ArticleJournal of ambient intelligence and humanized computing2023
Guided regularized random forest feature selection for smartphone based human activity recognition.
Article in Journal of ambient intelligence and humanized computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Ecological flow guarantee rate along the Xijiang River mainstream at different scales based on multiple probability distributions.Scientific reports · 2026Article
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- Digital Twin Prospects in IoT-Based Human Movement Monitoring Model.Sensors (Basel, Switzerland) · 2025Review
- Classification of User Behavior Patterns for Indoor Navigation Problem.Sensors (Basel, Switzerland) · 2025Article
- A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud.Scientific reports · 2025Article
- Single-nucleus transcriptomic analysis reveals the relationship between gene expression in oligodendrocyte lineage and major depressive disorder.Journal of translational medicine · 2024Article
- Human Activity Recognition via Score Level Fusion of Wi-Fi CSI Signals.Sensors (Basel, Switzerland) · 2023Article
- Enhancing COVID-19 tracking apps with human activity recognition using a deep convolutional neural network and HAR-images.Neural computing & applications · 2023Article
- Guided regularized random forest feature selection for smartphone based human activity recognition.Journal of ambient intelligence and humanized computing · 2023Article
- Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers.Sensors (Basel, Switzerland) · 2022Article
- Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer's Point of View.Sensors (Basel, Switzerland) · 2022Article
- Feature fusion using deep learning for smartphone based human activity recognition.International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 2021Article
- Clinical human activity recognition based on a wearable patch of combined tri-axial ACC and ECG sensors.Digital healthArticle
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2 authors.
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Abstract
Human activity recognition (HAR) is an eminent area of research due to its extensive scope of applications in remote health monitoring, sports, smart home, and many more. Smartphone-based HAR systems use high-dimensional sensor data to infer human physical activities. Researchers continuously endeavor to select pertinent and non-redundant features without compromising the classification accuracy. In this work, our aim is to build an efficient HAR model that not only extracts the most relevant features from the 3-axial accelerometer and gyroscope signal data but also enhances the classification accuracy of the HAR system, without data loss using time-frequency domain features. We propose a feature selection method based on guided regularized random forest (GRRF) to determine the most pertinent and non-redundant features to reduce the time to recognize the human activities efficiently. After selecting the most relevant features, a support vector machine (SVM) is used to identify various human physical activities. The UCI public dataset and a self-collected dataset are used to assess the generalization capability and performance of the proposed feature selection method. Eventually, the accuracy reached 99.10% and 99.30% on the self-collected and UCI HAR datasets, respectively.
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