A Computer Vision Method for Estimating Velocity from Jumps
This provides a low-cost alternative for amateur athletes who lack access to professional assessments, though it is incremental as it applies existing computer vision methods to a new domain.
The paper tackled the problem of estimating jump velocity for athletes using video recordings instead of specialized equipment, achieving an average R-value of 0.71 (SD = 0.06) for velocity estimation.
Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment like force plates. For the assessment, athletes perform drop and squat jumps, and key variables are measured, e.g. velocity, flight time, and time to stabilization, to name a few. However, amateur athletes may not have access to professionals or equipment that can provide these assessments. Here, we investigate the feasibility of estimating key variables using video recordings. We focus on jump velocity as a starting point because it is highly correlated with other key variables and is important for determining posture and lower-limb capacity. We find that velocity can be estimated with a high degree of precision across a range of athletes, with an average R-value of 0.71 (SD = 0.06).