 2014. Lecture Notes in Computer Science(), vol 8850. Springer, Cham. https://doi.org/10.1007/978-3-319-14112-1_21
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DOI: https://doi.org/10.1007/978-3-319-14112-1_21
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