No abstract

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.

This article focuses on the development of release velocity and spin prediction models for oval shaped footballs with a state-of-the-art passing machine. Since the trajectory of the ball can be predicted with aerodynamic models, the state of the ball at release time is of interest. At present, no prediction model for this initial state exists. This study measured release spin and velocity. A prediction model was developed based on various ball wear and measured release spin and velocity for different machine configurations. To sensor the motion, a high-speed camera with post image processing was used and release spin and velocity were calculated via regressions. The goal was to predict the release velocity within ±3% in 90% of all relevant cases, depending on the passing machine’s configuration. In addition, the dependency on the wear of the football was investigated. The results show that the release velocity can be predicted, independently of the wear of the ball, with the required accuracy for a reasonable range of machine configurations. For the release spin, a less accurate prediction model was developed. Both prediction models, their limitations and determination are presented in graphical form.

Purpose Active anterior rhinomanometry (AAR) and computed tomography (CT) are standardized methods for the evaluation of nasal obstruction. Recent attempts to correlate AAR with CT-based computational fluid dynamics (CFD) have been controversial. We aimed to investigate this correlation and agreement based on an in-house developed procedure. Methods In a pilot study, we retrospectively examined five subjects scheduled for septoplasty, along with preoperative digital volume tomography and AAR. The simulation was performed with Sailfish CFD, a lattice Boltzmann code. We examined the correlation and agreement of pressure derived from AAR (RhinoPress) and simulation (SimPress) and these of resistance during inspiration by 150 Pa pressure drop derived from AAR (RhinoRes150) and simulation (SimRes150). For investigation of correlation between pressures and between resistances, a univariate analysis of variance and a Pearson’s correlation were performed, respectively. For investigation of agreement, the Bland–Altman method was used. Results The correlation coefficient between RhinoPress and SimPress was r = 0.93 (p < 0.001). RhinoPress was similar to SimPress in the less obstructed nasal side and two times greater than SimPress in the more obstructed nasal side. A moderate correlation was found between RhinoRes150 and SimRes150 (r = 0.65; p = 0.041). Conclusion The simulation of rhinomanometry pressure by CT-based CFD seems more feasible with the lattice Boltzmann code in the less obstructed nasal side. In the more obstructed nasal side, error rates of up to 100% were encountered. Our results imply that the pressure and resistance derived from CT-based CFD and AAR were similar, yet not same.

In American football, high quality training focused on catching is currently not done with passing machines due to their poor pass accuracy and precision. From a coach’s point of view, accurate and precise passing machines are needed to relieve the quarterback from too much training effort. The two aims of this study were to increase the precision of a passing machine and develop an accurate pass prediction model for it. To meet the two aims and provide evidence that a passing machine can be precise and accurate enough for high quality training, an automated passing machine was developed and two experiments were carried out. The results of the first experiment showed that the machine performs with a precision within ±1% of the throwing distance for 218 of the 225 passes. The second experiment resulted in a pass prediction model, which is based on 55 videos and a fitting approach using a neural network. The model estimates the machine configuration for a pass to a targeted point in space. In regard to precision and accuracy, the performance of the machine exceeds the performance of a skilled quarterback. This project improves the state of the art of passing machines for American football and opens possibilities for research in various fields like motion analysis for catches, hand-eye coordination and performance analysis of athletes.