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

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

Abstract 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.