A collaboration between the proponents of the thesis titled "DEVELOPMENT OF WEB BASED APP FOR DETERMINING ANATOMICAL METRICS OF NON-LOCOMOTOR EXERCISES USING POSE ESTIMATION" for the completion of the degree of Bachelor of Science in Computer Science in Cavite State University - CCAT Campus.
- Real-Time Repetition Counting Using Machine Learning
- Real-Time Speed per Repetition Counting Using Machine Learning
- Calories Burned in Session
- Mobile Webapp compatible
Must use #unsafely-treat-insecure-origin-as-secure flag in chrome app - Register and Login
- Session Records Viewing
- Python
- NodeJS
- Git
- under root create file and save to .env.local
MONGODB_URI='Your MongoDB Connection string'
JWT_SECRET='Json Webtoken String (Random Generated String)'
DB='MongoDB Database name'
COLLECTION='MongoDB Collection name'
NEXT_PUBLIC_MODEL_API='api link for model engine'
In NEXT_PUBLIC_MODEL_API:
If running Model Engine locally use http://localhost:5000/predict
If using Google Colab for running Model Engine add /predict after url
Use the Google Colab below
Use the url after "Running on" and put /predict after the url
-
Clone the repository
-
Proceed to child directory
cd PoseExcercise-WebApp
- Install dependencies
npm install
- Create a python virtual environment:
Go to src/backend
cd src/backend
Install python virtual environment
pip install virtualenv
Create virtual environment
virtualenv -p python3
name of virtual environment
- Run the virtual environment
./
name of virtual environment/Scripts/activate
- Install python dependencies
pip install flask flask_cors joblib pandas numpy scikit-learn gevent
- Run the backend
Must be in src/backend
py ModelEngine.py
- Run the frontend
Must be in PoseExcercise-WebApp
npm run build
npm run start
- Open the browser and test the app