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<p>Human activity recognition (HAR) is essential for healthcare, surveillance, smart homes, and human-computer interaction, but traditional approaches relying on handcrafted features and shallow machine learning models often fail to capture the intricate dynamics of human movements. This study introduces HARNet, a deep learning (DL) model that overcomes these limitations by integrating advanced video preprocessing techniques and state-of-the-art architectures such as CvLSTM, LRCN, and BiLSTM. HARNet employs comprehensive video preprocessing and was evaluated on the UCF50 and HMDB51 datasets, which cover a wide range of human activities. We systematically analyzed the CvLSTM, LRCN, and BiLSTM models to assess their ability to learn spatial and temporal features from video sequences. Experimental results showed that BiLSTM achieved the highest accuracy, with 98.67% on UCF50 and 94.23% on HMDB51, outperforming LRCN and CvLSTM across all metrics, including precision, recall, F1 score, and ROC-AUC, and demonstrating the lowest error rates. HARNet effectively recognizes diverse human activities, enhancing intelligent systems that interpret human behavior with high precision; BiLSTM’s bidirectional nature proved especially effective in capturing temporal dependencies, making it ideal for real-world applications in monitoring, security, and human interaction, significantly boosting the performance and reliability of HAR systems.</p>

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HARNet: an enhanced deep learning-based human activity recognition model

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Md. Alamin Talukder,
  • Mohsin Kazi,
  • Md. Ashraf Uddin

摘要

Human activity recognition (HAR) is essential for healthcare, surveillance, smart homes, and human-computer interaction, but traditional approaches relying on handcrafted features and shallow machine learning models often fail to capture the intricate dynamics of human movements. This study introduces HARNet, a deep learning (DL) model that overcomes these limitations by integrating advanced video preprocessing techniques and state-of-the-art architectures such as CvLSTM, LRCN, and BiLSTM. HARNet employs comprehensive video preprocessing and was evaluated on the UCF50 and HMDB51 datasets, which cover a wide range of human activities. We systematically analyzed the CvLSTM, LRCN, and BiLSTM models to assess their ability to learn spatial and temporal features from video sequences. Experimental results showed that BiLSTM achieved the highest accuracy, with 98.67% on UCF50 and 94.23% on HMDB51, outperforming LRCN and CvLSTM across all metrics, including precision, recall, F1 score, and ROC-AUC, and demonstrating the lowest error rates. HARNet effectively recognizes diverse human activities, enhancing intelligent systems that interpret human behavior with high precision; BiLSTM’s bidirectional nature proved especially effective in capturing temporal dependencies, making it ideal for real-world applications in monitoring, security, and human interaction, significantly boosting the performance and reliability of HAR systems.