Validity of a Wearable Accelerometer Device to Measure Average Acceleration Values During High-Speed Running

Alexander, Jeremy; Hopkinson, Trent L.; Wundersitz, Daniel; Serpell, Benjamin G.; Mara, Jocelyn K; Ball, Nick

doi:10.1519/jsc.0000000000001396

Cited by 24 publications

(25 citation statements)

References 27 publications

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“…Another approach that has gained acceptance within the area of player tracking is wearable, acceleration-based tracking devices that incorporate microelectromechanical systems (MEMS) gyroscopes, magnetometers, and accelerometers into a single player-worn unit [ 4 ]. The devices utilize tri-axial accelerometers that are not positional based, but movement based (anterior-posterior, medial-lateral, and vertical) [ 5 ], to obtain descriptors of sports activities, such us accelerations, decelerations, jumps, change of direction or other accelerometer-derived measurements [ 6 ]. One such derived measurement is the PlayerLoad™ (Catapult Innovations, Melbourne, Australia), which is used to describe and quantify an athlete’s external workload [ 7 – 10 ].…”

Section: Introductionmentioning

confidence: 99%

Validity and reliability of an accelerometer-based player tracking device

et al. 2018

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This study aimed to determine the intra- and inter-device accuracy and reliability of wearable athletic tracking devices, under controlled laboratory conditions. A total of nineteen portable accelerometers (Catapult OptimEye S5) were mounted to an aluminum bracket, bolted directly to an Unholtz Dickie 20K electrodynamic shaker table, and subjected to a series of oscillations in each of three orthogonal directions (front-back, side to side, and up-down), at four levels of peak acceleration (0.1g, 0.5g, 1.0g, and 3.0g), each repeated five times resulting in a total of 60 tests per unit, for a total of 1140 records. Data from each accelerometer was recorded at a sampling frequency of 100Hz. Peak accelerations recorded by the devices, Catapult PlayerLoad™, and calculated player load (using Catapult’s Cartesian formula) were used for the analysis. The devices demonstrated excellent intradevice reliability and mixed interdevice reliability. Differences were found between devices for mean peak accelerations and PlayerLoad™ for each direction and level of acceleration. Interdevice effect sizes ranged from a mean of 0.54 (95% CI: 0.34–0.74) (small) to 1.20 (95% CI: 1.08–1.30) (large) and ICCs ranged from 0.77 (95% CI: 0.62–0.89) (very large) to 1.0 (95% CI: 0.99–1.0) (nearly perfect) depending upon the magnitude and direction of the applied motion. When compared to the player load determined using the Cartesian formula, the Catapult reported PlayerLoad™ was consistently lower by approximately 15%. These results emphasize the need for industry wide standards in reporting validity, reliability and the magnitude of measurement errors. It is recommended that device reliability and accuracy are periodically quantified.

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Section: Introductionmentioning

confidence: 99%

Validity and reliability of an accelerometer-based player tracking device

et al. 2018

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“…Previous studies with relevance for team sports activities have reported that raw accelerometer data show insufficient accuracy as a measure of impacts during jumping movements or average acceleration during high-speed running (Tran et al, 2010 ; Alexander et al, 2016 ). The authors assumed these discrepancies to result from a lack of gravity-compensation.…”

Section: Discussionmentioning

confidence: 99%

“…To assess the agreement between IMU-based variables and MA system variables mean bias, root mean square error (RMSE; Barnston, 1992 ), 95% limits of agreement (Atkinson and Nevill, 1998 ), Spearman's correlation coefficient and the percentage difference in the mean between criterion (MA) and measurement (CF, KF, LF) expressed as coefficient of variation (CV; Hopkins, 2000 ) were calculated for mean and peak acceleration values in x, y and z-axes (CF, LF, MA) as well as average and peak magnitude of the resulting vector (CF, KF, MA). According to previous research evaluating the relative error of IMU-based acceleration variables a CV ≤5% was considered as small, CV ≥5% and <20% as moderate and CV ≥20% as large (Wundersitz et al, 2015b ; Alexander et al, 2016 ). To approve the acceptable use of MEMS-based sensors in the field a CV <20% was intended (Tran et al, 2010 ; Wundersitz et al, 2015a ).…”

Section: Methodsmentioning

confidence: 99%

Player Monitoring in Indoor Team Sports: Concurrent Validity of Inertial Measurement Units to Quantify Average and Peak Acceleration Values

et al. 2018

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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.

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“…Screening of the selected papers and the 23 reviews produced 77 additional papers. As a result, 286 studies were identified for inclusion in the systematic review [ 24 , 41 , 43 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , ...…”

Section: Trendsmentioning

confidence: 99%

“…Subject specific models were also developed to estimate CoM velocity during running, based on step rate measures [ 242 ]. The errors entailed in instantaneous velocity estimation can be partially overcome by limiting the velocity analysis to average values in cyclic sports, for example, for each swimming stroke [ 104 , 105 , 106 , 108 , 290 ] turning action [ 192 ], lane [ 63 , 80 , 323 ], running cycle [ 162 , 328 ], or mean velocity in running [ 56 , 160 ]. The average velocity of progression was also obtained from ski IMUs, removing drift and assuming zero-velocity during a portion of the ski thrust phase, in cross-country skiing [ 120 , 237 ] and in uphill mountaineering [ 121 ].…”

Section: Trendsmentioning

confidence: 99%

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Camomilla

Bergamini

Fantozzi

et al. 2018

Sensors

391

302

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Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

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Validity of a Wearable Accelerometer Device to Measure Average Acceleration Values During High-Speed Running

Cited by 24 publications

References 27 publications

Validity and reliability of an accelerometer-based player tracking device

Validity and reliability of an accelerometer-based player tracking device

Player Monitoring in Indoor Team Sports: Concurrent Validity of Inertial Measurement Units to Quantify Average and Peak Acceleration Values

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

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