期刊
ENTROPY
卷 23, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/e23081065
关键词
human activity recognition; feature selection; gradient-based optimizer; grey wolf optimizer; metaheuristic
Human activity recognition (HAR) is crucial in various real-world applications, and a proposed efficient HAR system using lightweight feature selection method called GBOGWO has been developed to enhance classification accuracy. Experimental results show that GBOGWO significantly improves classification accuracy, with an average accuracy of 98%.
Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human-computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.
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