Version 1
: Received: 25 November 2017 / Approved: 27 November 2017 / Online: 27 November 2017 (05:38:58 CET)
How to cite:
Wister, M.A.; Pancardo, P.; Payró, P. Survey on Cycling Mobile Applications for Workout Performance. Preprints2017, 2017110170. https://doi.org/10.20944/preprints201711.0170.v1
Wister, M.A.; Pancardo, P.; Payró, P. Survey on Cycling Mobile Applications for Workout Performance. Preprints 2017, 2017110170. https://doi.org/10.20944/preprints201711.0170.v1
Wister, M.A.; Pancardo, P.; Payró, P. Survey on Cycling Mobile Applications for Workout Performance. Preprints2017, 2017110170. https://doi.org/10.20944/preprints201711.0170.v1
APA Style
Wister, M.A., Pancardo, P., & Payró, P. (2017). Survey on Cycling Mobile Applications for Workout Performance. Preprints. https://doi.org/10.20944/preprints201711.0170.v1
Chicago/Turabian Style
Wister, M.A., Pablo Pancardo and Pablo Payró. 2017 "Survey on Cycling Mobile Applications for Workout Performance" Preprints. https://doi.org/10.20944/preprints201711.0170.v1
Abstract
This article analyzes some available bike mobile applications for smartphones as an alternative to bike computers (Cycle Computers or speedometer or speed sensors). We have records of a large number of MTB (Mountain Bike) datasets, 219 datasets were recorded on 4 different routes. These applications create maps and profiles from geographic data. Inputs can be in GPS data (tracks and waypoints), driving routes, street addresses, or simple coordinates. Most applications estimate fields such as speed, heading, slope, distance, VMG (velocity made good) and pace (cadence). However, it is necessary to calculate the relationship between cadence and power in pedaling so that cyclists know the appropriate moment to apply power to their legs to improve the torque. This paper shows tables, comparative graphs, and performance evaluation of biking routes in four different cycling mobile applications.
Keywords
cycling computer; fitness and health statistics; bike computer; mobile sensing; social fitness network; bike mobile applications; wheeled vehicles; MTB datasets
Subject
Computer Science and Mathematics, Software
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
