exercise-classification

An extension of the previous 'Fitness-AI-Coach': a complete web application with real-time exercise recognition and counting. The exercise recognition model achieves 99% accuracy on the test set and 95% and 90% on two additional external datasets. || Give a star ⭐ to the repository if it was useful. Thank you! 😊

End-to-end Machine Learning pipeline for wearable sensor data featuring preprocessing, feature engineering, PCA, FFT, outlier detection, and repetition counting for barbell exercises.

This project focuses on exercise classification using the ResNet50 architecture implemented in PyTorch. The primary objective is to classify exercise videos into two categories, enabling users to identify and categorize exercises accurately.

This repository is currently used for development of Bramson the AI Motivational Gym Buddy.

This repository presents the implementation of our collaborative project between UPenn/Orthopaedics, UDel/Physical Therapy, and CMU/Engineering on exercise prediction.

A Computer Vision Project for the AAI3001 module

This project is to be able to classify any pose into a class by being able to process the landmarks or keypoints in the image.

Machine learning project for automatic barbell exercise classification using accelerometer and gyroscope sensor data from wearable devices. Features comprehensive signal processing, feature engineering, and multi-class classification to recognize 5 exercise types with intensity variations.