ArticleEntropy (Basel, Switzerland)2021
A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Wind Turbine Blade Fault Diagnosis Integrating Multi-Scale Enhanced Hierarchical Fuzzy Entropy, Isolation Forest and GWO-GRU.Entropy (Basel, Switzerland) · 2026Article
- Deep learning-based feature selection for detection of autism spectrum disorder.Frontiers in artificial intelligence · 2025Article
- Nature-Inspired Intelligent Computing: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Enhancing human activity recognition for the elderly and individuals with disabilities through optimized Internet-of-Things and artificial intelligence integration with advanced neural networks.Frontiers in neuroinformatics · 2024Article
- Human Activity Recognition Using Hybrid Coronavirus Disease Optimization Algorithm for Internet of Medical Things.Sensors (Basel, Switzerland) · 2023Article
- Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems.Scientific reports · 2023Article
- The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis.Biosensors · 2022Article
- Human Activity Recognition: Review, Taxonomy and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
- A Novel Reformed Reduced Kernel Extreme Learning Machine with RELIEF-F for Classification.Computational intelligence and neuroscience · 2022Article
- Multiclass feature selection with metaheuristic optimization algorithms: a review.Neural computing & applications · 2022Review
- Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
6 authors.
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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%.
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