Key result
Machine learning methods including neural networks, support vector machines, and k-nearest neighbors classified selected cycling features with an accuracy between 91.3% and 98.6%.
Machine learning methods can accurately classify cycling features using multimodal GPS and heart rate data acquired during long-term physical activities.
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High-accuracy ML classification of cycling features is feasible; hypothesis-generating for cardiovascular monitoring, requires prospective validation.
This paper presents methodology for the processing of GPS and heart rate signals acquired during long-term physical activities. The data analysed include geo-positioning and heart rate multichannel signals recorded for 272.2 h of cycling across the Andes mountains over a 5694-km long expedition. The proposed computational methods include multimodal data de-noising, visualization, and analysis in order to determine specific biomedical features. The results include the correspondence between the heart rate and slope for downhill and uphill cycling and the mean heart rate evolution on flat segments: a regression coefficient of - 0 . 014 bpm/h related to time and 6 . 3 bpm/km related to altitude. The classification accuracy of selected cycling features by neural networks, support vector machine, and k-nearest neighbours methods is between 91.3% and 98.6%. The proposed methods allow the analysis of data during physical activities, enabling an efficient human–machine interaction.
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Procházka et al. (2017) studied Physical activities (cycling). Machine learning classification methods (neural networks, SVM, k-NN) was evaluated on Classification accuracy of selected cycling features. Machine learning methods including neural networks, support vector machines, and k-nearest neighbors classified selected cycling features with an accuracy between 91.3% and 98.6%.
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