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.
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.