Data driven running technique identification

Abstract

We evaluate if acceleration and rotational IMU data and marker-based positional data can be used to quantify the running technique. We also investigate patterns between the runners’ anatomy, fitness level and technique, and how common instructions impact their running technique.

Running technique data, consisting of rotational velocity and acceleration from a foot mounted IMU and position from a marker-based motion capture system, is collected from 47 participants, together with data on anatomy and fitness level. Participants perform a testing protocol containing treadmill running at different velocities while receiving different technique instructions. Data is processed to extract a representative stride cycle for each data source for every participant at every velocity and technique instruction.

We evaluate three methods to quantify the technique using dimensionality reduction and reconstruction: sequential feature selection using multivaritate linear regression, principal component analysis, and autoencoder. Best performance is obtained for principal component analysis on all data sources. Information loss is significantly larger on rotational and acceleration data from the IMU than for positional data from the marker-based system.

Limited patterns between the anatomy and fitness level of runners and their technique were observed, and the found patterns are generally on parameters that are not related to technique, such as ground contact time and contact to flight-time ratio.

Most technique instructions are shown to impact technique, but the effect diminishes as velocity increases. A larger impact is seen when runners are asked to increase back-kick height, knee lift, or frequency, and a smaller impact is seen when asked to land further back with the foot or push the hip forward.