An adaptive filtering algorithm to estimate sprint velocity using a single inertial sensor

The assessment of sprint velocity is useful for evaluating performance and guiding training interventions. In this paper, we describe an adaptive filtering algorithm to estimate sprint velocity using a single, sacrum-worn magneto-inertial measurement unit. Estimated instantaneous velocity, average 10 m interval velocity, and peak velocity during 40 m sprints from the proposed method were compared to a reference method using photocell position-time data. Concurrent validity of the proposed method was assessed using mean absolute error and mean absolute percent error for all velocity estimates. The significance of the mean error was assessed using a factorial ANOVA for average interval velocity and a paired-samples t test for peak velocity. Reliability was assessed using Bland–Altman 95% limits of agreement for repeated measures. Average interval velocity was underestimated early in the sprint (− 0.25 to − 0.05 m/s) and overestimated later (0.13 m/s) with mean absolute error between 0.20 m/s (3.95%) and 0.62 m/s (7.78%). The average mean absolute error was 0.45 m/s (7.02%) for instantaneous velocity and 0.63 m/s (7.84%) for peak velocity. The limits of agreement grew progressively wider at greater distances (− 0.59 to 0.34 m/s for 0–10 m and − 1.32 to 1.59 m/s for 30–40 m). The estimation error from the proposed method is comparable to other wearable sensor-based methods and suggests its potential use to assess sprint performance.