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All Journal Journal of Applied Engineering and Technological Science (JAETS)
Phung Cong Phi Khanh
Hanoi National University of Education
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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A Cost-Effective Real-Time Human Activity Recognition System Using Supervised Learning Algorithms and Wearable Acceleration Sensors Nguyen Thi Thu; Phung Cong Phi Khanh; Trong-Minh Hoang; Duc-Tan Tran; Nguyen Ngoc Linh; Nguyen Canh Minh
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6830

Abstract

Human activity recognition (HAR) plays a vital role in health monitoring by providing detailed insights into daily movements. This study aims to enhance HAR by developing a lightweight and efficient machine learning model that balances accuracy, real-time performance, and affordability. Using acceleration data from a wearable inertial sensor, we extracted a novel feature set optimized for computational efficiency. The proposed model was evaluated on a benchmark dataset, achieving an accuracy of 98.9%, in classifying six essential daily activities: walking, walking upstairs, walking downstairs, laying, sitting, and standing. These results demonstrate the model’s potential for real-time health monitoring applications, offering a cost-effective and deployable solution for wearable-based activity recognition.
Co-Authors Duc-Tan Tran Nguyen Canh Minh Nguyen Ngoc Linh Nguyen Thi Thu Trong-Minh Hoang