GymLytics

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Visual Analytics of different exercises for humans 🏋️

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- FUNDING.yml
- - Exercise.py
  - Lunges.py
  - Plank.py
  - Pushup.py
  - ShoulderTap.py
  - Squat.py
- ThreadedCamera.py
- utils.py
GymLytics.py
LICENSE
README.md
requirements.txt

// repository documentation

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# GymLytics 🏋️ 
[![](https://img.shields.io/github/license/sourcerer-io/hall-of-fame.svg?colorB=ff0000)](https://github.com/akshaybahadur21/Autopilot/blob/master/LICENSE.txt)  [![](https://img.shields.io/badge/Akshay-Bahadur-brightgreen.svg?colorB=ff0000)](https://akshaybahadur.com)

Visual Analytics of different exercises for humans

## Code Requirements 🦄
You can install Conda for python which resolves all the dependencies for machine learning.

##### pip install requirements.txt

## Description 🏃
Exercise is any bodily activity that enhances or maintains physical fitness and overall health and wellness.

It is performed for various reasons, to aid growth and improve strength, preventing aging, developing muscles and the cardiovascular system, honing athletic skills, weight loss or maintenance, improving health and also for enjoyment. Many individuals choose to exercise outdoors where they can congregate in groups, socialize, and enhance well-being.

## Python  Implementation 👨‍🔬

**Supported Exercise types**

-  Pushup
-  Squat
-  Lunges
-  Shoulder Taps
-  Plank

**Source**
- '0' for webcam
- Any other source for a prerecorded video

If you face any problem, kindly raise an issue

## Setup 🖥️

1) First, record the exercise you want to perform analytics on; or you can setup your webcam so that it can stream your exercise in runtime.
2) Select the type of exercise you want to perform. (Look above for the supported exercises)
3) Run the `GymLytics.py` file with your current configuration

## Execution 🐉

```
python3 GymLytics.py --type pushup --source resources/push_aks.mov
```

## Results 📊

### Push ups
<div align="center">
<img src="https://github.com/akshaybahadur21/BLOB/blob/master/push.gif" width=800>
</div>

### Squats
<div align="center">
<img src="https://github.com/akshaybahadur21/BLOB/blob/master/gym_squats.gif" width=400>
</div>

## References 🔱
 
 -  Ivan Grishchenko and Valentin Bazarevsky, Research Engineers, Google Research. [Mediapipe by Google](https://github.com/google/mediapipe)

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