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<p>Human activity recognition (HAR) is the identification of daily human activities using wearable sensor data. In this study, we evaluate a deep learning–based HAR framework utilizing hip-mounted accelerometer and gyroscope signals from the USC-HAD dataset, which contains readings from healthy participants only. The proposed pipeline integrates convolutional feature extraction, bidirectional long short-term memory modeling, and an additive attention mechanism to capture temporal dependencies in the sensor data. The model is evaluated using performance matrices and leave-one-subject-out cross-validation (LOSO-CV) to assess subject-independent generalization. Performance is reported using accuracy, precision, recall, F1-score, and 95% confidence intervals, and statistical significance testing. Our experimental results show that under subject-exclusive splitting, the proposed model achieves 98% accuracy. Under strict LOSO-CV, the model achieves a performance of 78% ± 0.1130, providing a more realistic assessment of subject-independent generalization across unseen individuals. The dataset does not include clinical or patient populations. The findings are limited to non-clinical settings and should be interpreted within this scope. The results primarily contribute methodological insights into wearable-based HAR systems. The potential of this work for healthcare applications is discussed as a direction for future research, subject to validation on clinically representative datasets.</p>

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Human activity recognition using CNN–BiLSTM with attention on hip-mounted wearable sensors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月26日

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  • Fajr Naveed,
  • Hamza Khan,
  • Zaki Uddin,
  • Khalid Mehmood Cheema,
  • Muhammad Farhan Khan,
  • Syed Sohail Ahmed

摘要

Human activity recognition (HAR) is the identification of daily human activities using wearable sensor data. In this study, we evaluate a deep learning–based HAR framework utilizing hip-mounted accelerometer and gyroscope signals from the USC-HAD dataset, which contains readings from healthy participants only. The proposed pipeline integrates convolutional feature extraction, bidirectional long short-term memory modeling, and an additive attention mechanism to capture temporal dependencies in the sensor data. The model is evaluated using performance matrices and leave-one-subject-out cross-validation (LOSO-CV) to assess subject-independent generalization. Performance is reported using accuracy, precision, recall, F1-score, and 95% confidence intervals, and statistical significance testing. Our experimental results show that under subject-exclusive splitting, the proposed model achieves 98% accuracy. Under strict LOSO-CV, the model achieves a performance of 78% ± 0.1130, providing a more realistic assessment of subject-independent generalization across unseen individuals. The dataset does not include clinical or patient populations. The findings are limited to non-clinical settings and should be interpreted within this scope. The results primarily contribute methodological insights into wearable-based HAR systems. The potential of this work for healthcare applications is discussed as a direction for future research, subject to validation on clinically representative datasets.