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Open AccessArticle

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

1
Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 01 Zlín, Czech Republic
2
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic
3
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic
4
Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1523; https://doi.org/10.3390/s20051523
Received: 12 February 2020 / Revised: 4 March 2020 / Accepted: 5 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Intelligent Sensors for Monitoring Physical Activities)
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 3 , 8 and 8 , 15 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification. View Full-Text
Keywords: multimodal signal analysis; computational intelligence; machine learning; motion monitoring; accelerometers; classification multimodal signal analysis; computational intelligence; machine learning; motion monitoring; accelerometers; classification
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MDPI and ACS Style

Charvátová, H.; Procházka, A.; Vyšata, O. Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis. Sensors 2020, 20, 1523.

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