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.
Highlights
It is predicted that 70% of the world’s population will live in cities by 2050, so cities need to become smart by using information and communications technologies (ICTs) to make city services more aware, interactive and efficient [1], to meet the various requirements of such a large urban population for work, education and daily life
We propose a novel activity labeling approach based on a graph-based semi-supervised learning algorithm
In this article, we proposed an activity labeling approach in Health Smart Home based on the semi-supervised learning technique
Summary
It is predicted that 70% of the world’s population will live in cities by 2050, so cities need to become smart by using information and communications technologies (ICTs) to make city services more aware, interactive and efficient [1], to meet the various requirements of such a large urban population for work, education and daily life. We propose an activity labeling approach to support high-efficiency activity recognition This approach utilizes a segmentation technique to group consecutive sensor events that occur in a same activity into a same segment, which facilitates the feature extraction of different activities. This approach requires only a small number of manually labeled segments to realize labeling of the remaining large number of unlabeled segments, thereby greatly reducing the burden of manual labeling and improving the efficiency of activity recognition. The convergence results are used to predict the unknown category information of the remaining unlabeled segments, so as to realize the labeling of the entire training data set
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