A Novel IoT-Based System for Ten Pin Bowling

TL;DR

This paper introduces an IoT-Cloud system utilizing IMUs, DTW, and SVM to monitor, analyze, and classify bowling throws in real-time, enhancing coaching and skill assessment for athletes.

Contribution

It presents a novel IoT-based system integrating motion sensors, algorithms, and machine learning for real-time bowling performance analysis and error detection.

Findings

System effectively assesses throw quality

Detects common bowling errors accurately

Classifies skill levels reliably

Abstract

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.