RCIRNet: Rotator Cuff Injury Recognition Using Wearable IMU Signals With Cross-Attention

Rotator cuff injury (RCI) is a common cause of shoulder dysfunction, often accompanied by pain, restricted mobility, and reduced quality of life. Conventional imaging-based diagnostic approaches are effective for structural assessment but are often costly, time-consuming, and difficult to apply in large-scale screening or repeated follow-up. In this study, we propose the RCI recognition network (RCIRNet), a lightweight deep learning model for automated RCI recognition using upper-limb kinematic data collected from wearable inertial measurement units (IMUs). RCIRNet extracts temporal motion features with one-dimensional convolutional layers and introduces a channel-sequence cross-attention (CSCA) module to emphasize informative sensor channels and discriminative movement phases. Multi-segment acceleration signals from the upper thoracic spine, upper arm, and forearm during shoulder abduction are used to distinguish RCI from non-RCI conditions, including adhesive capsulitis and shoulders without dysfunction. Model performance is evaluated on an independent test set using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). RCIRNet achieves an accuracy of 88.8%, an F1-score of 88.9%, and an AUC of 0.932, outperforming traditional machine-learning models as well as BiLSTM and Transformer Encoder baselines. These results suggest that IMU-based deep learning with channel-sequence attention provides a promising auxiliary tool for low-cost and non-invasive RCI screening.