Abstract
ABSTRACT This study examined whether an inertial measurement unit (IMU) and machine learning models could accurately measure bowling volume (BV), ball release speed (BRS), and perceived intensity zone (PIZ). Forty-four male pace bowlers wore a high measurement range, research-grade IMU (SABELSense) and a consumer-grade IMU (Apple Watch) on both wrists. Each participant bowled 36 deliveries, split into two different PIZs (Zone 1 = 70–85% of maximum bowling effort, Zone 2 = 100% of maximum bowling effort). BRS was measured using a radar gun. Four machine learning models were compared. Gradient boosting models had the best results across all measures (BV: F-score = 1.0; BRS: Mean absolute error = 2.76 km/h; PIZ: F-score = 0.92). There was no significant difference between the SABELSense and Apple Watch on the same hand when measuring BV, BRS, and PIZ. A significant improvement in classifying PIZ was observed for IMUs located on the dominant wrist. For all measures, there was no added benefit of combining IMUs on the dominant and non-dominant wrists.
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