Skeleton clustering by multi-robot monitoring for fall risk discovery
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- 作者：Yutaka Deguchi ; Daisuke Takayama…
- 关键词：Skeleton clustering ; Human monitoring ; Mobile robots ; Service ; oriented DSMS
- 刊名：Journal of Intelligent Information Systems
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：48
- 期：1
- 页码：75-115
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Information Storage and Retrieval; Data Structures, Cryptology and Information Theory; Artificial Intelligence (incl. Robotics); IT in Business; Document Preparation and Text Processing;
- 出版者：Springer US
- ISSN：1573-7675
- 卷排序：48
文摘
This paper tackles the problem of discovering subtle fall risks using skeleton clustering by multi-robot monitoring. We aim to identify whether a gait has fall risks and obtain useful information in inspecting fall risks. We employ clustering of walking postures and propose a similarity of two datasets with respect to the clusters. When a gait has fall risks, the similarity between the gait which is being observed and a normal gait which was monitored in advance exhibits a low value. In subtle fall risk discovery, unsafe skeletons, postures in which fall risks appear slightly as instabilities, are similar to safe skeletons and this fact causes the difficulty in clustering. To circumvent this difficulty, we propose two instability features, the horizontal deviation of the upper and lower bodies and the curvature of the back, which are sensitive to instabilities and a data preprocessing method which increases the ability to discriminate safe and unsafe skeletons. To evaluate our method, we prepare seven kinds of gait datasets of four persons. To identify whether a gait has fall risks, the first and second experiments use normal gait datasets of the same person and another person, respectively. The third experiments consider that how many skeletons are necessary to identify whether a gait has fall risks and then we inspect the obtained clusters. In clustering more than 500 skeletons, the combination of the proposed features and our preprocessing method discriminates gaits with fall risks and without fall risks and gathers unsafe skeletons into a few clusters.