Open AccessA daily activity feature extraction approach based on time series of sensor events
Author(s) -
Yong Liu,
Hong Yang,
Shi-Cai Gong,
Ya Qing Liu,
Xing Zhong Xiong
Publication year - 2020
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2020280
Subject(s) - activity recognition , activities of daily living , computer science , feature extraction , statistic , feature selection , artificial intelligence , pattern recognition (psychology) , feature (linguistics) , selection (genetic algorithm) , series (stratigraphy) , data mining , machine learning , statistics , mathematics , psychology , paleontology , linguistics , philosophy , psychiatry , biology
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.