GPS-based analysis of physical activities using positioning and heart rate cycling data

Abstract This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features...
Ausführliche Beschreibung

Abstract This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features extraction, their classification and visualization. Real data were acquired from 41 cycling rides of the same 11.48-km long route divided into 2460 segments of approximately 60 s. The data were recorded with a varying sampling period within the range of 1–22 s depending on the route profile. The pre-processing stage included preparatory analysis, filtering and resampling of the data to a constant sampling rate. The proposed algorithm includes the evaluation of the cross-correlation between the heart rate and the altitude gradient as recorded by a GPS satellite system. A Bayesian approach was then applied to classify the cycling segment features into two classes (specifying cycling up and down) with the classification accuracy better than 93 %. A comparison with other classification methods is presented in the paper as well. The results include the following relationships: (1) the heart rate and altitude gradient, which shared a positive correlation coefficient of 0.62; (2) the heart rate and speed, which shared a negative correlation coefficient of −0.72 over all of the analysed segments; and (3) the mean heart rate change delay (6.8–11.5 s) in relation to the changes in the altitude gradients associated with cycling up and down. The paper forms a contribution to the use of computational intelligence and visualization for data processing both in cycling and fitness physical activities as well. Ausführliche Beschreibung