Abstract
The increasing interest in assessing physical demands in team sports has led to the development of multiple sports related monitoring systems. Due to technical limitations, these systems primarily could be applied to outdoor sports, whereas an equivalent indoor locomotion analysis is not established yet. Technological development of inertial measurement units (IMU) broadens the possibilities for player monitoring and enables the quantification of locomotor movements in indoor environments. The aim of the current study was to validate an IMU measuring by determining average and peak human acceleration under indoor conditions in team sport specific movements. Data of a single wearable tracking device including an IMU (Optimeye S5, Catapult Sports, Melbourne, Australia) were compared to the results of a 3D motion analysis (MA) system (Vicon Motion Systems, Oxford, UK) during selected standardized movement simulations in an indoor laboratory (n = 56). A low-pass filtering method for gravity correction (LF) and two sensor fusion algorithms for orientation estimation [Complementary Filter (CF), Kalman-Filter (KF)] were implemented and compared with MA system data. Significant differences (p < 0.05) were found between LF and MA data but not between sensor fusion algorithms and MA. Higher precision and lower relative errors were found for CF (RMSE = 0.05; CV = 2.6%) and KF (RMSE = 0.15; CV = 3.8%) both compared to the LF method (RMSE = 1.14; CV = 47.6%) regarding the magnitude of the resulting vector and strongly emphasize the implementation of orientation estimation to accurately describe human acceleration. Comparing both sensor fusion algorithms, CF revealed slightly lower errors than KF and additionally provided valuable information about positive and negative acceleration values in all three movement planes with moderate to good validity (CV = 3.9 – 17.8%). Compared to x- and y-axis superior results were found for the z-axis. These findings demonstrate that IMU-based wearable tracking devices can successfully be applied for athlete monitoring in indoor team sports and provide potential to accurately quantify accelerations and decelerations in all three orthogonal axes with acceptable validity. An increase in accuracy taking magnetometers in account should be specifically pursued by future research.
Highlights
Knowledge about physical demands in team sports has become increasingly important to optimize training programs, to enhance physical performance and to prevent injuries (Fox et al, 2017; Vanrenterghem et al, 2017)
Post-hoc Mann-Whitney-U tests revealed, that without orientation estimation the gravity component could not accurately be eliminated in all three axes leading to significant differences for totaly, mean/peak accelerationy, mean/peak decelerationy, peak decelerationx and peak decelerationz between Low-Pass Filter (LF) data and motion analysis (MA) system (p < 0.017, r = 0.39 – 0.50), whereas no significant differences were found between Complementary Filters (CF) data and MA system
The findings of this study show that wearable tracking devices containing a Mechanical Systems (MEMS)-based sensor have a great potential to be applied indoors as valid tool to determine accelerations and decelerations during of team sport specific movement including walking, running, jumping and change of direction simulations
Summary
Knowledge about physical demands in team sports has become increasingly important to optimize training programs, to enhance physical performance and to prevent injuries (Fox et al, 2017; Vanrenterghem et al, 2017). Several approaches were proposed to estimate the tracking device’s orientation with respect to the earth’s coordinate system, e.g., sensor fusion algorithms without GPS (Madgwick et al, 2011; Sabatini, 2011a; Valenti et al, 2015) Such algorithms commonly combine accelerometer and gyroscope signals to compute the device’s attitude (pitch and roll angles) relative to the direction of gravity. Validity of a commercially available IMU-based monitoring system that relies on KF-techniques have been proven regarding the magnitude of the resulting acceleration vector or the instantaneous rate of change of acceleration (Wundersitz et al, 2013, 2015a,b) Based on those parameters activity profiles and quantification of loads during games and training have been proposed for indoor team sports (Montgomery et al, 2010; Schelling and Torres, 2016; Luteberget and Spencer, 2017). Interpretation of individual locomotion might be beneficial for individualization of training programs, supervision of rehabilitation processes or control of each player’s injury risk
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