Integrated Biomechanical Motion Analysis in a Virtual Cycling Environment Using Wearable Sensors

Biomechanical motion analysis in a virtual cycling environment through the inertial measurement units (IMU) forms a specific approach to movement assessment, integrating accelerometric and gyrometric sensors. The paper provides comprehensive data for evaluating physical activity, monitoring rehabilitation exercises, assessing neurological conditions, and detecting cardiological abnormalities. The dataset comprises 50 experiments and recordings from five distinct virtual cycling tours with varying altitude profiles, collectively spanning over 1,100 kilometers. The proposed methodology includes automated segmentation of cycling routes based on slope variation, extraction of statistical and frequency features from physiological, accelerometric, and gyrometric signals, and their subsequent classification using signal processing and computational intelligence techniques. Analysis of 3,526 segmented intervals revealed significant correlations between heart rate variations and slope gradients, as well as estimations of motion symmetry coefficients relevant to biomechanical assessment. The classification accuracy reached 95.5% for motion and physiological features, and 85.6% for gyrometric data using the two-layer neural network model across different slope conditions. The findings demonstrate the potential of hybrid systems combining wearable sensors and virtual environments for advanced motion analysis. This work underscores the applicability of general-purpose digital signal processing methods and machine learning algorithms in the multichannel analysis of physiological data, with applications in neurology, rehabilitation, and telemedicine.