This repository contains code for a Body Movement Detection system using Gait Recognition Technology. The project utilizes machine learning techniques, specifically deep learning, to recognize different activities based on sensor data.
Prerequisites Python (3.6 or higher) TensorFlow NumPy Pandas Matplotlib Seaborn Scikit-learn Install the required dependencies using:
pip install [Required Dependenies]
Data Loading and Preprocessing: The code reads sensor data from a CSV file, preprocesses it by scaling features, and encodes activity labels.
Model Building: A deep learning model is constructed using TensorFlow and Keras. It consists of input and output layers with intermediate dense layers.
Model Training: The model is trained using the training dataset with a validation split. Training loss and accuracy are monitored.
Evaluation: The trained model is evaluated on the test dataset, and metrics such as accuracy and confusion matrix are displayed.
Visualization: Loss, accuracy, and confusion matrix are visualized using Matplotlib and Seaborn.
The model achieves a test accuracy of 97%. Confusion matrix and other visualizations are available also.
Explore different architectures and hyperparameters for improved performance. Investigate techniques for handling imbalanced datasets. Extend the model to recognize new activities.