Real-time track cycling performance prediction using ANFIS system

References

• Andrić, J. M., & Lu, D.-G.-G. (2017). Fuzzy methods for prediction of seismic resilience of bridges. International Journal of Disaster Risk Reduction, 22(January), 458–468.
   Google Scholar
• Axelrod, Y. K., & Diringer, M. N. (2008). Temperature management in acute neurologic disorders. Neurologic Clinics, 26(2), 585–603.
   PubMed Web of Science ®Google Scholar
• Bini, R. R., Hume, P. A., & Crofta, J. L. (2011). Effects of saddle height on pedal force effectiveness. Procedia engineering, 13, 51–55.
   Google Scholar
• Bini, R. R., Tamborindeguy, A. C., & Mota, C. B. (2010). Effects of saddle height, pedaling cadence, and workload on joint kinetics and kinematics during cycling. Journal of Sport Rehabilitation, 19(3), 301–314. Retrieved from https://www.researchgate.net/publication/46110797_Effects_of_Saddle_Height_Pedaling_Cadence_and_Workload_on_Joint_Kinetics_and_Kinematics_During_Cycling
   PubMed Web of Science ®Google Scholar
• Bland, J., Pfeiffer, K., & Eisenmann, J. C. (2012). The PWC170: Comparison of diVerent stage lengths in 11–16 year olds. European Journal of Applied Physiology, 112(5), 1955–1961.
   Google Scholar
• Blej, M., & Azizi, M. (2016). Comparison of Mamdani-type and Sugeno-type fuzzy inference systems for fuzzy real time scheduling. International Journal of Applied Engineering Research, 11(22), 11071–11075.
   Google Scholar
• Chang, F.-J., & Chang, Y.-T. (2006). Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Advances in Water Resources, 29(1), 1–10.
   Web of Science ®Google Scholar
• Dewan, M. W., Huggett, D. J., Warren Liao, T., Wahab, M. A., & Okeil, A. M. (2016). Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Materials & Design, 92, 288–299.
   Web of Science ®Google Scholar
• Grunau, B. E., Wiens, M. O., & Brubacher, J. R. (2010). Dantrolene in the treatment of MDMA-related hyperpyrexia: A systematic review. Canadian Journal of Emergency Medicine, 12(5), 435–442.
   Google Scholar
• Jones, W. D. (2006). Taking body temperature, inside out [body temperature monitoring]. IEEE Spectrum, 43(1), 13–15.
   Google Scholar
• Kazeminezhad, M. H., Etemad-Shahidi, A., & Mousavi, S. J. (2005). Application of fuzzy inference system in the prediction of wave parameters. Ocean Engineering, 32(14–15), 1709–1725.
   Google Scholar
• Majdar, H. A., & Vafakhah, M. (2016). Monthly river flow prediction using adaptive neuro-fuzzy inference system (a case study: Gharasu Watershed, Ardabil Province-Iran). Ecopersia, 3(4), 1175–1188.
   Google Scholar
• Makinson, T. J., & Malkinson, T. J. (2002). Skin temperature response during cycle ergometry. Electrical and computer engineering, 2002. IEEE CCECE 2002. Canadian conference on (Vol. 2, pp. 1123–1128). Winnipeg, MB: IEEE. doi:10.1109/CCECE.2002.1013105
   Google Scholar
• Marx, J., Hockberger, R., & Walls, R. (2013). Rosen’s emergency medicine: Concepts and clinical practice (8th ed., p. 2239). Philadelphia, PA: Saunders.
   Google Scholar
• Maughan, R. J., Otani, H., & Watson, P. (2012). Influence of relative humidity on prolonged exercise capacity in a warm environment. European Journal of Applied Physiology, 112(6), 2313–2321.
   PubMed Web of Science ®Google Scholar
• Neshat, M., & Adeli, A. (2011). A comparative study on ANFIS and fuzzy expert system models for concrete mix design. International Journal of Computer Science Issues, 8(3), 196–210.
   Google Scholar
• Ozgoren, M., Sakar, M., Oniz, A., Özgören, M., Şakar, M., Öniz, A., … Fak, T. (2010). Contact/non-contact sensor mesh for body temperature monitoring. In Biomedical Engineering Meeting (BIYOMUT), 2010 15th National (pp. 1–4). Antalya, Turkey: IEEE. doi:10.1109/BIYOMUT.2010.5479808
   Google Scholar
• Peveler, W., Bishop, P., Smith, J., Richardson, M., & Whitehorn, E. (2005). Comparing methods for setting saddle height in trained cyclists. Journal of Exercise Physiology Online, 8(1), 51–55.
   Google Scholar
• Polderman, K. H., Mayer, S. A., & Menon, D. (2008). Hypothermia therapy after traumatic brain injury in children. The New England Journal of Medicine, 359(11), 1178–1180.
   Google Scholar
• Ranković, G., Mutavdžić, V., Toskić, D., Preljević, A., Kocić, M., Nedin-Ranković, G., & Damjanović, N. (2010). Aerobic capacity as an indicator in different kinds of sports. Bosnian Journal of Basic Medical Sciences, 10(1), 44–48.
   Google Scholar
• Rexhepi, A. M., & Brestovci, B. (2014). Prediction of vo2max based on age, body mass, and resting heart rate. Human Movement, 15(1), 56–59.
   Google Scholar
• Ross, T. J. (2004). Fuzzy logic with engineering applications(2nd ed.). Hoboken, NJ: John Wiley & Sons. doi:10.1002/9781119994374
   Google Scholar
• Saikia, H., Bhattacharjee, D., & Lemmer, H. H. (2012). Predicting the performance of bowlers in IPL: An application of artificial neural network. International Journal of Performance Analysis in Sport, 12(1), 75–89.
   Web of Science ®Google Scholar
• Salman AbdulWahed, M., & Seno Ismat, N. (2012). A comparison of Mamdani and Sugeno inference systems for a satellite image classification. Anbar Journal for Engineering Sciences, مجلة الأنبار للعلوم الهندسية, المؤتمر ال(العدد الخاص-الجزء الثاني), 296–306. doi: 10.1109/WAC.2006.376033
   Google Scholar
• Sanches, J. M., Pereira, B., & Paiva, T. (2012). Headset bluetooth and cell phone based continuous central body temperature measurement system. Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, (Vol. 2010, pp. 2975–2978). Buenos Aires, Argentina: IEEE. doi:10.1109/IEMBS.2010.5626172
   Google Scholar
• Schoeman, R., Coetzee, D., & Schall, R. (2017). Analysis of super rugby from 2011 to 2015. International Journal of Performance Analysis in Sport, 17(3), 190–201.
   Web of Science ®Google Scholar
• She, J., Nakamura, H., Makino, K., Ohyama, Y., & Hashimoto, H. (2014). Selection of suitable maximum-heart-rate formulas for use with Karvonen formula to calculate exercise intensity. International Journal of Automation and Computing, 12(1), 62–69.
   Google Scholar
• Shleeg, A., Aishalaa, M., & Ellabib, I. (2013). Comparison of Mamdani and Sugeno fuzzy interference systems for the breast cancer risk. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 7(No. 10), 695–699.
   Google Scholar
• Somanathan, L., Khalil, I., Sornanathan, L., & Khalil, I. (2010). Fitness monitoring system based on heart rate and SpO2 level. Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on (pp. 1–5). doi:10.1109/ITAB.2010.5687666
   Google Scholar
• Stangier, C., Abel, T., Hesse, C., Claen, S., Mierau, J., Hollmann, W., & Struder, H. K. (2015). Effects of cycling vs. running training on endurance performance in preparation for inline speed skating. J Strength Cond Res, (October), 41–49. doi:10.1519/JSC.0000000000001247
   Google Scholar
• Sudin, S., Md Shakaff, A. Y., Aziz, F., Ahmad Saad, F. S., Zakaria, A., & Salleh, A. F. (2016). Track cyclist performance monitoring system using wireless sensor network. In N. A. Yacob, M. Mohamed, & M. A. K. Megat Hanafiah (Eds.), Regional conference on science, technology and social sciences (RCSTSS 2014): Science and technology (pp. 123–131). Singapore: Springer Singapore. doi:10.1007/978-981-10-0534-3_12
   Google Scholar
• Sudin, S., Shakaff, A. Y. M., Aziz, F., Salleh, A. F., Zakaria, A., & Saad, F. S. A. (n.d.). Development of a track cyclist performance monitoring system using wireless sensor technology. MoHE 2014.
   Google Scholar
• Tümer, A. E., & Koçer, S. (2017). Prediction of team league ’ s rankings in volleyball by artificial neural network method. International Journal of Performance Analysis in Sport, 17(3), 202–211.
   Web of Science ®Google Scholar
• UCI. (2016). What you should know about velodrome. Retrieved from http://www.uci.ch/track/news/article/what-you-should-know-about-velodromes/
   Google Scholar
• Vandergriendt, C. (2018). What is the Normal Body Temperature Range?
   Google Scholar
• Wang, G., Liu, R., Liu, G., Zhang, B., & Liu, F. (2010). Experimental study on the effect of pressure on human skin temperature and heart rate. Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on (pp. 1–4). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-77953295032&partnerID=40&md5=b7eb9039bee9d156ecd15d70bda572df
   Google Scholar
• Xiao, Q. (2017). Time series prediction using bayesian filtering model and fuzzy neural networks. Optik - International Journal for Light and Electron Optics, 140, 104–113.
   Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
   Google Scholar
• Zhao, J., Lorenzo, S., An, N., Feng, W., Lai, L., & Cui, S. (2013). Effects of heat and different humidity levels on aerobic and anaerobic exercise performance in athletes. Journal of Exercise Science and Fitness, 11(1), 35–41.
   Web of Science ®Google Scholar
• Zounemat-Kermani, M., & Teshnehlab, M. (2008). Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Applied Soft Computing, 8(2), 928–936.
   Google Scholar