As the world is modernizing, we are spending more time sitting at desks. It can lead to various other health-related problems, which can be prevented by exercising. These exercises include walking, jogging, weight lifting etc. Exercises like weight lifting can lead to multiple sprains, strains, or fractures if they are not done correctly. Learning how to do them correctly requires supervision from gym experts. But in our busy lives, we barely have time to go to the gym. So in our study, we are exploring whether we can predict if a person is exercising correctly or not by analyzing data collected from various sensors attached to the user as they exercise. Previous authors have only showed the perforamnce of using Decision Tree model. So in our project, we are using different preprocessing approach and experimented with various machine learning models. As the number of features were high, we used two different types of feature selection and compared their performance.
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- Support Vector Machine : 74.3%
- Decision Tree : 67.92%
- Random Forest : 86.2%
- Gaussian Naive Bayes : 57.62%
- K Nearest Neighbour : 70.5%
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- Language : Python
- IDE : Google Colab
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- Tensorflow
- Scikit-learn
- Numpy
- Pandas