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Computer Vision & Imaging AI
Motion & Pose Analysis
Fitness & Wellness

Camera-Based Form Coaching with Motion and Pose Analysis for a Strength Studio Franchise


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Problem

One coach, twelve lifters

A 35-studio boutique strength-training franchise ran small-group classes where a single coach supervised up to twelve members lifting simultaneously. Form errors on squats and deadlifts went uncorrected for whole sessions, rep counts were self-reported and unreliable, and members had no objective record of progress. New-joiner injuries and early cancellations were both trending upward across the network.

Solution

Real-time pose feedback system

We built a camera-based motion and pose analysis system for each lifting bay. A pose-estimation model tracks joint angles through every rep, counts sets automatically, and surfaces colour-coded form flags on the bay's display so coaches intervene where it matters most. Post-session summaries with rep quality and load progression sync to the member app, and studio-level dashboards roll up to the franchisor.

Measurable Impact

What changed after launch

Rep-count accuracy of 96% against coach manual counts during the 4-studio validation pilot

Form corrections delivered per class rose 2.4x once flags directed coach attention

Six-month member retention improved from 61% to 71% in equipped studios versus unequipped

All 35 studios live on the system within 8 months of the first pilot

Tech & Tools Used

What powered the build

Python
PyTorch
MediaPipe Pose
OpenCV
NVIDIA Jetson Orin
TensorRT
Node.js
PostgreSQL
React Native
AWS S3 + IoT Core

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