Int J Sports Med 2018; 39(10): 802-808
DOI: 10.1055/s-0044-100793
Training & Testing
© Georg Thieme Verlag KG Stuttgart · New York

Inertial Sensors are a Valid Tool to Detect and Consistently Quantify Jumping

Rhys Spangler
1   Centre for Sport Research (CSR), School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia
,
Timo Rantalainen
2   Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Science, Deakin University, Burwood, Australia
,
Paul B. Gastin
1   Centre for Sport Research (CSR), School of Exercise and Nutrition Sciences, Deakin University, Burwood, Australia
,
Daniel Wundersitz
3   Exercise Physiology, La Trobe University, Flora Hill, Australia
› Author Affiliations
Further Information

Publication History



accepted 28 December 2017

Publication Date:
19 July 2018 (online)

Abstract

Considering the large and repetitive loads associated with jumping in team sports, automatic detection and quantification of jumping may show promise in reducing injury risks. The aim of this study was to validate commercially available inertial-movement analysis software to detect and quantify jumping in team sports. In addition, the test-retest reliability of the software to quantify jumping was assessed. Seventy-six healthy male participants completed a team sport circuit six times containing seven common movements (including three countermovement and two single-leg jumps) whilst wearing an inertial sensor (Catapult Sports, Australia). Jump detection accuracy was assessed by comparing the known number of jumps to the number recorded by the inertial movement analysis software. A further 27 participants separately performed countermovement and single-leg jumps at 33%, 66% and 100% of maximal jump height over two sessions. Jump height quantification accuracy was assessed by comparing criterion three-dimensional motion analysis-derived heights to that recorded by the inertial movement analysis software. Test-retest reliability was assessed by comparing recorded jump heights between both testing sessions. Catapult’s inertial movement analysis software displayed excellent jump detection accuracy (96.9%) and test-retest jump height quantification reliability (ICC: 0.86 [countermovement jump], 0.88 [single-leg jump]). However, significant mean bias (–2.74 cm [95% LoA –10.44 – 4.96]) was observed for jump height quantification. Overall, Catapult’s inertial movement analysis software appears to be a suitable method of automatically detecting jumping in team sports, and although reliable, caution is advised when using the IMA software to quantify jump height.

 
  • References

  • 1 Catapult Sports. Sprint help – inertial movement analysis (IMA). (2012). Available in Catapult Sport Sprint Software. www.catapultsports.com
  • 2 Abdi H. Coefficient of variation. In: Salkind NJ. (ed). Encyclopedia of Research Design. SAGE Publications; 2010: 169-171
  • 3 Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999; 8: 135-160
  • 4 Casartelli N, Müller R, Maffiuletti NA. Validity and reliability of the myotest accelerometric system for the assessment of vertical jump height. J Strenght Cond Res 2010; 24: 3186-3193
  • 5 Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci Model Dev 2014; 7: 1247-1250
  • 6 Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assessment 1994; 6: 284-290
  • 7 Edgecomb SJ, Norton KI. Comparison of global positioning and computer-based tracking systems for measuring player movement distance during Australian football. J Sci Med Sport 2006; 9: 25-32
  • 8 Elvin NG, Elvin AA, Arnoczky SP. Correlation between ground reaction force and tibial acceleration in vertical jumping. J Appl Biomech 2007; 23: 180
  • 9 Glüer CC, Blake G, Lu Y, Blunt BA, Jergas M, Genant HK. Accurate assessment of precision errors: how to measure the reproducibility of bone densitometry techniques. Osteoporos Int 1995; 5: 262-270
  • 10 Harriss DJ, Macsween A, Atkinson G. Standards for ethics in sport and exercise science research: 2018 update. Int J Sports Med 2017; 38: 1126-1131
  • 11 Impellizzeri FM, Rampinini E, Maffiuletti N, Marcora SM. A vertical jump force test for assessing bilateral strength asymmetry in athletes. Med Sci Sports Exerc 2007; 39: 2044-2050
  • 12 Iwamoto J, Takeda T. Stress fractures in athletes: review of 196 cases. J Orthop Sci 2003; 8: 273-278
  • 13 Jarning JM, Mok KM, Hansen BH, Bahr R. Application of a tri-axial accelerometer to estimate jump frequency in volleyball. Sports Biomech 2015; 14: 95-105
  • 14 Kelly D, Coughlan GF, Green BS, Caulfield B. Automatic detection of collisions in elite level rugby union using a wearable sensing device. Sports Eng 2012; 15: 81-92
  • 15 Rodgers LJ, Nicewander WA. Thirteen ways to look at the correlation coefficient. Am Stat 1988; 42: 59-66
  • 16 Lian ØB, Engebretsen L, Bahr R. Prevalence of jumper’s knee among elite athletes from different sports: a cross-sectional study. Am J Sports Med 2005; 33: 561-567
  • 17 Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics 1989; 45: 255-268
  • 18 MacDonald K, Bahr R, Baltich J, Whittaker JL, Meeuwisse WH. Validation of an inertial measurement unit for the measurement of jump count and height. Phys Ther Sport 2016; 25: 15-19
  • 19 McClay IS, Robinson JR, Andriacchi TP, Frederick EC, Gross T, Martin P, Valiant G, Williams KR, Cavanagh PC. A profile of ground reaction forces in professional basketball. J Appl Biomech 1994; 10: 222-236
  • 20 McInnes S, Carlson J, Jones C, McKenna M. The physiological load imposed on basketball players during competition. J Sports Sci 1995; 13: 387-397
  • 21 McNamara DJ, Gabbett TJ, Chapman P, Naughton G, Farhart P. The validity of microsensors to automatically detect bowling events and counts in cricket fast bowlers. Int J Sports Physiol Perform 2015; 10: 71-75
  • 22 Mitchell E, Monaghan D, O'Connor NE. Classification of sporting activities using smartphone accelerometers. Sensors 2013; 13: 5317-5337
  • 23 Murray NB, Black GM, Whiteley RJ, Gahan P, Cole MH, Utting A, Gabbett TJ. Automatic detection of pitching and throwing events in baseball with inertial measurement sensors. Int J Sports Physiol Perform 2016; 12: 533-537
  • 24 Nuzzo JL, Anning JH, Scharfenberg JM. The reliability of three devices used for measuring vertical jump height. J Strength Cond Res 2011; 25: 2580-2590
  • 25 Picerno P, Camomilla V, Capranica L. Countermovement jump performance assessment using a wearable 3D inertial measurement unit. J Sports Sci 2011; 29: 139-146
  • 26 Ravi N, Dandekar N, Mysore P, Littman ML. Activity recognition from accelerometer data. Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence 2005; Pittsburgh, PA, USA: 1541–1546
  • 27 Roberts S, Trewartha G, Stokes K. A comparison of time-motion analysis methods for field-based sports. Int J Sports Physiol Perform 2006; 1: 386-397
  • 28 Tillman MD, Hass CJ, Brunt D, Bennett GR. Jumping and landing techniques in elite women’s volleyball. J Sports Sci Med 2004; 3: 30-36
  • 29 Wundersitz DW, Gastin PB, Robertson S, Davey PC, Netto KJ. Validation of a trunk-mounted accelerometer to measure peak impacts during team sport movements. Int J Sports Med 2015; 36: 742-746
  • 30 Wundersitz DW, Josman C, Gupta R, Netto KJ, Gastin PB, Robertson S. Classification of team sport activities using a single wearable tracking device. J Biomech 2015; 48: 3975-3981
  • 31 Zaki MJ, Meira Jr W. Data mining and analysis: fundamental concepts and algorithms. Cambridge: Cambridge University Press; 2014