IMU-based identification of rowing conditions through supervised machine learning.

Abstract

Background: Rowing combines on-water and ergometer-based training, while inertial measurement units (IMUs) offer a practical approach for field-based biomechanical monitoring. This study examined whether trunk-mounted IMU data can distinguish static Concept2 (C2), dynamic RP3, and on-water rowing conditions.

Methods: Ten youth rowers (16.3 ± 0.8 years) performed high-intensity rowing in all three conditions. Triaxial acceleration and gyroscope data were recorded at 208 Hz. Six classical machine-learning and four deep-learning models classified overlapping three-stroke windows using leave-one-subject-out cross-validation. Repeated-measures analyses assessed condition-specific IMU magnitude features.

Results: The ensemble achieved the highest mean accuracy (85.5 ± 10.3%). Individual accuracy varied from 24.0% to 97.1% across models and athletes. The 7,545 windows were imbalanced toward Boat, with a no-information rate of 71.8%. Boat was identified most reliably, whereas RP3 was frequently misclassified as Boat. Nine of ten magnitude features remained significant after false-discovery-rate correction.

Conclusions: Trunk-mounted IMU signals distinguished the three rowing conditions at group level. However, class imbalance, RP3 familiarity, measurement order, boat-standardisation asymmetry, and athlete-level variability require cautious interpretation. Applied use requires athlete-specific calibration and prospective validation.