Automatic Bike Fitting System

Automated data-driven bike fitting using Bioracer Motion Sensing Data

The project
Background
Advantages
Goals
Senior Researcher
The project

To solve the expert subjectivity problem, and to improve the overall fitting process, we have started a research project to develop a methodology to perform an automated bike fitting based on a novel data-driven decision-making processes.

In a first phase, the automated bike fitting system will assist bike-fitters in their fitting process and advise them about the saddle height, fore and aft positioning based on objective features.

The final goal of our project is to have a fully autonomous bike fitting system, which can fit a cyclist with sufficient accuracy in a short period of time.

Background

An easy to use bike fitting procedure is a must to keep cyclists injury free and motivated. Current bike fitting processes however are very labor intensive and subjective as they are based on the experience of a bike-fitting expert.

A bike fit can take up to three hours and is always an interplay between comfort, performance and injury prevention. Every bike fitter has their own vision and own points of attention, hence why the cycling position is based on their prior experience and education.

Cycling is a biomechanical movement which indicates that there should be certain features to objectively determine the optimal cycling position.

Advantages

This project will be revolutionary in the cycling industry as less experience will be needed to properly fit a cyclist on his bike. Bike retailers and big sports enterprises will be able to fit a cyclist on a new bike without knowledge of the human body or biomechanics as the algorithm will guide them through the fitting process.

Our system will be easy to use, and more time efficient than current alternatives. The cost will be greatly reduced and more cyclists can be fitted within a short space of time.

Goals

The first goal is to determine the “optimal” cycling position, with the expert features in mind. Afterwards, we want to find new features using machine learning. The end goal is to have a commercially available system that can be used by any bike shop, a system that is easy to use and adds to the existing bike fit experience. Ideally, every cyclist would get a bike fit and ride injury free to enjoy their sport even more.

Senior Researcher
Prof. Steven Verstockt

Contact us for more information about this project

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