LAUSR

Gaining better insight into the loading volume and intensity of runners outside of the lab will allow progress in academia and industry. Within academia, monitoring the load experienced during every day activities will improve the quality of longitudinal research studies addressing overuse injury development. This is because real world load patterns prior to the onset of the injury could be analysed and the influence of activity related fatigue could be considered. The footwear industry could use SEF information to optimize footwear selection or customization for an individual. SEF might also give feedback to the customer with respect to a less injury-risky movement behaviour. The results of the present study provide evidence that the prediction based estimation of loading parameters based on machine learning algorithms might be a feasible approach for the prediction of loading parameters. Nonetheless, prediction quality needs to be further improved, in particular for loading parameters outside of the sagittal plane. This might be achieved by using more sophisticated machine learning techniques and/or by adding sensor information from other locations or different sensors, e.g. gyroscopes or pressure sensors.