Optimizing Athlete Workload Monitoring with Supervised Machine Learning for Running Surface Classification Using Inertial Sensors
WenBin Zhu, Qianwei Zhang, SongYan Ni · International Journal of Advanced Computer Science and Applications · 2025
Monitoring athlete movement is important to improve performance, reduce fatigue, and decrease the likelihood of injury. Advanced technologies, including computer vision and inertial sensors, have been widely explored in classifying sport-specific movements. Combining automated sports action labeling with athlete-monitoring data provides an effective approach to enhance workload analysis. Recent studies on categorizing sport-specific movements show a trend toward training and evaluation methods based on individual athletes, allowing models to capture unique features peculiar to each athlete. This is particularly beneficial for movements that exhibit large variations in technique between athletes. The current study uses supervised machine learning models, including Neural Networks and Support Vector Machines (SVM), to distinguish between running surfaces, namely, athletics track, hard sand, and soft sand, using features extracted from an upper-back inertial measurement unit (IMU) sensor. Principal Component Analysis (PCA) is applied for feature selection and dimensionality reduction, enhancing model efficiency and interpretability. Our results show that athlete-dependent training approaches considerably enhance the classification performance compared to athlete-independent approaches, achieving higher weighted average precision, recall, F1-score, and accuracy (p < 0.05).