Transformer-Enabled Smartphone System for Intelligent Physical Activity Monitoring
Leping Zhang, Fengjiao Jiang, Guopeng Jia, Yue Wang · International Journal of Advanced Computer Science and Applications · 2025
This study addresses the prevalent decline in physical activity among university students in the contemporary information society, proposing an innovative deep learning-based framework for intelligent physical activity recognition. Central to this framework is the comprehensive utilization of high-precision Inertial Measurement Units (IMUs) integrated within smartphones, encompassing triaxial accelerometers, gyroscopes, and magnetometers, enabling multi-dimensional, real-time capture of students' daily activity postures. For algorithmic design, this research transcends traditional limitations by adopting the more advanced Transformer architecture as its core classifier. Through the distinct self-attention mechanism inherent to this architecture, the proposed method efficiently and precisely extracts critical spatiotemporal features from vast sensor data, thereby achieving accurate identification and classification of various physical activities, such as walking, running, and climbing stairs. Rigorous evaluation results demonstrate significant advantages in key performance metrics, including recognition accuracy, when compared to conventional recurrent neural networks (e.g., Long Short-Term Memory networks, Recurrent Neural Networks) and classic machine learning algorithms (e.g., Random Forest), with a validation accuracy reaching 93.97%. This forward-looking research outcome not only provides a reliable and efficient technological means for monitoring the physical activity status of university students but also establishes a robust data foundation for the future development and implementation of targeted health intervention measures.