Self-supervised learning based on local attention encoding module for human activity recognition with wearable data

A self-supervised transfer learning method based on a local attention encoding module (LAEM) is proposed for human activity recognition using wearable devices. This method effectively captures spatiotemporal features through the local attention mechanism and leverages a self-supervised learning strategy to extract general features from unlabeled data, significantly reducing reliance on labeled data. Experimental results indicate that the proposed method achieves an average F1 score improvement of 2.7% across multiple target datasets, with the maximum improvement reaching 4.3%. By thoroughly fine-tuning the model structure, the method further enhances the accuracy and transferability of activity recognition, demonstrating outstanding performance in cross-dataset transfer learning and small dataset scenarios. Additionally, the approach optimizes feature representation for target tasks and validates its adaptability and generalization capabilities under data-scarce conditions.