Validation of Wearable Sensors during Team Sport-Specific Movements in Indoor Environments

Abstract

The aim of this study was to determine possible influences, including data processing and sport-specific demands, on the validity of acceleration measures by an inertial measurement unit (IMU) in indoor environments. IMU outputs were compared to a three-dimensional (3D) motion analysis (MA) system and processed with two sensor fusion algorithms (Kalman filter, KF; Complementary filter, CF) at temporal resolutions of 100, 10, and 5 Hz. Athletes performed six team sport-specific movements whilst wearing a single IMU. Mean and peak acceleration magnitudes were analyzed. Over all trials (n = 1093), KF data overestimated MA resultant acceleration by 0.42 ± 0.31 m∙s−2 for mean and 4.18 ± 3.68 m∙s−2 for peak values, while CF processing showed errors of up to 0.57 ± 0.41 m∙s−2 and −2.31 ± 2.25 m∙s−2, respectively. Resampling to 5 Hz decreased the absolute error by about 14% for mean and 56% for peak values. Still, higher acceleration magnitudes led to a large increase in error. These results indicate that IMUs can be used for assessing accelerations in indoor team sports with acceptable means. Application of a CF and resampling to 5 Hz is recommended. High-acceleration magnitudes impair validity to a large degree and should be interpreted with caution.

Duration-Specific Peak Acceleration Demands During Professional Female Basketball Matches

“…the IMU-based device in the horizontal plane (Roell et al, 2019), it can be assumed that measurement errors affect the measurement quality of |acc res | and |acc hor |. Consequently, increased measurement bias reduces the fitting quality in these movement planes.…”

“…To exclude phase-shift dual pass filtering and a correction of the cut-off frequency to 9.97 Hz was applied (Winter, 2009 ). The devices' attitude was corrected through application of a previously-validated complementary filter algorithm on the accelerometer and gyroscope data (compiled and edited with C++) (Valenti et al, 2015 ; Roell et al, 2018 , 2019 ). For higher congruence between IMU data and actual body movements, attitude-corrected accelerometer data were resampled to 5 Hz by averaging (Roell et al, 2019 ).…”

“…The devices' attitude was corrected through application of a previously-validated complementary filter algorithm on the accelerometer and gyroscope data (compiled and edited with C++) (Valenti et al, 2015 ; Roell et al, 2018 , 2019 ). For higher congruence between IMU data and actual body movements, attitude-corrected accelerometer data were resampled to 5 Hz by averaging (Roell et al, 2019 ). Magnitudes of the 3D resultant acceleration vector (|acc res |), the horizontal vector (|acc hor |), and the vertical acceleration vector (|acc vert |) were calculated according to the following formulas.…”

“…The validity of the IMUs has to be mentioned as a limitation of this study. Although the data processing system has previously been validated during team sport-specific movements against a 3D motion analysis system (Roell et al, 2018(Roell et al, , 2019, reduced accuracy has been reported for peak values of the horizontal resultant acceleration. Using the median as a robust variable against outliers for every time interval in this study is expected to account for this error and minimize inaccuracies in the horizontal plane.…”

“…Use of GPS units is restricted to outdoor environments, thus inertial measurement units (IMU) serve as an alternative in indoor team sports. Accurate estimations of acceleration in multiple movement planes during sport-specific movements have been shown after adequate data processing (Roell et al, 2019 ). However, time-ordered visualization of these data remains rather disorganized, which emphasizes the need for a standardized description of actual workloads.…”