Development and Validation of Accelerometer-Based Machine Learning Models for Classifying Walking, Running, and Jumping Activities

Quantifying mechanical loading during daily physical activities is essential for designing and evaluating bone health interventions. Accelerometers are a promising tool for estimating these loads under free-living conditions, yet existing prediction models depend on prior knowledge of the activity being performed. This study developed and validated machine learning models to automatically distinguish between walking, running, and jumping using accelerometer data. Forty-eight healthy adults completed a protocol of walking, running, and jumping tasks while wearing ActiGraph GT9X Link accelerometers at the ankle, lower back, and hip. Three algorithms (Random Forest, Support Vector Machine, and K-Nearest Neighbors) were trained and evaluated through multiple performance metrics. All models achieved excellent classification accuracy across sensor placements, with percent agreement between 93.8% and 97.7%, receiver operating characteristic area under the curve values consistently above 0.97, and Kappa coefficients exceeding 0.89. These results demonstrate that accelerometer-based activity classification can reliably differentiate walking, running, and jumping, establishing a practical framework for applying activity-specific mechanical loading prediction equations under free-living conditions.