{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:18:02Z","timestamp":1772151482749,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T00:00:00Z","timestamp":1662076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013frequency 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.<\/jats:p>","DOI":"10.3390\/s22176632","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2459-1610","authenticated-orcid":false,"given":"Sheikh Badar ud din","family":"Tahir","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan"}]},{"given":"Abdul Basit","family":"Dogar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}]},{"given":"Rubia","family":"Fatima","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0166-2239","authenticated-orcid":false,"given":"Affan","family":"Yasin","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3724-6528","authenticated-orcid":false,"given":"Muhammad","family":"Shafiq","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Qujing Normal University, Qujing 655011, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3306-1195","authenticated-orcid":false,"given":"Javed Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Science and Technology, Bannu 28100, Pakistan"}]},{"given":"Muhammad","family":"Assam","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Science and Technology, Bannu 28100, Pakistan"}]},{"given":"Abdullah","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Research Centre, Future University in Egypt, New Cairo 11745, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2703-1969","authenticated-orcid":false,"given":"El-Awady","family":"Attia","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16273, Saudi Arabia"},{"name":"Mechanical Engineering Department, Faculty of Engineering (Shoubra), Benha University, Cairo 11629, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107561","DOI":"10.1016\/j.patcog.2020.107561","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Min","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99152","DOI":"10.1109\/ACCESS.2019.2927134","article-title":"A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data","volume":"7","author":"Gumaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","unstructured":"Kwapisz, J.R., Weiss, G.M., and Moore, S.A. 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