Attention-driven BI-LSTM for Robust Human Activity Recognition and Classification in Disaster Scenarios

Accurate and robust human activity recognition are essential for surveillance, healthcare and smart environment applications. However, the unpredictability and complexity of human motions provide significant challenges in obtaining the desired levels of accuracy and robustness. Conventional machine learning models such as Decision Tree, Gaussian NB and K Neighbors, have shown limited efficacy with accuracy estimates ranging from 78.3% to 89.3%. Cutting-edge techniques like Random Forest, RBF SVC and XGB Classifier achieve a maximum accuracy of 93.8%. We introduce a BI-LSTM model that focuses on the most important features using bi-directional long short-term memory networks with a special attention mechanism to address these limitations. The present model demonstrates exceptional performance, attaining an accuracy of 99.83%, a precision of 99.46%, a recall of 99.75% and an F1 score of 99.85%, thereby surpassing other approaches by a substantial margin. The obtained findings validate the model's resilience and effectiveness in precisely recognizing and categorizing human actions in different fields and situations.