CPSC-5616EL-01 - Fitness Exercise Classifier

Assignment Task

Introduction

1. Of this assignment, you developed a fitness exercise classification system using machine learning algorithms (RF and SVM). Now, in

2. You will extend your work by exploring deep learning (DL) techniques. Deep learning has shown exceptional performance in learning from sequential and time-series data, which makes it suitable for IMU sensor data analysis. You will implement and compare various DL models to determine the most effective for classifying exercises such as squats

Objective

• Develop and train 1D CNN (Convolutional Neural Network) models appropriate for sequential sensor data classification.

• Develop and train GRU (Gated Recurrent Unit) networks for sequential sensor data classification.

• Bonus: Create and evaluate hybrid CNN-GRU models to leverage the benefits of both CNNs and LSTMs.

Data Preparation

Ensure that the dataset is properly formatted and preprocessed for deep learning models.

Data Preparation 

Ensure that the dataset is properly formatted and preprocessed for deep learnin models.

Note on Toolset Usage.

You are encouraged to make full use of all the machine learning libraries and tools we've discussed in class. This includes, but is not limited to, libraries like scikit-learn, Numpy, pandas, and matplotlib. The objective is to familiarize yourself with real-world application scenarios and to simplify certain processes where needed. However, always ensure you understand the underlying mechanics of the tools you're using. Proper documentation and justification of your chosen methods and tools are essential for a comprehensive assessment.

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