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• W3095432913 abstract "Health Smart Home (HSH) is an important part of smart city. This technology provides a new kind of remote medical treatment, and can effectively alleviate the shortage of medical resources caused by aging population and help elderly people live at home more safely and independently. Activity recognition is the core of Health Smart Home. However, constructing activity recognition models usually requires a large amount of labeled data, which imposes a heavy burden on manual labeling. In this article, the authors propose an activity labeling approach based on a graph-based semi-supervised learning algorithm. This approach can divide the raw sensor event sequence without any label information into appropriate segments. Consecutive sensor events that occurred in a same activity are grouped into a same segment. In addition, this approach requires only a small number of manually labeled segments to complete the labeling of the remaining large number of unlabeled segments, thereby greatly reducing the burden of manual labeling. After that, all the labeled data can be further used for activity recognition in smart homes. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed activity labeling approach." @default.
• W3095432913 created "2020-11-09" @default.
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• W3095432913 date "2020-01-01" @default.
• W3095432913 modified "2023-10-17" @default.
• W3095432913 title "Graph-Based Semi-Supervised Learning for Activity Labeling in Health Smart Home" @default.
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• W3095432913 doi "https://doi.org/10.1109/access.2020.3033589" @default.
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