Shahrood University of Technology Journal of AI and Data Mining 2322-5211 13 3 2025 07 01 Attention-HAR: Advanced Human Activity Recognition Using a Deep Learning Model with an Integrated Attention Mechanism 293 304 3492 10.22044/jadm.2025.15658.2683 EN Navid Raisi Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Mahdi Rezaei Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK Behrooz Masoumi Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran Journal Article 2025 01 29 Human Activity Recognition (HAR) using computer vision is an ‎expanding field with diverse applications, including healthcare, ‎transportation, and human-computer interaction. While classical ‎approaches such as Support Vector Machines (SVM), Histogram ‎of Oriented Gradients (HOG), and Hidden Markov Models ‎‎(HMM) rely on manually extracted features and struggle with ‎complex motion patterns, deep learning-based models (e.g., ‎Convolutional Neural Networks (CNN), Long Short-Term ‎Memory (LSTM), Transformer-based models) have improved ‎performance but still face challenges in handling occlusions, ‎noisy environments, and computational efficiency. This paper ‎introduces Attention-HAR, a novel deep neural network model ‎designed to enhance HAR performance through three key ‎innovations: Conv3DTranspose for spatial upsampling, ‎ConvLSTM2D for capturing spatiotemporal patterns, and a ‎custom attention mechanism that prioritizes critical frames within ‎sequences. Unlike conventional attention mechanisms, our ‎approach dynamically assigns weights to key frames, reducing the ‎impact of redundant frames and enhancing interpretability and ‎computational efficiency. Experimental results on the UCF-101 ‎dataset demonstrate that Attention-HAR outperforms state-of-the-‎art models, achieving an accuracy of 97.61%, a precision of ‎‎97.95%, a recall of 97.49%, an F1-score of 97.64, and an AUC ‎of 99.9%. With only 1.26 million parameters, the model is ‎computationally efficient and well-suited for deployment on ‎lightweight platforms. These findings suggest that integrating ‎temporal-spatial feature learning with attention mechanisms can ‎significantly improve HAR in dynamic and complex ‎environments‏.‏ https://jad.shahroodut.ac.ir/article_3492_e549ef99e343612689d9c5332dafa640.pdf