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Artificial Intelligence · Major Project
Human Pose Estimation
Body keypoint tracking that counts exercise reps and checks form
2-3 weeks build
Overview
A workout tracker built on 33-point body pose. Joint angles are computed from keypoints every frame, and a per-exercise state machine counts squats, push-ups and bicep curls only when the full range of motion is reached. Form cues flag common faults such as a shallow squat, and sessions are logged to a small web dashboard.
What makes it stand up
- 0133-keypoint body pose from a single RGB camera
- 02Joint-angle maths with smoothing to steady noisy keypoints
- 03State-machine rep counting that ignores half reps
- 04Rule-based form cues for depth, alignment and tempo
- 05Session history with reps, sets and duration per exercise
Module breakdown
- 01
Pose tracking
MediaPipe Pose with visibility filtering on each landmark.
- 02
Angle engine
Three-point joint angles with exponential smoothing.
- 03
Rep counter
Up and down thresholds per exercise driving a state machine.
- 04
Form feedback
On-screen cues when angles leave the target range.
- 05
Dashboard
Flask API and React view of logged sessions.
After this, you will be able to
- Explain how joint angles are derived from 2D keypoints
- Defend your rep-counting thresholds against edge cases
- Demo live counting with a deliberate half rep rejected
Technology stack
- Python
- MediaPipe
- OpenCV
- Flask
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support