Prediction of ACL injury

The dataset is designed for predicting anterior cruciate ligament (ACL) injuries in basketball players, comprising a total of 104 participants (11 with injury data, 93 without). The test content includes four files: The Raw_data1 primarily includes athletes' Relative Deadlift test, Squat Jump test, Injury History, Weekly Basketball Hours, Y-Balance Test (YBT), and incorporates injury labels. The second part consists of three files, containing biomechanical data collected during a stop-jump and side-cutting maneuver test. This movement is divided into three phases: Emergency-stop; Initial-acceleration; Side-cutting。 Raw_data2-Emergency_stop_phase: Indicators include center of pressure (COP), SHARK_ANGLE, joint forces and moments at the hip, knee, and ankle, as well as surface electromyography (EMG) signals from lower-limb muscles. (L: left leg; R: right leg) Raw-data3-Initial-acceleration-phase: Indicators include center of pressure (COP), SHARK_ANGLE, joint forces and moments at the hip, knee, and ankle (each mechanical parameter is measured in three directions: X, Y, and Z), as well as surface EMG signals from lower-limb muscles. (L: left leg; R: right leg; here, biomechanical parameters are further distinguished by direction due to asymmetrical mechanical outputs between legs.) Raw-data4-Side-cutting_phase: Indicators include ground reaction force (GRF) peaks, joint forces and moments at the hip, knee, and ankle, as well as surface EMG signals from muscles of the supporting leg. To ensure movement consistency, all participants performed the side-cutting maneuvers using a crossover step during this phase. Meanwhile, the biomechanical assessment was analyzed in a three-dimensional (3D) plane with three axes: the X-axis for flexion-extension, the Y-axis for adduction-abduction, and the Z-axis for internal-external rotation.