A Wearable System to Analyze the Human Arm for Predicting Injuries Due to Throwing.

Area of Science:

• Sports Medicine
• Biomechanics
• Wearable Technology

Background:

• Throwing-related injuries are common in sports like baseball, cricket, and javelin.
• Understanding the biomechanical factors leading to upper arm injuries in throwing athletes remains limited.
• Cricket fast bowlers are particularly susceptible to upper arm injuries due to repetitive high-velocity throwing actions.

Purpose of the Study:

• To develop a real-time wearable system for analyzing cricket players' throwing actions.
• To extract key biomechanical parameters indicative of potential upper arm injuries.
• To identify novel injury risk factors using machine learning algorithms.

Main Methods:

• A wearable system integrating Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors was developed.
• EMG signals were analyzed to assess muscle activity during the throwing motion.
• IMU data captured detailed arm kinematics and dynamics.

Main Results:

• The system successfully extracted critical parameters related to muscle activation and arm movement during cricket bowling.
• Analysis of these parameters identified established indicators of potential upper arm injuries.
• An unsupervised learning algorithm revealed novel correlations associated with injury risk.

Conclusions:

• The developed wearable system provides a promising tool for real-time monitoring of cricket players.
• Objective biomechanical data can aid in the early detection and prevention of upper arm injuries.
• Further research can refine the system for broader application in sports injury prevention.