Tracking Performance in Endurance Racing Sports: Evaluation of the Accuracy Offered by Three Commercial GNSS Receivers Aimed at the Sports Market

Advances in global navigation satellite system (GNSS) technology have resulted in smaller and more accurate GNSS receivers, which have become increasingly suitable for calculating instantaneous performance parameters during sports competitions, for example by providing the difference in time between athletes at any location along a course. This study investigated the accuracy of three commercially available GNSS receivers directed at the sports market and evaluated their applicability for time analysis in endurance racing sports. The receivers evaluated were a 1 Hz wrist-worn standalone receiver (Garmin Forerunner 920XT, Gar-920XT), a 10 Hz standalone receiver (Catapult Optimeye S5, Cat-S5), and a 10 Hz differential receiver (ZXY-Go). They were validated against a geodetic, multi-frequency receiver providing differential position solutions (accuracy < 5 cm). Six volunteers skied four laps on a 3.05 km track prepared for cross-country skiing, with all four GNSS receivers measuring simultaneously. Deviations in position (horizontal plane, vertical, direction of travel) and speed (horizontal plane and direction of travel) were calculated. In addition, the positions of all receivers were mapped onto a mapping trajectory along the ski track, and a time analysis of all 276 possible pairs of laps was performed. Specifically, the time difference between any two skiers for each integer meter along the track was calculated. ZXY-Go, CAT-S5, and GAR-920XT had horizontal plane position errors of 2.09, 1.04, and 5.29 m (third quartile, Q3), and vertical precision 2.71, 3.89, and 13.35 m (interquartile range, IQR), respectively. The precision in the horizontal plane speed was 0.038, 0.072, and 0.66 m s-1 (IQR) and the time analysis precision was 0.30, 0.13, and 0.68 s (IQR) for ZXY-Go, Cat-S5, and Gar-920XT, respectively. However, the error was inversely related to skiing speed, implying that for the low speeds typically attained during uphill skiing, substantially larger errors can occur. Specifically, at 2.0 m s-1 the Q3 was 0.96, 0.36, and 1.90 s for ZXY-Go, Cat-S5, and Gar-920XT, respectively. In summary, the differential (ZXY-Go) and 10 Hz standalone (Cat-S5) receivers performed substantially better than the wrist-worn receiver (Gar-920XT) in terms of horizontal position and horizontal speed calculations. However, all receivers produced sub-second accuracy in the time analysis, except at very low skiing speeds

Propulsive Power in Cross-Country Skiing: Application and Limitations of a Novel Wearable Sensor-Based Method During Roller Skiing

Cross-country skiing is an endurance sport that requires extremely high maximal aerobic power. Due to downhill sections where the athletes can recover, skiers must also have the ability to perform repeated efforts where metabolic power substantially exceeds maximal aerobic power. Since the duration of these supra-aerobic efforts is often in the order of seconds, heart rate, and pulmonary VO2 do not adequately reflect instantaneous metabolic power. Propulsive power (Pprop) is an alternative parameter that can be used to estimate metabolic power, but the validity of such calculations during cross-country skiing has rarely been addressed. The aim of this study was therefore twofold: to develop a procedure using small non-intrusive sensors attached to the athlete for estimating Pprop during roller-skiing and to evaluate its limits; and (2) to utilize this procedure to determine the Pprop generated by high-level skiers during a simulated distance race. Eight elite male cross-country skiers simulated a 15 km individual distance race on roller skis using ski skating techniques on a course (13.5 km) similar to World Cup skiing courses. Pprop was calculated using a combination of standalone and differential GNSS measurements and inertial measurement units. The method's measurement error was assessed using a Monte Carlo simulation, sampling from the most relevant sources of error. Pprop decreased approximately linearly with skiing speed and acceleration, and was approximated by the equation Pprop(v,v˙) = −0.54·v −0.71·v˙ + 7.26 W·kg−1. Pprop was typically zero for skiing speeds >9 m·s−1, because the athletes transitioned to the tuck position. Peak Pprop was 8.35 ± 0.63 W·kg−1 and was typically attained during the final lap in the last major ascent, while average Pprop throughout the race was 3.35 ± 0.23 W·kg−1. The measurement error of Pprop increased with skiing speed, from 0.09 W·kg−1 at 2.0 m·s−1 to 0.58 W·kg−1 at 9.0 m·s−1. In summary, this study is the first to provide continuous measurements of Pprop for distance skiing, as well as the first to quantify the measurement error during roller skiing using the power balance principle. Therefore, these results provide novel insight into the pacing strategies employed by high-level skiers

Validity of Velocity Measurements of a Motorized Resistance Device During Change of Direction

The aim of this study was to determine validity of velocity measurements of a motorized resistance device (MRD) during change of direction (CoD). Eight male (age: 22.1 ± 4.2 yrs; weight: 83.3 ± 17.1 kg; height: 181.6 ± 12.6 cm) and three female participants (age: 21.7 ± 1.5 yrs; mass: 69.7 ± 2.4 kg; height: 167.0 ± 3.6 cm) completed the modified 505 CoD test (m505) with turning off the left and right foot while exposed to external loads (3, 6, and 9 kg) provided by the MRD. Three-dimensional kinematic data were measured (200 Hz) for all tests using a full-body marker set with an additional marker placed on the pulley used to attach the carabiner (CAR) at the end of the line from the MRD to the participant. Average velocity of overall center of mass (COMvel), pelvis (COMpelvis_vel), and pulley (CARvel) was then calculated and compared to the velocity measured by MRD (MRDvel) in 0.5 s intervals 1.5 s before and after CoD. Average velocities from these intervals were then compared using correlational, Bland–Altman analysis, coefficient of variation (CV), and statistical parametric mapping (SPM). Mostly, excellent correlations were observed and ranged from 0.93 to 1.00, 0.53 to 1.00 and 0.93 to 1.00 for the 3, 6, and 9 kg load conditions, respectively. CV values ranged from 0.3 to 3.2%, 0.8 to 4.3%, and 1.5 to 7.7% for the CARvel, COMpelvis_vel, and COMvel comparisons, respectively. The biases for CARvel comparisons ranged from −0.027 to 0.05 m/s, −0.246 to 0.128 m/s and −0.486 to 0.082 m/s across all load conditions and time intervals for the CARvel, COMpelvis_vel, and COMvel comparisons, respectively. SPM analysis indicated significant differences between MRDvel and COMvel and COMpelvis_vel over short time periods during the CoD, but no difference between MRDvel and CARvel. The velocity measurements obtained by a MRD during a m505 test are valid as low biases, low CV’s, and high correlations are observed for the MRDvel to CARvel comparison. As single points of measurement (i.e., laser) has been proven useful to assess other athletic tasks (i.e., sprint running), the single point CARvel comparison is an appropriate comparison for validating MRDvel measurements during the m505 test

Rate of force development relationships to muscle architecture and contractile behavior in the human vastus lateralis

In this study, we tested the hypotheses that (i) rate of force development (RFD) is correlated to muscle architecture and dynamics and that (ii) force–length–velocity properties limit knee extensor RFD. Twenty-one healthy participants were tested using ultrasonography and dynamometry. Vastus lateralis optimal fascicle length, fascicle velocity, change in pennation angle, change in muscle length, architectural gear ratio, and force were measured during rapid fixed-end contractions at 60° knee angle to determine RFD. Isokinetic and isometric tests were used to estimate individual force–length–velocity properties, to evaluate force production relative to maximal potential. Correlation analyses were performed between force and muscle parameters for the first three 50 ms intervals. RFD was not related to optimal fascicle length for any measured time interval, but RFD was positively correlated to fascicle shortening velocity during all intervals (r = 0.49–0.69). Except for the first interval, RFD was also related to trigonometry-based changes in muscle length and pennation angle (r = 0.45–0.63) but not to architectural gear ratio. Participants reached their individual vastus lateralis force–length–velocity potential (i.e. their theoretical maximal force at a given length and shortening velocity) after 62 ± 24 ms. Our results confirm the theoretical importance of fascicle shortening velocity and force–length–velocity properties for rapid force production and suggest a role of fascicle rotation.publishedVersio

Reliability of phase-specific outcome measurements in change-of-direction tests using a motorized resistance device

This study aims to determine test-retest reliability of phase-specific information during initial acceleration, deceleration, and re-acceleration phases of different change-of-direction (CoD) tests using a motorized resistance device (MRD). A total of 21 participants (16 males and five females, with mean age of 22.3 ± 3.9 years, body mass of 75.2 ± 6.9 kg, height of 177.9 ± 6.8 cm) completed the modified 505 (m505), 10-0-5, and 15-0-5 CoD tests on four different test sessions while exposed to an external load (3 kg) provided by the MRD. Outcome variables included overall and phase-specific kinetic (force, power, and impulse) and kinematic (time, distance, velocity, and acceleration/deceleration) data during the initial acceleration, deceleration, and re-acceleration phases. The deceleration and re-acceleration phases were further divided into two subphases, namely, early and late subphases, using 50% of maximum velocity. Reliability was assessed using an intraclass correlation coefficient (ICC), coefficient of variation (CV), typical error (TE), and minimal detectable change (MDC). Good to excellent ICC values (>0.75) and acceptable (<10%) to good (<5%) CV values were observed for most outcome measurements. Specifically, 80.1% (822 out of 1,026) of all variables showed good or better relative reliability (i.e., ICC ≥ 0.75), while 97.0% (995 out of 1,026) of all variables showed acceptable or better absolute reliability (i.e., CV < 10%). In conclusion, the present study demonstrates that the MRD can obtain reliable phase-specific outcome measurements across different CoD tests, providing coaches and researchers with new opportunities to advance our understanding of CoD ability and inform more advanced CoD training prescriptions

Accuracy of non-invasive cuffless blood pressure in the intensive care unit: Promises and challenges

ObjectiveContinuous non-invasive cuffless blood pressure (BP) monitoring may reduce adverse outcomes in hospitalized patients if accuracy is approved. We aimed to investigate accuracy of two different BP prediction models in critically ill intensive care unit (ICU) patients, using a prototype cuffless BP device based on electrocardiogram and photoplethysmography signals. We compared a pulse arrival time (PAT)-based BP model (generalized PAT-based model) derived from a general population cohort to more complex and individualized models (complex individualized models) utilizing other features of the BP sensor signals.MethodsPatients admitted to an ICU with indication of invasive BP monitoring were included. The first half of each patient’s data was used to train a subject-specific machine learning model (complex individualized models). The second half was used to estimate BP and test accuracy of both the generalized PAT-based model and the complex individualized models. A total of 7,327 measurements of 15 s epochs were included in pairwise comparisons across 25 patients.ResultsThe generalized PAT-based model achieved a mean absolute error (SD of errors) of 7.6 (7.2) mmHg, 3.3 (3.1) mmHg and 4.6 (4.4) mmHg for systolic BP, diastolic BP and mean arterial pressure (MAP) respectively. Corresponding results for the complex individualized model were 6.5 (6.7) mmHg, 3.1 (3.0) mmHg and 4.0 (4.0) mmHg. Percentage of absolute errors within 10 mmHg for the generalized model were 77.6, 96.2, and 89.6% for systolic BP, diastolic BP and MAP, respectively. Corresponding results for the individualized model were 83.8, 96.2, and 94.2%. Accuracy was significantly improved when comparing the complex individualized models to the generalized PAT-based model in systolic BP and MAP, but not diastolic BP.ConclusionA generalized PAT-based model, developed from a different population was not able to accurately track BP changes in critically ill ICU patients. Individually fitted models utilizing other cuffless BP sensor signals significantly improved accuracy, indicating that cuffless BP can be measured non-invasively, but the challenge toward generalizable models remains for future research to resolve

Quantitative technique analysis in XC-skiing

Due to the complexity of human locomotion, a quantitative analysis of technique in sports is often difficult. For that reason a qualitative approach is most widespread, in particular amongst practitioners. The qualitative approach offers fast and individually adjusted feedback from a technical coach. However, it is also prone to bias due to the coaches (or researchers) prior assumptions. A quantitative approach is less prone to such bias. This thesis suggests two different methods for a quantitative approach to technique analysis. Both methods are applied to a group of 6 elite cross country skiers using the V2 (or double dance) technique on a roller skiing treadmill. The methods are used to assess links between technique and performance, quantified by FIS-points. The first method was an extensive analysis of the skiers 3D movement patterns, quantified by the coordinates of 41 markers positioned on the athletes skin and equipment. These markers determined the skiers posture. A dimensional reduction technique (PCA) was used to decompose the complex, but highly redundant set of postures into a comprehensible amount of uncorrelated variables. Each of these uncorrelated variables represented multi-segment movements, which could be visualized as movements by a stick figure. Also, the center of mass (COM) of the athletes were determined by a segment model based on the markers, which enabled an assessment of the effect of postural movements to whole body movements. Normalization and weighting procedures novel to the field of sports science enabled a direct comparison of the postural movements between athletes. The second method used a much simpler approach, and consisted solely of measurements from an accelerometer and a gyroscope (both 3 axis) positioned at the athletes sacrum. The aim was to assess whether such a system could record interesting differences between athletes. If it could, the simplicity of the experimental setup, and the light weight of the sensor suggest that quantitative measurement of technique would be feasible both in regular training, and even in competition situations. Both methods proved able to identify differences in skiing technique, even in a group consisting solely of elite skiers. Some of the differences appeared to relate to the FIS-point ranking of the athletes, which suggested that these features could be important for performance. In particular, the coordination between major hip flexor musculature and vertical COM motion appeared relevant, and suggested a more beneficial utilization of potential in the best ranked skiers. A second aspect appeared to be a preference in the best ranked skiers to use a smaller lateral COM excursion, which was closely linked to the axial rotation of the pelvis during the leg push. Also included in this thesis are two appendices. Appendix A outlines the method used to obtain drift free measurements of displacements from the accelerometer and gyroscope output, and assess the accuracy of these measurements. Appendix B is included to show that sensors of similar specifications as those used in appendix A are incorporated in current marked smart phones, and investigates the possibility use smart phones as a tool for technique analysis

Classification of Cross-Country Ski Skating Sub-Technique Can Be Automated Using Carrier-Phase Differential GNSS Measurements of the Head’s Position

Position–time tracking of athletes during a race can provide useful information about tactics and performance. However, carrier-phase differential global navigation satellite system (dGNSS)-based tracking, which is accurate to about 5 cm, might also allow for the extraction of variables reflecting an athlete’s technique. Such variables include cycle length, cycle frequency, and choice of sub-technique. The aim of this study was to develop a dGNSS-based method for automated determination of sub-technique and cycle characteristics in cross-country ski skating. Sub-technique classification was achieved using a combination of hard decision rules and a neural network classifier (NNC) on position measurements from a head-mounted dGNSS antenna. The NNC was trained to classify the three main sub-techniques (G2–G4) using optical marker motion data of the head trajectory of six subjects during treadmill skiing. Hard decision rules, based on the head’s sideways and vertical movement, were used to identify phases of turning, tucked position and G5 (skating without poles). Cycle length and duration were derived from the components of the head velocity vector. The classifier’s performance was evaluated on two subjects during an in-field roller skiing test race by comparison with manual classification from video recordings. Classification accuracy was 92–97% for G2–G4, 32% for G5, 75% for turning, and 88% for tucked position. Cycle duration and cycle length had a root mean square (RMS) deviation of 2–3%, which was reduced to &lt;1% when cycle duration and length were averaged over five cycles. In conclusion, accurate dGNSS measurements of the head’s trajectory during cross-country skiing contain sufficient information to classify the three main skating sub-techniques and characterize cycle length and duration

Classification of cross-country ski skating sub-technique can be automated using carrier-phase differential GNSS measurements of the head's position

Position–time tracking of athletes during a race can provide useful information about tactics and performance. However, carrier-phase differential global navigation satellite system (dGNSS)-based tracking, which is accurate to about 5 cm, might also allow for the extraction of variables reflecting an athlete’s technique. Such variables include cycle length, cycle frequency, and choice of sub-technique. The aim of this study was to develop a dGNSS-based method for automated determination of sub-technique and cycle characteristics in cross-country ski skating. Sub-technique classification was achieved using a combination of hard decision rules and a neural network classifier (NNC) on position measurements from a head-mounted dGNSS antenna. The NNC was trained to classify the three main sub-techniques (G2–G4) using optical marker motion data of the head trajectory of six subjects during treadmill skiing. Hard decision rules, based on the head’s sideways and vertical movement, were used to identify phases of turning, tucked position and G5 (skating without poles). Cycle length and duration were derived from the components of the head velocity vector. The classifier’s performance was evaluated on two subjects during an in-field roller skiing test race by comparison with manual classification from video recordings. Classification accuracy was 92–97% for G2–G4, 32% for G5, 75% for turning, and 88% for tucked position. Cycle duration and cycle length had a root mean square (RMS) deviation of 2–3%, which was reduced to <1% when cycle duration and length were averaged over five cycles. In conclusion, accurate dGNSS measurements of the head’s trajectory during cross-country skiing contain sufficient information to classify the three main skating sub-techniques and characterize cycle length and duration

Mean Euclidean differences between reconstruction and measurement for gaps in single markers.

<p>Mean Euclidean differences between reconstruction and measurement for gaps in single markers.</p