A Desktop application built with Python, Tkinter, and DeepLabCut to track barbell and bodyweight kinematics. Designed for researchers, coaches, and athletes to monitor Speed, Range of Motion (ROM), and Force-Velocity Profiling.
- Multi-Modal Tracking: Support for both Powerlifting (Barbell) and Calisthenics (Bodyweight) movements.
- Video Analysis: Automated trajectory and speed tracking with smart scaling.
- Live Webcam Mode: Real-time feedback using your PC's camera with on-screen smoothing (work in progress).
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Force-Velocity Profiling (FVP): Multi-load analysis to determine theoretical
$F_0$ ,$V_0$ , and profile slope. - Smart Repetition Detection: Automatic detection of eccentric and concentric phases with a manual editor.
- Data Export: Save your results and figures in JSON or CSV format for external analysis.
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Biomechanical Metrics:
- Mean & Peak Velocity (
$m/s$ ). - Vertical Range of Motion (
$cm$ ). - Real-time Trajectory Mapping.
- Mean & Peak Velocity (
The system generates a labeled video showing the bar path or joint trajectory.
| Main Menu | Biomechanical Metrics | Force-Velocity Profiling |
|---|---|---|
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The application utilizes custom-trained DeepLabCut models (MobileNetV2 architecture).
- Tracking: * Powerlifting: Tracks the center of the barbell and the edges of the plate.
- Calisthenics (Alpha): Tracks wrist, elbow, and shoulder.
- Calibration: * For Barbell: Uses the standard 45cm plate to calculate a
pixels-to-metersscale.- For Calisthenics: Uses an anatomical scaling factor (forearm length estimated at 16% of user height).
- Physics Engine: Data is filtered using a Butterworth Lowpass Filter (via
scipy) to remove measurement noise before calculating velocity.
To run this project, you need a Python environment (3.8 - 3.10 recommended, I used Python 3.8.18).
git clone [https://github.com/LeFaillerFrancois/app_vbt.git](https://github.com/LeFaillerFrancois/app_vbt.git)
cd app_vbtpython -m venv venvname# On Windows
venvname\Scripts\activate# On Mac/Linux
source venvname/bin/activatepip install -r requirements.txtpython main.py-
Go to Video Processing.
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choose between the Powerlifting or Calisthenics model depending on your workout type (check).
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Select a side-view video (mp4).
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The software will resize the video and run the DeepLabCut inference (Progress Bar included).
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View results and edit reps if needed via the integrated Rep Editor.
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NEW : Export: Use the "Save" buttons to export data as CSV/JSON or save the generated figures.
Even if Github isn't made for that you can find test videos with the excepted results of the analysis in the test_video file.
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Position your camera perpendicular to the barbell.
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Click Start Webcam.
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The real-time smoothing algorithm will track the bar even with minor camera shake.
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Collect CSV files from multiple trials with different weights.
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In the Multiple File Page, add each CSV and enter the corresponding load (kg).
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Click Compute Profile to generate your linear regression (
$Load = Velocity \times Slope + F_0$ ) and displays the graph.
- NEW: Calisthenics Model: Currently in Alpha. Trained on limited data (pull-ups/dips); accuracy may vary.
- Camera Placement: Camera must be strictly perpendicular (side-view) to the barbell. A tilted camera will distort the ROM measurements.
- Environment: Try to avoid having other plates in the background to prevent tracking interference.
- Hardware: Analysis is faster with an NVIDIA GPU, but compatible with CPU-only machines.
- Data Export: Save analysis reports as PDF or Excel.
- Power Output: Calculate Watts based on athlete's body weight + bar load and other metrics.
- Cloud Sync: Store athlete profiles over time. ?
- TFLite Integration: Lightweight inference for faster CPU performance.
Developed by Le Failler François. Feel free to reach out for suggestions or bugs!