  Bowling is a target sport that is popular among all age groups with
professionals and amateur players. Delivering an accurate and consistent
bowling throw into the lane requires the incorporation of motion techniques.
Consequently, this research presents a novel IoT-Cloud based system for
providing real-time monitoring and coaching services to bowling athletes. The
system includes two inertial measurement units (IMUs) sensors for capturing
motion data, a mobile application and a cloud server for processing the data.
First, the quality of each phase of a throw is assessed using a Dynamic Time
Wrapping (DTW) based algorithm. Second, an on device-level technique is
proposed to identify common bowling errors. Finally, an SVM classification
model is employed for assessing the skill level of bowler athletes. We
recruited nine right-handed bowlers to perform 50 throws wearing the two
sensors and using the proposed system. The results of our experiments suggest
that the proposed system can effectively and efficiently assess the quality of
the throw, detect common bowling errors and classify the skill level of the
bowler.
