Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model

Sheikh Badar ud din Tahir, Abdul Basit Dogar, Rubia Fatima, Affan Yasin, Muhammad Shafiq, Javed Ali Khan, Muhammad Assam, Abdullah Mohamed, El Awady Attia

Research output: Contribution to journalArticlepeer-review

Abstract

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

Original languageEnglish
Article number6632
JournalSensors
Volume22
Issue number17
DOIs
Publication statusPublished - Sept 2022

Keywords

  • Hilbert–Huang transform (HHT)
  • human physical activity recognition (HPAR)
  • inertial measurement unit (IMU)
  • stochastic gradient descent (SGD)

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