A Cost-Effective Real-Time Human Activity Recognition System Using Supervised Learning Algorithms and Wearable Acceleration Sensors

Nguyen Thi Thu (Hanoi University of Industry)
Phung Cong Phi Khanh (Hanoi National University of Education)
Trong-Minh Hoang (Posts and Telecommunications Institute of Technology)
Duc-Tan Tran (Phenikaa University)
Nguyen Ngoc Linh (Viet Nam National University)
Nguyen Canh Minh (University of Transport and Communications)

Journal of Applied Engineering and Technological Science (JAETS)
Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)

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.