Human Activity Recognition (HAR) is a rapidly growing field with applications ranging from healthcare and fitness tracking to smart home systems and security. Traditional methods often rely on handcrafted features and conventional machine learning algorithms, which may not effectively capture the complex temporal and spatial patterns inherent in human activities. Deep learning-based models have emerged as powerful tools for addressing these challenges, leveraging their ability to automatically learn hierarchical features from raw sensor data.