Abstract
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%.
Highlights
The widespread use of mobile and smart devices has increased the demand for various smart home and Internet of Things (IoT) applications [1]
Different techniques have been used for human activity recognition (HAR), such as computer vision methods [7,8,9] that use cameras to track human motion and actions, and wearable devices that should be carried by users, such as wearable sensors [10], smartwatches [11], and smartphones [12,13]
We propose a new feature selection (FS) method to improve the HAR system using the hybridization of two algorithms, namely the gradient-based optimizer (GBO) and grey wolf optimizer (GWO)
Summary
Ahmed Mohamed Helmi 1,2 , Mohammed A.
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