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Workload Management System for Cricketers
Summary
Sri Lankan cricketers face high injury rates. A new system using wearable sensors and AI analyzes player workload to prevent injuries and prolong careers.
Area of Science:
- Sports Medicine
- Biomechanical Engineering
- Artificial Intelligence in Sports
Background:
- High injury rates in cricket, particularly among Sri Lankan players, lead to shortened careers.
- Dynamic movements in cricket increase player vulnerability to musculoskeletal injuries.
- Effective workload management is crucial for athlete health and performance longevity.
Purpose of the Study:
- To develop an innovative workload management system for cricketers.
- To mitigate sports-related injuries and enhance player career sustainability.
- To provide a user-friendly platform for analyzing critical workload parameters.
Main Methods:
- Utilized wearable Inertial Measurement Unit (IMU) sensors for data collection.
- Employed Convolutional Neural Network (CNN) models to classify athlete activities.
- Computed key workload parameters post-activity sessions for comprehensive analysis.
Main Results:
- Successfully developed a system for collecting and analyzing cricketer workload data.
- Demonstrated the potential of IMU sensors and CNNs in injury prevention strategies.
- Laid the groundwork for a user-friendly interface to present workload insights.
Conclusions:
- The implemented system offers a proactive approach to managing cricketer workload.
- AI-driven analysis of sensor data can significantly reduce injury incidence.
- This technology has the potential to revolutionize injury prevention in professional cricket.
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