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Ilir Aliu presented a deadlift form-tracking prototype that uses multiple vision…

2026-02-16 · 19:41 UTC ·IlirAliu_ ·1 min read

Brief

Ilir Aliu presented a deadlift form-tracking prototype that uses multiple vision models to estimate spinal curvature and bar path from video. The system segments the lifter, detects the barbell plate, uses pose landmarks to constrain analysis, and generates plots such as barbell Y displacement and a back-roundness heatmap for rep-by-rep lifting assessment.

Source evidence

title: @IlirAliu_: For all my lifters: computer vision app to measure back curvature during deadlif...
author: IlirAliu_
contenttype: twitterpost
published: 2026-02-16T19:41:23+00:00
sourceurl: https://x.com/IlirAliu/status/2023482861815570738

word_count: 115

Tweet by @IlirAliu_

For all my lifters: computer vision app to measure back curvature during deadlift! main technical highlights: — RF-DETR (Roboflow) to segment the person (great performance out-the-box with no additional training!) — YOLO11n (Ultralytics) for bounding box prediction around the barbell weight plate (trained on my own small dataset). — Mediapipe (Google) for pose landmark detection to guide the bounds for the line fitting. — Custom logic to fit a line across the back. about the plots on the right side: The Back Roundness Map shows the deviation of the estimated back curve from a line of best fit. Credit: Jeremy Park —— if it matters in AI or Robotics you'll see it here first: http://22astronauts.com

Posted: 2026-02-16T19:41:23.000Z
Engagement: 2306 likes, 213 retweets, 64 replies