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Counting Bicep Reps

AIM

To track and count bicep curls in real time using computer vision techniques with OpenCV and Mediapipe's Pose module.

This project does not use a specific dataset as it works with real-time video from a webcam.

https://drive.google.com/file/d/13Omm8Zy0lmtjmdHgfQbraBu3NJf3wknw/view?usp=sharing

LIBRARIES NEEDED

LIBRARIES USED
  • OpenCV
  • Mediapipe
  • NumPy

DESCRIPTION

What is the requirement of the project?

  • The project aims to provide a computer vision-based solution for tracking fitness exercises like bicep curls without the need for wearable devices or sensors.
Why is it necessary?
  • Helps fitness enthusiasts monitor their workouts in real time.
  • Provides an affordable and accessible alternative to wearable fitness trackers.
How is it beneficial and used?
  • Real-time feedback on workout form and repetition count.
  • Can be extended to other fitness exercises and integrated into fitness apps
How did you start approaching this project? (Initial thoughts and planning)
  • Explored Mediapipe's Pose module for pose landmark detection.
  • Integrated OpenCV for video frame processing and real-time feedback.
  • Planned the logic for detecting curls based on elbow angle thresholds.
Mention any additional resources used (blogs, books, chapters, articles, research papers, etc.).
  • Mediapipe official documentation.
  • OpenCV tutorials on video processing.

EXPLANATION

DETAILS OF THE DIFFERENT FEATURES

- Pose Estimation: Utilized Mediapipe's Pose module to detect key landmarks on the human body.
- Angle Calculation: Calculated angles at the elbow joints to determine curl movement.
- Rep Tracking: Incremented rep count when alternating between full curl and relaxed positions.
- Real-Time Feedback: Displayed the remaining curl count on the video feed.

PROJECT WORKFLOW

Initial setup:

  • Installed OpenCV and Mediapipe.
  • Set up a webcam feed for video capture.

Pose detection:

  • Used Mediapipe's Pose module to identify body landmarks.

Angle calculation:

  • Implemented a function to calculate the angle between shoulder, elbow, and wrist.

Rep detection:

  • Monitored elbow angles to track upward and downward movements.

Real-time feedback:

  • Displayed the remaining number of curls on the video feed using OpenCV.

Completion:

  • Stopped the program when the target reps were completed or on manual exit.

PROJECT TRADE-OFFS AND SOLUTIONS

  • Accuracy vs. Simplicity:
    • Using elbow angles alone may not handle all body postures but ensures simplicity.
    • Solution: Fine-tuned angle thresholds and added tracking for alternating arms.
  • Real-Time Performance vs. Model Complexity:
    • Mediapipe's lightweight solution ensured smooth processing over heavier models.

SCREENSHOTS

  1. Entering no of reps you want to perform Screenshot 2025-01-19 184454

  2. Performing reps Screenshot 2025-01-19 184607

Project workflow

graph LR  
A[Webcam Feed] --> F[Enter No of Biceps Reps]
F --> B[Mediapipe Pose Detection]  
B --> C[Elbow Angle Calculation]  
C --> D[Rep Count Decrement]  
D --> E[Real-Time Update on Frsame]

CONCLUSION

KEY LEARNINGS

Insights gained from the data

  • Real-time video processing using OpenCV.
  • Pose detection and landmark analysis with Mediapipe.
Improvements in understanding machine learning concepts
  • Understanding geometric computations in pose analysis.
  • Effective use of pre-trained models like Mediapipe Pose.
Challenges faced and how they were overcome
  • Challenge: Handling incorrect postures.
    • Solution: Fine-tuning angle thresholds.

USE CASES

Personal Fitness Tracker
- Helps users track their workouts without additional equipment.

Fitness App Integration
- Can be integrated into fitness apps for real-time exercise tracking.