Affiliation:
1. Information Technology Department, Faculty of Computers & Informatics, Zagazig University, Zagazig 44519, Egypt
2. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abstract
Background: In our current digital world, smartphones are no longer limited to communication but are used in various real-world applications. In the healthcare industry, smartphones have sensors that can record data about our daily activities. Such data can be used for many healthcare purposes, such as elderly healthcare services, early disease diagnoses, and archiving patient data for further use. However, the data collected from the various sensors involve high dimensional features, which are not equally helpful in human activity recognition (HAR). Methods: This paper proposes an algorithm for selecting the most relevant subset of features that will contribute efficiently to the HAR process. The proposed method is based on a hybrid version of the recent Coronavirus Disease Optimization Algorithm (COVIDOA) with Simulated Annealing (SA). SA algorithm is merged with COVIDOA to improve its performance and help escape the local optima problem. Results: The UCI-HAR dataset from the UCI machine learning repository assesses the proposed algorithm’s performance. A comparison is conducted with seven well-known feature selection algorithms, including the Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Reptile Search Algorithm (RSA), Zebra Optimization Algorithm (ZOA), Gradient-Based Optimizer (GBO), Seagull Optimization Algorithm (SOA), and Coyote Optimization Algorithm (COA) regarding fitness, STD, accuracy, size of selected subset, and processing time. Conclusions: The results proved that the proposed approach outperforms state-of-the-art HAR techniques, achieving an average performance of 97.82% in accuracy and a reduction ratio in feature selection of 52.7%.
Funder
Princess Nourah bint Abdulrahman University
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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