Cyclist Performance Classification System based on Submaximal Fitness Test

Authors

  • S. Sudin School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia. Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Lot 16-21 Taman Muhibah, Jejawi-Permatang, 02600 Arau, Perlis, Malaysia.
  • A.Y. M. Shakaff School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia. Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Lot 16-21 Taman Muhibah, Jejawi-Permatang, 02600 Arau, Perlis, Malaysia.
  • A. Zakaria School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia. Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Lot 16-21 Taman Muhibah, Jejawi-Permatang, 02600 Arau, Perlis, Malaysia.
  • A. F. Salleh School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia.
  • F. S. A. Saad School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia. Centre of Excellence for Advanced Sensor Technology, Universiti Malaysia Perlis, Lot 16-21 Taman Muhibah, Jejawi-Permatang, 02600 Arau, Perlis, Malaysia.
  • A. H. Abdullah School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Tetap Pauh Putra, 02600 Arau, Perlis, Malaysia.

Keywords:

Astrand-Ryhming, Cyclist, Performance Monitoring, PWC.

Abstract

Performances among cyclist always measured by time traveled from start to finish line and then the winner in cycling event also decided by time or who crossed the finish line first. On the other hand, cyclist performance can be measured through cardiorespiratory and physical fitness, and this performance can be enhanced by proper training to increase fitness and skill without burden. A wireless sensor network (WSN) system developed by combined various sensing element to capture physiological and bicycle’s kinetics feedback. Physiological data such as heart rate variability (HRV) and kinetic data such as paddling power and cadence used as input in Astrand-Ryhming and PWC150 submaximal test to classify the performance group among cyclist. Developed HRV system using Photoplethysmography (PPG) provides the significant output with R2 value was 0.967. A group of 15 cyclists from three different backgrounds was used as a subject in this study. Maximal oxygen intake (VO2max) produced by AstrandRyhming test correlated with estimated paddling power produced by PWC150 test with P<0.01 and the R2 value was 0.8656. Discriminant analysis was 88.3% successfully classified cyclist into 3 group and group of trained and untrained cyclist clearly separated.

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Published

2018-05-29

How to Cite

Sudin, S., Shakaff, A. M., Zakaria, A., Salleh, A. F., Saad, F. S. A., & Abdullah, A. H. (2018). Cyclist Performance Classification System based on Submaximal Fitness Test. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-13), 73–78. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4124

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