Cycling segments multimodal analysis and classification using neural networks

Procházka, Aleš; Vaseghi, Saeed; Charvátová, Hana; Ťupa, Ondřej; Vyšata, Oldřich

dc.title	Cycling segments multimodal analysis and classification using neural networks	en
dc.contributor.author	Procházka, Aleš
dc.contributor.author	Vaseghi, Saeed
dc.contributor.author	Charvátová, Hana
dc.contributor.author	Ťupa, Ondřej
dc.contributor.author	Vyšata, Oldřich
dc.relation.ispartof	Applied Sciences (Switzerland)
dc.identifier.issn	2076-3417 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2017
utb.relation.volume	7
utb.relation.issue	6
dc.type	article
dc.language.iso	en
dc.publisher	Molecular Diversity Preservation International (MDPI)
dc.identifier.doi	10.3390/app7060581
dc.relation.uri	http://www.mdpi.com/2076-3417/7/6/581/htm
dc.subject	GPS data acquisition	en
dc.subject	heart rate analysis	en
dc.subject	neural computing	en
dc.subject	visualization	en
dc.subject	computational intelligence	en
dc.subject	classification	en
dc.subject	human-machine interaction	en
dc.description.abstract	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. © 2017 by the authors.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1007421
utb.identifier.obdid	43876825
utb.identifier.scopus	2-s2.0-85020253439
utb.identifier.wok	000404449800057
utb.source	j-scopus
dc.date.accessioned	2017-09-08T12:14:54Z
dc.date.available	2017-09-08T12:14:54Z
dc.description.sponsorship	Cancer Research UK
dc.rights	Attribution 4.0 International
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.rights.access	openAccess
utb.contributor.internalauthor	Charvátová, Hana
utb.fulltext.affiliation	Aleš Procházka 1,2,, Saeed Vaseghi 1, Hana Charvátová 3, Ondřej Ťupa 1 and Oldřich Vyšata 1,2,4 1 Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic; [email protected] (S.V.); [email protected] (O.T.); [email protected] (O.V.) 2 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic 3 Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 05 Zlín, Czech Republic; [email protected] 4 Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Prague, Czech Republic Correspondence: [email protected]; Tel.: +420-220-444-198 Academic Editor: Christos Bouras
utb.fulltext.dates	Received: 21 April 2017; Accepted: 31 May 2017; Published: 4 June 2017
utb.fulltext.references	1. Fister, I., Jr.; Ljubic, K.; Suganthan, P.N.; Fister, I. Computational intelligence in sports: Challenges and opportunities within a new research domain. Appl. Math. Comput. 2015, 262, 178–186. 2. Arduini, A.; Gomez-Cabrera, M.C.; Romagnoli, M. Reliability of different models to assess heart rate recovery after submaximal bicycle exercise. J. Sci. Med. Sport 2011, 14, 352–357. 3. Charvátová, H.; Procházka, A.; Vaseghi, S.; Vyšata, O.; Janáčová, D.; Líška, O. Physiological and GPS Data Fusion. In Proceedings of the International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Prague, Czech Republic, 29–30 October 2015; pp. 1–4. 4. Fasel, B.; Sporri, J.; Gilgien, M.; Boffi, G.; Chardonnens, J.; Muller, E.; Aminian, K. Three-Dimensional Body and Centre of Mass Kinematics in Alpine Ski Racing Using Differential GNSS and Inertial Sensors. Remote Sens. 2016, 8, 671. 5. Bucher, S.S.; Supej, M.; Sandbakk, O.; Holmberg, H.C. Downhill turn techniques and associated physical characteristics in cross-country skiers. Scand. J. Med. Sci. Sports 2014, 24, 708–716. 6. Hurst, H.T.; Swarén, M.; Hébert-Losier, K.; Ericsson, F.; Sinclair, J.; Atkins, S.; Homlberg, H.C. GPS-Based Evaluation of Activity Profiles in Elite Downhill Mountain Biking and the Influence of Course Type. J. Sci. Cycl. 2013, 2, 25–32. 7. Formenti, D.; Trecroci, A.; Cavaggioni, L. Heart rate response to a marathon cross-country skiing race: A case study. Sport Sci. Health 2014, 11, 125–128. 8. Mekik, C.; Arslanoglu, M. Investigation on Accuracies of Real Time Kinematic GPS for GIS Applications. Remote Sens. 2009, 1, 22–35. 9. Gilgien, M.; Sporri, J.; Limpach, P.; Geiger, A.; Müller, E. The Effect of Different Global Navigation Satellite System Methods on Positioning Accuracy in Elite Alpine Skiing. Sensors 2014, 14, 18433–18453. 10. Erden, F.; Velipasalar, S.; Alkar, A.Z.; Cetin, A.E. Sensors in Assisted Living: A survey of signal and image processing methods. IEEE Signal Process. Mag. 2016, 33, 36–44. 11. Ahmad, F.; Cetin, A.E.; Ho, K.C.; Nelson, J. Signal Processing for Assisted Living: Developments and Open Problems. IEEE Signal Process. Mag. 2016, 33, 25–26. 12. Bang, Y.; Kim, J.; Yu, K. An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services. Sensors 2016, 16, 1768. 13. Maddison, R.; Ni Mhurchu, C.; Cavaggioni, L. Global Positioning System: A new opportunity in physical acivity measurement. Int. J. Behav. Nutr. Phys. Act. 2009, 6, 73. 14. Damani, A.; Shah, H.; Shah, K.; Vala, M. Global Positioning System for Object Tracking. Int. J. Comput. Appl. 2015, 109, 40–45. 15. Drawil, N.M.; Amar, H.M.; Basir, O.A. GPS localization accuracy classification: A context-based approach. IEEE Trans. Intell. Transp. Syst. 2013, 14, 262–273. 16. Schmid, A. Positioning Accuracy Improvement with Differential Correlation. IEEE J. Sel. Top. Signal Process. 2009, 3, 587–598. 17. Wang, H.; Ou, J.; Yuan, Y. Strategy of Data Processing for GPS Rover and Reference Receivers Using Different Sampling Rates. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1144–1149. 18. Whyte, G.P.; George, K.; Shave, R.; Middleton, N.; Nevill, A.M. Training Induced Changes in Maximum Heart Rate. Int. J. Sports Med. 2008, 29, 129–133. 19. Procházka, A.; Vaseghi, S.; Yadollahi, M.; Ťupa, O.; Mareš, J.; Vyšata, O. Remote Physiological and GPS Data Processing in Evaluation of Physical Activities. Med. Biol. Eng. Comput. 2013, 52, 301–308. 20. Charvátová, H.; Procházka, A.; Vaseghi, S.; Vyšata, O.; Vališ, M. GPS-based Analysis of Physical Activities Using Positioning and Heart Rate Cycling Data. Signal Image Video Process. 2016, 1–8, doi:10.1007/s11760-016-0928-z. 21. Procházka, A.; Vyšata, O.; Vališ, M.; Ťupa, O.; Schatz, M.; Mařík, V. Use of Image and Depth Sensors of the Microsoft Kinect for the Detection of Gait Disorders. Neural Comput. Appl. 2015, 26, 1621–1629. 22. Muro-de-la-Herran, A.; Garcia-Zapirain, B.; Mendez-Zorrilla, A. Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications. Sensors 2014, 14, 3362–3394. 23. Procházka, A.; Vyšata, O.; Vališ, M.; Ťupa, O.; Schatz, M.; Mařík, V. Bayesian Classification and Analysis of Gait Disorders Using Image and Depth Sensors of Microsoft Kinect. Digit. Signal Process. 2015, 47, 169–177. 24. Shi, G.; Wang, Y.; Li, S. Human Motion Capture System and its Sensor Analysis. Sens. Transducers 2014, 172, 206–212. 25. Ťupa, O.; Procházka, A.; Vyšata, O.; Schatz, M.; Mareš, J.; Vališ, M.; Mařík, V. Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect. Biomed. Eng. Online 2015, 14, 97. 26. Hostalkova, E.; Vysata, O.; Prochazka, A. Multi-dimensional biomedical image de-noising using Haar transform. In Proceedings of the 15th International Conference on Digital Signal Processing, Cardiff, UK, 1–4 July 2007; Sanei, S., Chambers, J, Eds.; Cardifff University: Cardiff, UK, 2007; pp. 175–178. 27. Jerhotová, E.; Švihlík, J.; Procházka, A. Biomedical Image Volumes Denoising via the Wavelet Transform. In Applied Biomedical Engineering; Gargiulo, G.D., McEwan, A., Eds.; INTECH: Rijeka, Croatia, 2011; pp. 435–458. 28. Vaseghi, S. Advanced Signal Processing and Digital Noise Reduction; Wiley & Teubner: West Sussex, UK, 2000. 29. Bishop, C.M. Pattern Recognition and Machine Learning; Springer: Cambridge, UK, 2006. 30. Procházka, A.; Vyšata, O.; Ťupa, O.; Mareš, J.; Vališ, M. Discrimination of Axonal Neuropathy Using Sensitivity and Specificity Statistical Measures. Neural Comput. Appl. 2014, 25, 1349–1358. 31. Lin, I.-C.; Peng, J.-Y.; Lin, C.-C.; Tsai, M.-H. Adaptive Motion Data Representation with Repeated Motion Analysis. IEEE Trans. Vis. Comput. Graph. 2011, 17, 527–538. 32. Theodoridis, S.; Koutroumbas, K. Pattern Recognition; Academic Press: Cambridge, MA, USA, 2009. 33. Bouboulis, P.; Theodoridis, S.; Mavroforakis, C.; Evaggelatou-Dalla, L. Complex Support Vector Machines for Regression and Quaternary Classification. IEEE Trans. Neural Netw. Learn. Syst. 2015, 26, 1260–1274. 34. Kataria, A.; Singh, M.D. A Review of Data Classification Using k-Nearest Neighbour Algorithm. Int. J. Emerg. Technol. Adv. Eng. 2013, 3, 354–360. 35. Chen, H.; Wang, G.; Xue, J.H.; He, L. A novel hierarchical framework for human action recognition. Pattern Recognit. 2016, 55, 148–159.
utb.fulltext.sponsorship	All real data were kindly provided by Professor Saeed Vaseghi from his cycling across the Andes in the autumn of 2016. The physiological monitoring was done at the Department of Neurology of the Faculty hospital of the Charles University in Hradec Kralove. The whole project was in support of Cancer Research UK (www.justgiving.comfundraisingsaeedvaseghi).
utb.scopus.affiliation	Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Prague, Czech Republic; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic; Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, Prague, Czech Republic