Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning

Abstract

ABSTRACTFast bowlers are at a high risk of overuse injuries. There are specific bowling frequency ranges known to have negative or protective effects on fast bowlers. Inertial measurement units (IMUs) can classify movements in sports, however, some commercial products can be too expensive for the amateur athlete. As a large number of the world's population has access to an IMU (e.g. smartphones), a system that works on a range of different IMUs may increase the accessibility of automated workload monitoring in sport. Seventeen elite fast bowlers in a training setting were used to train and/or validate five machine learning models by bowling and performing fielding drills. The accuracy of machine learning models trained using data from all three bowling phases (pre-delivery, delivery and post-delivery) were compared to those trained using only the delivery phase at a sampling rate of 250 Hz. Next, models were trained using data down-sampled to 125 Hz, 50 Hz, and 25 Hz to mimic results from lower specification sensors. Models trained using only the delivery phase showed similar accuracy (> 95%) to those trained using all three bowling phases. When delivery-phase data were down-sampled, the accuracy was maintained across all models and sampling frequencies (>96%).