Authors
Ben Gronbach, Kyle Capili, Seyedeh Fatemeh Khatami Firoozabadi
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 20. Oct 16, 2026. Epub Oct 16, 2026.
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.
PMID:
42944428
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.
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