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A daily activity feature extraction approach based on time series of sensor events

1 Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China
2 School of Information Science & Technology, Dalian Maritime University, Dalian 116026, China
3 Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong 643000, China

Activity recognition benefits the lives of residents in a smart home on a daily basis. One of the aims of this technology is to achieve good performance in activity recognition. The extraction and selection of the daily activity feature have a significant effect on this performance. However, commonly used extraction of daily activity features have limited the performance of daily activity recognition. Based on the nature of the time series of sensor events caused by daily activities, this paper presents a novel extraction approach for daily activity feature. First, time tuples are extracted from sensor events to form a time series. Subsequently, several common statistic formulas are proposed to form the space of daily activity features. Finally, a feature selection algorithm is employed to generate final daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on four different classifiers. The results of the experiment reveal that the proposed approach is an improvement over the commonly used approach.
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© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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