Robust IoT Activity Recognition via Stochastic and Deep Learning

In the evolving landscape of Internet of Things (IoT) applications, human activity recognition plays an important role in domains such as health monitoring, elderly care, sports training, and smart environments. However, current approaches face significant challenges: sensor data are often noisy and variable, leading to difficulties in reliable feature extraction and accurate activity identification; furthermore, ensuring data integrity and user privacy remains an ongoing concern in real-world deployments. To address these challenges, we propose a novel framework that synergizes advanced statistical signal processing with state-of-the-art machine learning and deep learning models. Our approach begins with a rigorous preprocessing pipeline—encompassing filtering and normalization—to enhance data quality, followed by the application of probability density functions and key statistical measures to capture intrinsic sensor characteristics. We then employ a hybrid modeling strategy combining traditional methods (SVM, Decision Tree, and Random Forest) and deep learning architectures (CNN, LSTM, Transformer, Swin Transformer, and TransUNet) to achieve high recognition accuracy and robustness. Additionally, our framework incorporates IoT security measures designed to safeguard data integrity and privacy, marking a significant advancement over existing methods in both efficiency and effectiveness.