Assessment of spatiotemporal sprinting kinematics in the field: A comparison of a commercial 2-D markerless motion capture software using artificial intelligence with OptoJump ^TM

Field-based assessment of spatiotemporal kinematics including contact time (CT), flight time (FT), step time (ST), step length (SL), step frequency (SF), and step velocity (SV) is essential for evaluating sprint performance, yet existing methods are often inaccessible for coaches. The aim of this study was to examine the agreement between a markerless motion capture (MMC) system (VueMotion) and a field-based system (OptoJump™) for measuring spatiotemporal kinematics. Seventeen participants performed a total of 75 flying 20 m sprints in which spatiotemporal parameters were simultaneously obtained using OptoJump™ and VueMotion via a consumer smartphone (4k, 60 fps). Individual step analysis (n = 748) showed minimal bias (≤0.3%) for ST (bias: 0.000 s; LoA: ± 0.015 s), SF (bias: −0.01 Hz; LoA: ± 0.27 Hz), SL (bias: 0.2 cm; LoA: ± 10.1 cm), and SV (bias: −0.03 m·s − 1 ; LoA: ± 0.61 m·s − 1 ). Although overall temporal measurement was accurate, sub-component metrics demonstrated that the MMC system overestimated CT (−2.9%; bias: −0.003 s; LoA: ± 0.015 s) and underestimated FT (3.4%; bias: 0.004 s; LoA: ± 0.015 s). While systematic bias remained consistent for trial-averaged (20 m) parameters (n = 75), limits of agreement narrowed substantially: avg-CT (LoA: ± 0.006 s), avg-FT (LoA: ± 0.006 s), avg-ST (LoA: ± 0.002 s), avg-SL (LoA: ± 1.5 cm), avg-SF (LoA: ± 0.04 Hz), and avg-SV (LoA: ± 0.11 m·s − 1 ). Currently, assessments of single-step metrics should be interpreted with caution due to random error; however, our findings indicate that average metrics provide a viable approach for monocular MMC spatiotemporal analysis using consumer-grade hardware.