this study will be conducted to investigate the effect of muscular fatigue on lower limb kinematics during single-leg drop landing in female professional volleyball players using a mobile-based motion analysis application.
Female volleyball players aged 20-25 years
Practice professional volleyball training or competition experience of at least 2 years
typically engage in a high volume of training, with regular participation in at least five training sessions per week
Willingness to adhere to fatigue protocol and provide informed consent
Current or recent (within the past 6 months) lower limb injury or medical condition
History of lower limb surgeries
Neurological disorders or balance impairments .
Experiencing pain or discomfort during jumping or landing tasks
Single group
fatigue protocol
Other interventionBefore formal testing, participants will perform a five-minute warm-up replicating their usual pre-volleyball training routine. Following the warm-up, the single-leg drop landing (SLDL) task will be clearly explained and demonstrated to all participants.Stand barefoot on both legs on a 30 cm box, then extend the testing leg forward. They will then step off the box using the testing leg, land on the same leg with a toe-heel landing pattern. After familiarization, participants will perform three trials of the SLDL task under non-fatigued conditions.Following the completion of the non-fatigue single-leg drop landing (SLDL) trials, each participant will perform three maximal vertical jumps to assess their maximum touch height. The highest value obtained from these attempts will be recorded as the participant's baseline for subsequent comparisons.Following the completion of the baseline maximal vertical jump assessment, participants will undergo a systematic fatigue induction protocol
hip range of motion
OpenCap will be used to assess hip range of motion. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity
knee flexion range of motion
OpenCap will be used to assess knee flexion range of motion. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity
ankle dorsiflexion range of motion
OpenCap will be used to assess ankle dorsiflexion range of motion. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity
knee valgus angle
OpenCap will be used to assess knee valgus angle. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity
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