Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. Of the above-mentioned forms of radio frequency (RF) technology, UWB is considered one of the most accurate approaches because it can provide positioning estimates with centimeter-level accuracy. However, it is not yet known whether UWB can also offer such accurate position estimates during strenuous dynamic activities in which moves are characterized by fast changes in direction and velocity. To answer this question, this paper investigates the capabilities of UWB indoor localization systems for tracking athletes during their complex (and most of the time unpredictable) movements. To this end, we analyze the impact of on-body tag placement locations and human movement patterns on localization accuracy and communication reliability. Moreover, two localization algorithms (particle filter and Kalman filter) with different optimizations (bias removal, non-line-of-sight (NLoS) detection, and path determination) are implemented. It is shown that although the optimal choice of optimization depends on the type of movement patterns, some of the improvements can reduce the localization error by up to 31%. Overall, depending on the selected optimization and on-body tag placement, our algorithms show good results in terms of positioning accuracy, with average errors in position estimates of 20 cm. This makes UWB a suitable approach for tracking dynamic athletic activities.

Short communication Validity and reliability of peak tibial accelerations as real-time measure of impact loading during over-ground rearfoot running at different speeds

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 ± 2.04 BW•s −1 , mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 ± 7.90 BW•s −1 (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA.

The ability to perform fast cutting manoeuvres is essential in soccer and requires sufficient traction between shoe and surface. Artificial turf (AT) surfaces are widely used in soccer and among others turf moisture and shoe studs can influence traction. The aim of this study was to quantify the influence of moisture (DRY and WET AT), for three realistic shoe stud characteristics sets [Turf Field (TF), Artificial Grass (AG) and Firm Ground (FG)], on cutting performance, executed traction and perception of the players. Twelve experienced soccer players performed 10 × 5 m shuttle run tests. Ground reaction forces of the open stance phase of the 180° turns were measured and required traction was calculated. Players' perception was also measured. A two-way 2×3 analysis of variance (ANOVA) Repeated Measures with Bonferroni correction was conducted. On dry AT no performance differences between the three tested shoe stud characteristics sets could be measured. On wet AT the AG and FG designs performed evenly well but when wearing the TF shoe, equipped with 74 short studs, significant surface x shoe interaction effects indicated decreased performance and traction on the wet surface. The experienced players perceived shoe x surface evoked differences in performance and traction very well

Despite the fact that marker-based systems for human motion estimation provide very accurate tracking of the human body joints (at mm precision), these systems are often intrusive or even impossible to use depending on the circumstances, e.g. markers cannot be put on an athlete during competition. Instrumenting an athlete with the appropriate number of markers requires a lot of time and these markers may fall off during the analysis, which leads to incomplete data and requires new data capturing sessions and hence a waste of time and effort. Therefore, we present a novel multiview video-based markerless system that uses 2D joint detections per view (from OpenPose) to estimate their corresponding 3D positions while tackling the people association problem in the process to allow the tracking of multiple persons at the same time.Our proposed system can perform the tracking in real-time at 20-25 fps. Our results show a standard deviation between 9.6 and 23.7 mm for the lower body joints based on the raw measurements only. After filtering the data, the standard deviation drops to a range between 6.6 and 21.3 mm. Our proposed solution can be applied to a large number of applications, ranging from sports analysis to virtual classrooms where submillimeter precision is not necessarily required, but where the use of markers is impractical.