Sensor-Based Indices for the Prediction and Monitoring of Anterior Cruciate Ligament Injury: Reliability Analysis and a Case Study in Basketball.

The possibility of measuring predictive factors to discriminate athletes at higher risk of anterior cruciate ligament (ACL) injury still represents an open research question. We performed an observational study with thirteen female basketball players who performed monopodalic jumps and single-leg squat tests. One of them suffered from an ACL injury after the first test session.

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players.

Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose.

Validity and reliability of the GYKO inertial sensor system for the assessment of the elbow range of motion.

Background: The evaluation of joints range of motion (ROM) represents a fundamental step in the diagnosis of joint disorders. Assessors usually measure the ROM angle through a universal goniometer (UG). GYKO inertial system (Microgate, Bolzano, Italy) represent a noninvasive, easy-to-use, Inertial Measurement Unit (IMU) method for the measurement of the elbow ROM.