A comparative analysis of LSTM models aided with attention and squeeze and excitation blocks for activity recognition.

Human Activity Recognition plays a vital role in various fields, such as healthcare and smart environments. Traditional HAR methods rely on sensor or video data, but sensor-based systems have gained popularity due to their non-intrusive nature. Current challenges in HAR systems include variability in sensor data influenced by factors like sensor placement, user differences, and environmental conditions. Additionally, imbalanced datasets and computational complexity hinder the performance of these systems in real-world applications. To address these challenges, this paper proposes an LSTM-based HAR model enhanced with attention and squeeze-and-excitation blocks. The LSTM captures temporal dependencies, while the attention mechanism dynamically focuses on important parts of the input sequence. The squeeze-and-excitation block recalibrates channel-wise feature importance, allowing the model to emphasize the most informative features. The proposed model demonstrated a 99% accuracy rate, showcasing its effectiveness in recognizing various activities from sensor data. The integration of attention and squeeze-and-excitation mechanisms further boosted the model's ability to handle complex datasets. Comparative analysis with existing LSTM models confirms that the proposed approach improves accuracy and reduces computational complexity, making it a highly suitable model for real-world applications.