Advanced Fitness Tracker

This was the Machine Learning code that was designed for Advanced fitness purposes.

Hardware- Raspberry Pi 0w with custom charging circuit, BMP180(Pressure Sensor) and MPU9250(Gyro Sensor)

Tech stack- Python 3.x, Sklearn, Scipy, Numpy, and Pandas.

Features

• Detects the exercise when the device is in the active-time mode i.e when the code is running. Can currently detect Vertical Raises, Bicep Curls, Push Ups and Sit-Ups.
• Detects number of steps and displays total calorie burnt in passive mode.
• Detects the number of counts a user workouts.
• Total Model accuracy of 0.95

Requirements:

• Pandas- pip install pandas

  • csv included
    

• Sklearn- pip install scikit-learn

• Numpy- pip install numpy

• Matplotlib.pyplot(Optional)- pip install matplotlib

• scipy.signal- pip install scipy

How to use

• Install the requirements.
  1. Fill up the body-movement data in ExerciseClassifier.csv and save it in the directory of the file FitnessXpertProgram.py (Mind the Path of the csv file).
  2. Run the code to get counts and detect exercises.

Errors

1. Expect incomplete data to process error from csv.

Solution: Browse to the csv file and delete the last row since it shouldn't matter after the data is acquired for a long time.

1. Inaccurate detection if the active-time is lesser than required i.e 30s.
   

Solution: Workout/Acquire data for more than 30s.

Note: Evaluation Data for different workouts are saved with their respective names for testing.

Built With

• python