IMU-Based Bilateral Kinematic Waveform Analysis Associated with Shoulder Pain History in Competitive Swimmers

Abstract

Shoulder injuries are one of the most common overuse problems in competitive swimming, yet objective, field-deployable methods for quantifying pain-related movement alterations remain limited. This study investigates whether bilateral IMU sensors placed on the upper humerus can identify kinematic bilateral waveform similarities tied to a history of shoulder pain in swimmers. In this study, thirty-four NCAA Division 1 collegiate swimmers and water polo players completed a standardized swimming test and were classified into moderate/severe pain (n = 22) and none/mild pain (n = 12) groups based on self-reported shoulder pain scores. Kinematic features were extracted and evaluated using statistical tests and machine learning classification. The none/mild pain group showed a higher peak left-right shoulder cross-correlation across all axes (0.85 vs. 0.78, p = 0.002). The best-performing model Classification accuracy for distinguishing between moderate/severe pain and none/mild pain conditions achieved a balanced accuracy of 70.6% (24/34 participants), with a balanced accuracy of 69.7% (95% CI [52.7–85.2%]) and an ROC AUC of 0.731 (95% CI [0.557–0.890]), suggesting that personalized machine learning approaches may be necessary to improve individual-level classification performance. Further investigation of intra-subject associations between shoulder pain history and kinematic bilateral waveform similarities is warranted to better understand their relationship and determine potential causal mechanisms.