3D load cell for measure force in a bicycle crank

This paper presents a new 3D instrumented crank prototype for characterization, analysis and validation in race bikes. System characterization throws a maximum linearity error of 0.30% for parallel projection transfer function. Uncertainty values for Monte Carlo method resulted smaller than classic method computations. An experiment was designed with four controlled factors where data obtained fallowed normal distribution. Symmetry and Cadence statistics (RMS, Mean and Variance) were used in ANOVA, showing that Symmetry on outdoor Environment was higher than indoor tests. Heaviest Subject presented greater symmetry, and the Gears increasing prove symmetry rising. The lightest Subject developed higher Cadence values, as also was developed on Indoor Environment. Greater speed was achieved for bigger gears. System variability was observed in ANOVA by Variance variable behavior. Routine programmed obtained useful graphs for sport training: Effective force, Torque and Power output symmetry analysis and Force 3D projection decomposition in crank arm analysis were done.