Virtual Cycling Tours

Biomechanical motion analysis based on signals acquired in the virtual cycling environment integrated with data from additional accelerometric sensors provide information important for evaluation of physical activities, monitoring of rehabilitation exercises, assessment of neurological disorders, and detection of cardiological problems. Datasets include analysis of 50 experimental runs and data recorded during 5 selected tours with different altitude profiles, that form more than 1000 km long cycling route together. The proposed methodology includes segmentation of cycling routes into segments with different slopes, evaluation of associated means of motion and physiological signals, and their classification. Results based on 3526 segment include estimation of coefficients of symmetry in selected slope segments. Applications of this research point to the use of wearable sensors in hybrid systems for motion analysis in the virtual environment. Conclusions present possibilities of general digital signal processing methods and computational intelligence use for analysis of multichannel signals in the multidisciplinary areas.