The prediction of speed and incline in outdoor running in humans using accelerometry

Abstract

Purpose: To explore whether triaxial accelerometric measurements can be utilized to accurately assess speed and incline of running in free-living conditions. Methods: Body accelerations during running were recorded at the lower back and at the heel by a portable data logger in 20 human subjects, 10 men, and 10 women. After parameterizing body accelerations, two neural networks were designed to recognize each running pattern and calculate speed and incline. Each subject ran 18 times on outdoor roads at various speeds and inclines; 12 runs were used to calibrate the neural networks whereas the 6 other runs were used to validate the model. Results: A small difference between the estimated and the actual values was observed: the square root of the mean square error (RMSE) was 0.12 m . s(-1) for speed and 0.014 radiant (rad) (or 1.4% in absolute value) for incline. Multiple regression analysis allowed accurate prediction of speed (RMSE = 0.14 m . s(-1)) but not of incline (RMSE = 0.026 rad or 2.6% slope). Conclusion: Triaxial accelerometric measurements allows an accurate estimation of speed of running and incline of terrain (the latter with more uncertainty). This will permit the validation of the energetic results generated on the treadmill as applied to more physiological unconstrained running conditions