A Multimodel Sensor Fusion Approach for Real Time Batting Performance Analysis Using IOT-Enabled Cricket Bat

Abstract

Batting performance is heavily dependent on precise timing, swing mechanism and quality of bat-bowl contact and control. However, training is mostly subjective at the grassroots level, relying on visual judgement by coaches or self-assessment by players. These methods overlook critical micro data, not tracking exactly where the ball hits the bat, how timing fluctuates, or how consistently the swing follows its path. This limitation restricts a player’s ability to identify mistakes and improve independently. The paper presents the design and development of an IOT-enabled smart cricket bat that integrates piezoelectric impact sensing, motion detection, and wireless communication to provide real-time batting performance analysis. The system captures multimodal data to assess swing speed, impact location, and timing index. To validate the effectiveness of the cricket bat system, primary data was collected using a pre-post usage survey design involving the same group of users. Initial market research identifies batting challenges while post-usage survey quantifies improvement after implementing the system. The results show immense improvement in timing clarity, sweet spot analysis, error recognition, and performance confidence. The proposed system provides the potential of multi-model sensor fusion to democratize access to objective cricket training analysis and reduce overall reliance on subjective or virtual coaching feedback. This cricket bat technology is not just a device, but a sports management solution that addresses the biggest structural gap in the Indian ecosystem—unequal access to accessible training tools.