DeM-FCN: an ultra-lightweight and purely convolutional framework for edge-native human activity recognition in wearable fitness tracking

Full abstract

Read the full abstract

Introduction Wearable human activity recognition has become an important component of intelligent fitness tracking, but deploying accurate recognition models on resource-constrained edge devices remains challenging. Existing deep learning methods often rely on recurrent structures, attention mechanisms, or complex hybrid architectures, which increase computational cost and limit real-time deployment. Methods This study proposes DeM-FCN, a lightweight and purely convolutional framework for smart dumbbell-based resistance-training activity recognition. The model integrates a physics-aware input representation, Gaussian noise regularization, stacked one-dimensional convolutional blocks, Global Max Pooling, and a cost-sensitive focal loss to improve subject-independent recognition. The input representation extends raw inertial measurements by introducing trigonometric encoding of Euler angles and acceleration and gyroscope magnitude features, allowing the model to capture both orientation-related motion patterns and orientation-insensitive motion intensity. The proposed model was evaluated using Leave-One-Subject-Out cross-validation on a custom smart dumbbell dataset containing four resistance-training exercises collected from 15 subjects. Results DeM-FCN achieved an accuracy of 0.966, macro F1-score of 0.916, and macro AUC of 0.982, while maintaining only 73.7 K parameters, 14.84 M FLOPs, and a model size of 0.29 MB. Additional evaluations on PAMAP2 and MHEALTH suggested that the convolutional backbone retained useful class-ranking ability on public IMU-based HAR datasets, while the reduced macro F1-scores indicated that hard-label daily activity recognition remains more challenging than constrained resistance-training recognition due to broader activity diversity, sensor-domain differences, and missing modality information. A refined ablation study confirmed that trigonometric encoding and magnitude features provide complementary benefits, with magnitude features contributing more strongly to cross-subject robustness. Discussion The results suggest that DeM-FCN provides a favorable accuracy-efficiency trade-off for wearable resistance-training recognition and offers a practical foundation for edge-oriented fitness monitoring.