A Transfer Learning Method for Cross-Scenario Abnormal Running Posture Detection Based on Wearable Sensors

Running is a popular sport, but abnormal running postures can lead to injuries. With the growing popularity of wearable electronics, real-time detection of abnormal running postures has become increasingly feasible. However, the variability of real-world running scenarios poses significant challenges to the robustness of deep learning-based detection methods. Models trained on fixed scenarios often experience a substantial decline in performance when applied to different environments. To address these issues, this paper proposes a cross-scenario transfer learning (CSTL) framework to detect abnormal running postures by wearable inertial sensors, addressing the challenges of high time complexity and limited adaptability across various running scenarios. In the pre-training stage, we propose a Light-Norm Transformer (LNT) model to improve recognition accuracy using data collected from a single running scenario. In the fine-tuning stage, the pre-trained LNT model is transferred to adapt to five running scenarios with a contribution-guided freezing and dynamic adapter fine-tuning strategy, and the leave-one-out cross-validation method is adopted to evaluate the cross-scenario classification performance on each running scenario in the test module. The proposed CSTL method significantly reduces training time and enhances recognition accuracy, achieving an accuracy of 87.0% on the five running scenarios with only 10 epochs of fine-tuning, outperforming the non-transfer learning approach and achieving the optimal performance-efficiency balance. The proposed CSTL method enhances the adaptability and robustness of abnormal running posture detection systems in practical, real-world applications.