Launching today
MyBikeFitting
Free AI bike fitting via webcam or video
57 followers
Free AI bike fitting via webcam or video
57 followers
Professional bike fitting used to cost $200+ and a trip to the shop. MyBikeFitting does it in 5 minutes, from home, for free. Use your webcam, upload a video, or snap a photo. Our AI measures knee angle, hip angle, back angle & torso-thigh ratio — then gives you specific saddle and handlebar recommendations based on your riding style and pain points. 100% on-device. No account. No data sent to any server. Works for road, MTB, gravel & triathlon.
@elouan_mbf Congratulations on the launch! I'm not an avid cycler myself, but I do own a bicycle and have some knee pain from other sporting activities (or getting old? Perish the thought!) so I'll definitely be checking this out, and absolutely recommending this to my other friends who are more active bikers.
Very commendable that you're offering this product for no cost at all - is there a plan to monetise the product down the line, or are you just looking for feedback to improve the product?
@michael_nash2 It's just a PWYW model, nothing more. I have a full time job and I'm not looking to be an entrepreneur anywhere soon. I got tired of all the freemium services, so here you go, free stuff ;)
minimalist phone: creating folders
@elouan_mbf How accurate is it for that fitting? Because I see this as a huge investment (not only financial), but also for your health. If you chose wrong bicycle, you can harm yourself. What is the feedback from testing users so far?
@busmark_w_nika We had 239 reviews for 4.72/5 average. It's as accurate as an AI bike fitting can be. It's as the same level as other online bike fit but mybikefitting is free, so you can do as much as you want
On-device bike fitting will hit scale pain on pose jitter and camera-angle variance, which can swing knee/hip angles enough to give wrong saddle or reach recommendations.
Best practice is multi-frame smoothing plus confidence gating, camera calibration prompts (side-on, crank at 3 o’clock), and optionally ArUco or simple reference markers to estimate scale and bike geometry reliably.
How are you validating recommendations against known-fit datasets, and do you plan an “uncertainty score” or retake guidance when pose confidence is low?
@ryan_thill
Good points on the technical challenges! Here's what I've implemented so far:
Current validation pipeline:
First -> Brightness checks to filter out poor lighting conditions upfront
Then ->70% confidence threshold across all 33 keypoints for the analysis to proceed
And -> Data coherence checks that halt the analysis if there are too many inconsistencies between frames
I'm working on a tutorial showing users how to properly capture video and what works best (camera angle, positioning, lighting, etc.) and making clear guidance on setup to reduce variance before it becomes a problem.
I've made it very clear on the site that some bike fitting issues can't be solved with a simple bike fit alone. Setting proper expectations is key!