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Journal of Sports Sciences
ISSN: 0264-0414 (Print) 1466-447X (Online) Journal homepage: https://www.tandfonline.com/loi/rjsp20
Caution using data from triaxial accelerometers
housed in player tracking units during running
Suzi Edwards, Samuel White, Seaton Humphreys, Robert Robergs & Nicholas
O’Dwyer
To cite this article: Suzi Edwards, Samuel White, Seaton Humphreys, Robert Robergs & Nicholas O’Dwyer (2019) Caution using data from triaxial accelerometers housed in player tracking units during running, Journal of Sports Sciences, 37:7, 810-818, DOI: 10.1080/02640414.2018.1527675 To link to this article: https://doi.org/10.1080/02640414.2018.1527675
Published online: 11 Oct 2018.
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SPORTS MEDICINE AND BIOMECHANICS
Caution using data from triaxial accelerometers housed in player tracking units
during running
Suzi Edwards a,b,c, Samuel Whitea, Seaton Humphreysc, Robert Robergs c,d and Nicholas O’Dwyer c,e
aSchool of Environmental and Life Sciences, University of Newcastle, Ourimbah, Australia;bPriority Research Centre for Physical Activity and
Nutrition, University of Newcastle, Callaghan, Australia;cSchool of Exercise Science, Sport and Health, Charles Sturt University, Bathurst, Australia; dSchool of Exercise and Nutrition Sciences, Queensland University of Technology, Brisbane, Australia;eDiscipline of Exercise and Sport Science,
University of Sydney, Sydney, Australia
ABSTRACT
Usage of accelerometers within player tracking devices in sport to quantify load, vertical ground reaction force (vGRF) or energy expenditure is contrary to placement guidelines. This study aimed to determine whether trunk-mounted accelerometers were a valid and reliable method to estimate thoracic segment or centre of gravity (COG) acceleration or vGRF, and the whether the elasticised harness contributes to the overestimation of acceleration. Ten male amateur rugby players performed five linear running tasks per lower limb at three speeds, twice, each with a different player tracking unit. Three-dimensional data were recorded and triaxial accelerometers were attached lateral to the device on the harness and skin and both shanks. Accelerometers demonstrated poor reliability (ICC:0.0–0.67), high variability (CV%:14–33%) and change in mean (41–160%), and were not valid to estimate vertical acceleration of the COG and thoracic segment nor vGRF. Caution is advised when utilising trunk-mounted triaxial accelerometer data as it is not a valid or reliable means to estimate peak vertical acceleration for its thoracic location nor whole-body COG acceleration or vGRF during running. To improve player tracking instrument validity and reliability, a new attachment method and/or harness material(s), that reduce or eliminate extraneous acceleration during running, are urgently required.
ARTICLE HISTORY
Accepted 6 September 2018
KEYWORDS
Biomechanics; athletic training; motion analysis; physical performance
Introduction
Current gold-standard methods to measure performance of human movement, such as three-dimensional (3D) motion analysis systems and/or force platforms, are costly, require highly-trained personnel with a specialised laboratory, can be invasive to the participant (McLean et al.,2005), time-con-suming, and also cannot be used in the normal competition and/or training environment. Since it’s often unrealistic for sports to employ gold-standard methodology, the use of iner-tial measurement units (IMU) such as player tracking devices has become commonplace to record training sessions and match play information. Triaxial accelerometers housed within player tracking devices attached at the thoracic region (i.e. trunk-mounted accelerometry) are now the most commonly used IMUs that have been used to measure load (Boyd, Ball and Aughey 2011; Colby, Dawson, Heasman, Rogalski, & Gabbett, 2014), ground reaction force (GRF) (Wundersitz, Gastin, Richter, Robertson, & Netto,2015), stiffness (Buchheit, Gray, & Morin,2015) or energy expenditure (Walker, McAinch, Sweeting, & Aughey, 2016). This usage contradicts acceler-ometer attachment location guidelines that they should be attached to the shank to estimate GRF (Hennig & Lafortune,
1991) and approximate impact load (Colby et al.,2014); and to the pelvic region, close to the centre-of-gravity (COG) (Le Huec, Saddiki, Franke, Rigal, & Aunoble, 2011) for energy
expenditure (Rosenberger et al.,2013). Accelerometer attach-ment location is of critical concern due to acceleration attenuation, whereby acceleration magnitudes are dissipated superiorly throughout the kinetic chain during ground contact (Lafortune, Lake, & Hennig, 1996). This issue of attachment location was recently highlighted by Nedergaard et al. (2017) who showed weak to moderate correlations between segmen-tal accelerations from body-worn accelerometry, and the authors cautioned interpretation to estimate whole-body mechanical loading. Thus this questions the use of trunk-mounted accelerometers to estimate acceleration of segments at locations inferior to the trunk segment, such as shank segment acceleration.
The attachment ideally should be on a bony surface to minimise soft tissue movement artefact and the overestima-tion of peak acceleraoverestima-tion (Yang & Hsu, 2010). Yet in practice, these devices are worn in an elasticised harness usually attached between T1-T6 vertebrae. While it has been postu-lated that the harnesses elasticity can create “whipping” movements that are the likely cause of the extraneous accel-erations reported by trunk-mounted accelerometers com-pared to gold-standard measures (Wundersitz, Gastin, Richter, & Netto, 2013; Wundersitz, Gastin, Robertson, Davey, & Netto, 2015), this suggestion remains unsubstantiated (Nedergaard et al.,2017).
CONTACTSuzi Edwards [email protected] School of Environmental and Life Sciences, University of Newcastle, 10 Chittaway Rd, Ourimbah, NSW 2258, Australia
https://doi.org/10.1080/02640414.2018.1527675
Previous trunk-mounted accelerometry research has attenu-ated this known acceleration overestimation by using low-pass filters with 6–12Hz cut-off frequencies (Wundersitz, Gastin, Richter, Robertson and Netto,2015, Wundersitz, Gastin, Richter and Netto, 2013, Wundersitz, Gastin, Robertson, Davey and Netto,2015; Wundersitz, Netto, Aisbett, & Gastin,2013). When it is known these high-intensity human movements occur between 10–20 Hz (Nigg & Wakeling,2001), cut-off frequencies < 20Hz run the risk of attenuating relevant signal content at higher frequencies of human movement and over-smoothing the data. Other research claiming validity of trunk-mounted accelerometry has compared it to non-gold-standard measures including timing gates (Alexander et al.,2016) and other types of accelerometers (Nedergaard et al.,2017). Whereas Simons and Bradshaw (2016) have developed dubious criteria for “good” reliability, with the peak resultant GRF or acceleration CV% and ICC between 8–9.99 and 0.8–0.89, respectively; criteria that tra-ditionally are not classified as “good” reliability in the sport science literature (Hopkins, Marshall, Batterham, & Hanin,2009). Therefore, this current study aimed to determine whether the trunk-mounted accelerometer housed within a player tracking device was a valid and reliable means to estimate thoracic seg-ment acceleration, COG acceleration, or vGRF during running. This current study also investigated if the overestimation of the trunk-mounted accelerometry compared to gold standard mea-sures was due to the movement of the elasticised harness and/or skin movement.
Methods Participants
Ten male rugby union players who were currently playing for the University’s rugby team within the local competition (age: 21 ± 2 yr; height: 1.81 ± 0.50 m; mass: 81.8 ± 11.1 kg) were recruited and provided written informed consent. The institu-tion’s Human Research Ethics Committee approved the meth-odology for this research (HREC 2014/122).
Each participant was assigned one (n = 4) or two (n = 6) GPSports device (SPI HPU, GPSports Pty. Ltd., Canberra, Australia) in a randomised order. The GPSports device housed a triaxial accelerometer (100Hz; 16G; mass: 66g; dimensions: 74mm*42mm*16mm). The device was placed in the pouch of the manufacturer’s harness, approximately located between the T1 to T6 vertebrae. Two wireless Trigno electromyography devices with inbuilt triaxial accelerometers with their electro-myography function disabled (148Hz, 14G, Delsys, Natick, MA, USA) were used to investigate if the overestimation of the player tracking accelerometer was due to the movement of the elasticised harness and/or skin movement. To achieve this aim, these Trigno devices were placed 7.5cm lateral to the player tracking device on the material of the harness and on the skin surface lateral to the harness. 3D kinematic and GRF data were captured respectively by a 10 Oqus 300+ camera system (300Hz, Qualisys AB, Göteborg, Sweden) and two mul-tichannel Kistler force platforms (1200Hz, Type 9281CA and 9821EA, Kistler, Winterthur, Switzerland) embedded in the floor, and connected to two control units (Type 5233A, Kistler, Winterthur, Switzerland). As the sampling rates for
accelerometers of the GPSports device (100Hz) and Trigno (148Hz) are intrinsic and cannot be altered, 3D kinematic (300Hz) and GRF (1200Hz) data were set at multiples of the sampling rate of these accelerometer devices in accordance with Nyquist sampling theorem.
Kinematic data from the 3D motion analysis system, multi-component force platforms and Trigno devices were time synchronised and collected using Qualisys Track Manager (v2). The manufacturer of player tracking device when con-tacted could not provide a method to time synchronise this player tracking accelerometer data with the other devices. Therefore, to ensure that this data was then time synchronised with the player tracking data, by a research assistant manually applying a sharp impact simultaneously to the player tracking device and the Trigno device (that could be time synchronised with Qualisys Track Manager) attached to the elasticised har-ness at the beginning of every trial. The peak vertical accel-eration of the Trigno and player tracking accelerometer was used as the timestamp of the start of the trial in each respec-tive data file. Retro-reflecrespec-tive markers were placed on the participants’ trunk, pelvis, extremities and head (Schaefer, O’Dwyer, Ferdinands, & Edwards, 2018), and a three-marker cluster on the posterior aspect of the device itself (Figure 1).
Experimental protocol
After anthropometrics were measured, participants’ performed a standardised warm-up consisting of self-determined static and dynamic stretching, and four sets of four 20m shuttles, with 20s rest between each shuttle. After familiarisation, parti-cipants’ performed five left and five right running trials across the force platform at each of the three different speeds (slow, medium, fast) in a randomised order with 1-min between trials. A trial was deemed successful when the participants’ whole foot contacted the force platform(s) and they attained a speed within ± 0.5 m.s−1of the required speed (slow 3.3m.s−1, medium 5.0m.s−1, fast 6.7m.s−1). Trials that failed to meet these criteria were repeated. Speeds were measured by infra-red timing gates (SmartSpeed 4-Gate System, Fusion Sport, Coopers Plains, Australia) placed 1m apart either side of the centre of the force platform. To attain the running speeds of slow, medium and fast, the participants performed approxi-mately 10 run-up strides to the force platform, starting at 15m, 20m or 25m from the platform, respectively. The weight-acceptance and propulsion phases of running were defined in accordance with Edwards, Austin, and Bird (2017).
Data reduction and analysis
Data analysis was performed using Visual 3D software (v5, C-Motion, Germantown, MD, USA). A fourth-order zero-phase Butterworth low-pass digital filter (fc= 20 Hz) was used to filter
the raw GRF, 3D kinematic from the 3D motion analysis system and Trigno acceleration data. It should be noted that the man-ufacturer was contacted and denied our request to provide the GPSports device acceleration filtering methods. Therefore, this data was not filtered after it was exported from the devices software in G, and note that a limitation of this current study is that we do not know if comparable filtering methods were employed between the player tracking accelerometer data and the other acceleration data. Trigno accelerometer data was
normalised so that each axis equalled zero when static, the filtered Trigno data was converted from millivolts to G (1G = 0.624mV) and then the static baseline value at the begin-ning of each trial for each axis was subtracted from the signal for each respective axis. Procedures definitions of the 3D segmental mass and inertial properties are outlined in Schaefer et al. (2018). The three-markers located on the player tracking device also formed a 3D segment. The Cartesian local coordinate system sign conventions for the 3D kinematic data from the 3D motion analysis system were defined as mediolateral x-axis, anterior-posterior y-axis and superior-inferior direction z-axis. Orientation of the player tracking and Trigno devices was stan-dardised when the participant was standing upright and defined according to the manufacturer as medio-lateral direction for x-axis GPSports and y-axis Trigno, anterior-posterior for z-axis GPSports and Trigno, and superior-inferior direction for y-axis GPSports and x-axis Trigno. The superior-inferior axis was denoted as the “vertical acceleration” axis and was analysed within this current study (y-axis GPSports, x-axis Trigno, x-axis 3D kinematic). It is acknowledged that this superior-inferior axis of the GPSports device and Trigno are not a true vertical orien-tation relative to the ground. This limiorien-tation is due to the forward inclination angle of these devices during alterations in gait. A representative example of the player tracking device forward inclination angle between initial foot-ground contact (IC) and take-off (TO) is shown inFigure 2and displays a mean
angle 19.0 ± 2.6° with a range of 7.8–26.9°. Incorporating this changing inclination angle during gait to obtain the true vertical acceleration is not possible as this angle can only be estimated when standing still with a triaxial accelerometer or with a gyro-scope during gait that neither devices housed (Watanabe, Saito, Koike, & Nitta,2011).
Three outcome variables analysed in this current study were the peak vertical acceleration, time from IC to peak vertical acceleration and vertical GRF (vGRF). In each running phase (weight-acceptance, propulsion), the vertical accelera-tion variables were measured for the following segments using 3D kinematic data from the 3D motion analysis system: the whole body centre-of-mass, based upon the full-body marker set (3D-COG); thorax segment (3D-Thoracic), and player tracking segment (3D-GPS). Vertical acceleration vari-ables were also measured from the triaxial accelerometers within the player tracking device and from the Trigno triaxial accelerometers attached to the player tracking device harness (Accel-Harness) and to the adjacent skin (Accel-Skin). vGRF measured by the force platforms was reported in G.
Statistical analyses
Means and standard deviations of the ten trials (five trials for each lower limb) were calculated for all variables across three running speeds (slow, medium, fast). Repeated measures fac-torial analyses of variance (ANOVAs) were calculated in
Statistica (v13, Statsoft Inc, Tulsa, OK, USA) to determine any significant changes (p < 0.05) in the means of all outcome variables over the running conditions. When main effects were found, Tukey post hoc tests were conducted to identify the precise locus of the effect. Factorial analyses were implemen-ted in this current study due to their inherent control of experiment-wise error, thereby ensuring that tests on indivi-dual variables were carried out only where a significant effect was identified. This was essential in a comprehensive experi-mental protocol such as the present study where multiple dependent variables were being assessed. Three factors for the analyses were: type of acceleration measurement, phase (weight-acceptance, propulsion), and speed (slow, medium, fast). Three separate factorial analyses were carried out. 1) Locations of measurement: type of acceleration measurement (GPSports, 3D-GPS, 3D-Thoracic). 2) Sources of error measure-ment: type of acceleration measurement (GPSports, Accel-Skin, Accel-Harness, 3D-GPS). 3) Variables: type of acceleration mea-surement (GPSports, 3D-COG, vGRF). A representation of the type of acceleration signals is shown inFigure 3.
Bivariate Pearson’s product-moment correlation (p < 0.05) was calculated between the player tracking accelerometer and the relevant gold-standard variables, and classified according to Hopkins (2000). Reliability was assessed using a specialised Microsoft Excel spreadsheet (Laursen, Francis, Abbiss, Newton, & Nosaka, 2007). Using this spreadsheet the typical error of measurement (TE, G), coefficient of variation expressed as a percent of the mean outcome variable (CV%), change in the mean (Δ mean, %, G), and test-retest (intraclass) correlation (Hopkins, 2000) classified according to Fleiss (1999) were calculated.
Results
Location of measurement: Maximum acceleration dis-played a main effect for acceleration type (F2,18 = 77.1,
p < 0.001) and interactions for type*phase (F2,18 = 35.4,
p < 0.001) and type*speed (F4,36= 8.9, p < 0.001; Figure 4
(1)(a)). Post hoc analysis for the type*phase interaction showed throughout both phases that acceleration from the GPSports was higher than the GPS and the 3D-Thoracic (p < 0.001). 3D-3D-Thoracic acceleration was lower than that of the 3D-GPS during the weight-acceptance only (p < 0.001). Type*speed interaction showed that the 3D-Thoracic acceleration was lower for slow-speed com-pared to both medium- and fast-speed (p < 0.001) but no difference was observed between medium- and fast-speed. While the 3D-GPS acceleration at a slow-speed was similar to the medium- or fast-speed, the 3D-GPS accelera-tion at medium-speed decreased when compared to the fast-speed (p = 0.037). Slow-speed GPSports acceleration was not different from either the medium- or fast-speed (p > 0.05), but the GPSports acceleration at a medium-speed was smaller than fast-medium-speed (p = 0.017). GPSports acceleration was higher at all speeds when compared to GPS and Thoracic acceleration (p < 0.001), and GPS acceleration was observed to be greater than 3D-Thoracic acceleration at the fast-speed (p < 0.001). For the speed*phase interactions during weight-acceptance, only the slow-speed acceleration was decreased when compared to both medium- and fast-speed (p < 0.05), yet during propulsion there were no differences in acceleration between speeds. Time (100%) GPS Inclination Angle (degrees) 75.0 50.0 25.0 0.0 100.0 5.0 30.0 10.0 15.0 20.0 20.0
Figure 2.A representative example from a single participant of the player tracking device forward inclination angle between initial foot-ground contact and take-off during running. Grey lines indicates data from individual trials, whereas the bold line indicates the mean of all of these individual trials.
3D-Thoracic acceleration when all speeds were pooled was correlated with GPSports during weight-acceptance (p < 0.001, r = 0.61) but not propulsion (p = 0.22, r = 0.20).
For the time from IC to maximum acceleration (duration), a main effect was observed for speed (F2,16= 33.74, p < 0.001), with
interactions between phase*speed (F2,16 = 24.16, p < 0.001),
type*speed (F4,32 = 7.05, p < 0.001) and phase*type*speed
(F4,32= 4.99, p < 0.001;Figure 4(1)(b)). Phase*type*speed
inter-action post hoc analysis revealed throughout stance, 3D-Thoracic duration when compared to GPSports had longer duration at slow-speed (p < 0.001), but was not different between types at medium- or fast-speed. 3D-Thoracic duration was longer at slow-speed, but shorter at fast-speed than 3D-GPS duration during propulsion (p < 0.001), however there was no difference between medium- and fast-speed during weight-acceptance. 3D-GPS duration was only longer than GPSports duration at slow-speed in weight-acceptance (p = 0.027). With increasing speed, 3D-Thoracic duration decreased during stance (p < 0.001), except between medium- and fast-speed in weight-acceptance (p < 0.001). With increasing speed, 3D-Thoracic duration did not change between medium- and fast-speed in weight-accep-tance (p < 0.001) and slow- and medium-speed in propulsion (p < 0.001). Both phases showed 3D-GPS decreased in duration with increased speed (p < 0.001), yet not between medium- and speed. GPSports duration was longer between slow and speed in weight-acceptance, and a shorter duration for fast-speed compared with slow- and medium-fast-speed (p < 0.001).
Source of error measurement (Harness/skin movement): A main effect of type was observed (F3, 27= 14.21, p < 0.001),
with interaction between type*phase (F3,27= 6.43, p = 0.002),
and phase*speed (F2,18= 14.62, p < 0.001; Figure 4(2)(a)) for
peak acceleration. Post hoc analysis of the type*phase interac-tion determined that GPSports accelerainterac-tion was higher than all other types of acceleration during both phases (p < 0.001), except between 3D-GPS acceleration during weight-accep-tance (p = 0.14). In both phases, Accel-Skin acceleration dis-played a lower magnitude compared to Accel-Harness and 3D-GPS acceleration during both phases (p < 0.001), yet there was no difference between the 3D-GPS and the Accel-Harness acceleration. For the phase*speed interaction, the peak accel-eration during the slow-speed was lower than the medium-and fast-speed during the weight-acceptance (p < 0.05), how-ever no difference in medium- and fast-speed acceleration was exhibited. During the propulsion, only slow- and fast-speed accelerations were different (p < 0.05).
The time from IC to peak acceleration (duration) showed a main effect for speed (F2,16= 21.90, p < 0.001), with
interac-tions between type*phase (F3,24 = 5.73, p = 0.004),
phase*-speed (F2,16 = 21.69, p < 0.001), type*speed (F6,48 = 4.05,
p = 0.002), and phase*type*speed (F6,48 = 3.85, p = 0.003;
Figure 4(2)(b)). Post hoc analysis of the phase*type*speed
interaction determined that Accel-Harness duration was not different to either the GPSports or 3D-GPS duration during stance. GPSports duration showed longer duration for slow-and fast-speed during weight-acceptance, slow-and medium- slow-and fast-speed during propulsion. GPSports also showed longer duration compared to the 3D-GPS at slow- and fast-speed during weight-acceptance, but only at fast-speed during
Figure 3.A representation of the vertical acceleration signals during running. Dotted lines indicates data from individual trials, whereas the bold line indicates the mean of all of these individual trials.
propulsion. Accel-Skin duration during propulsion was differ-ent across all speeds (p < 0.001). During weight-acceptance, the 3D-GPS, Accel-Harness and Accel-Skin durations for med-ium- and fast-speed were not different, GPSports displayed significant differences with a shorter duration for fast- com-pared to slow-speed (p < 0.05). During propulsion, the 3D-GPS
and the Accel-Harness durations were similar between med-ium- and fast-speed, and GPSports duration was similar between slow- and medium-speed.
Variables: There was a main effect for type (F2,18 = 60.0,
p < 0.001) and speed (F2,18= 22.2, p < 0.01) with an interaction
between type*phase (F2,18 = 232.6, p < 0.001), phase*speed
(1a)
(1b)
(2a)
(2b)
(3a)
(3b)
Figure 4.Means and standard errors of the (a) peak vertical acceleration and (b) time from initial foot-ground contact to peak vertical acceleration during weight-acceptance and propulsion phase in running. (1) Location of measurement. (2) Source of error measurement. (3) Variables.
(F2,18= 70.1, p < 0.001), type*speed (F4,36= 13.8, p < 0.001) and
phase*type*speed (F4,36= 3.7, p = 0.01;Figure 4(3)(a)). Post hoc
analysis determined that the GPSports acceleration was higher than 3D-COG acceleration and vGRF, and that all variables were higher in the weight-acceptance vs propulsion (p < 0.001). Type*speed interaction observed the GPSports acceleration was higher across all speeds (p > 0.05) compared to vGRF and 3D-COG. Phase*type*speed interaction revealed that the GPSports acceleration was higher than vGRF and 3D-COG accel-eration during weight-acceptance at all speeds, however during propulsion GPSports was only higher than 3D-COG acceleration at slow-speed and lower than vGRF at medium- and fast-speed (p < 0.001).
3D-COG acceleration was correlated with GPSports acceleration during weight-acceptance (p < 0.01,r = 0.38) but not propulsion (p = 0.053, r = 0.31), and with 3D-Thoracic acceleration during weight-acceptance (p < 0.001,r = 0.63) and propulsion (p = 0.015, r = 0.35) when speeds were pooled. When speeds were pooled, vGRF was correlated with GPSports (p < 0.01, r = 0.44) and 3D-COG (p < 0.001, r = 0.63) acceleration during weight-acceptance, but not in propulsion (GPSports: p = 0.19, r = -0.21;3D-COG p = 0.29, r = 0.17).
Main effects for time to peak acceleration were observed for type (F2,16 = 61.1, p = 0.037) and speed
(F2,16 = 57.2, p < 0.001), with interactions of phase*type
(F2,16= 3.8, p = 0.04), phase*speed (F2,16= 39.8, p < 0.001),
type*speed (F1,8 = 3.5, p = 0.02), and phase*type*speed
(F4,32= 11.9, p < 0.001; Figure 4(3)(b)). Post hoc analysis of
the main effects determined a longer GPSports duration compared to vGRF duration but no difference with 3D-COG duration, and the duration decreased with speed (p < 0.001). It was also revealed that the duration was shorter in the weight-acceptance than propulsion for GPSports, 3D-COG and VGRF, and duration decreased as speed increased in both phases, except medium- to fast-speed during the weight-acceptance (p < 0.001). Phase*type*speed interaction showed a longer GPSports duration than vGRF in both phases (p < 0.001) and 3D-COG at medium- (p < 0.001) and fast-speed (p = 0.048) during weight-acceptance and medium-speed during pro-pulsion (p = 0.025).
Reliability
Throughout stance, the GPSports triaxial accelerometer mostly exhibited poor reliability, high CV% and change in mean to estimate vertical acceleration patterns of the COG; 3D-thoracic segment and vGRF (Table 1). In contrast the GPSports accelerometer during both phases when estimating 3D-GPS acceleration displayed excellent ICC and low CV% but high change in mean at the slow- and medium-speed. However, at the fast speed during both phases the GPSports accelerometer only displayed fair to good ICC and high CV% with a small change in mean when estimating 3D-GPS accel-eration. GPSports triaxial accelerometer showed excellent ICC to estimate thoracic acceleration, yet displayed a higher CV% and change in mean during propulsion.
Discussion
Despite knowing that trunk-mounted accelerometers can overestimate whole-body acceleration, they continue to be used to calculate variables to quantify sports performance such as vGRF and energy expenditure. This current study’s major finding was that the accelerometers embedded within the GPSports device exhibit poor reliability and were not a valid representation of thoracic segment vertical acceleration for where they are located, nor able to accurately estimate 3D-COG acceleration or peak vGRF. This current study observed that the critical issue contributing to the GPSports device’s poor reliability and validity to measure vertical acceleration variables was the effect of the movement of the elasticised harness relative to the skin.
This current study was the first to confirm the prior sugges-tion that the elasticised harness of the player tracking device is one of the major contributors to extraneous accelerometer magnitudes. The absence of difference between the 3D-GPS segment and Accel-Harness signal for the magnitude and time of the peak vertical acceleration, suggests that the fabric pocket encapsulating the player tracking device within the harness was not a significant contributor to extraneous accel-erations. Nevertheless, the higher peak vertical acceleration along with a greater magnitude of difference between Accel-Harness compared to Accel-Skin indicates that the elasticised
Table 1.Reliability of GPS triaxial accelerometer to measure peak vertical acceleration compared to the gold-standard 3D motion capture and force platforms.
Slow Medium Fast All
Δ Mean CV% TE Δ Mean CV% TE Δ Mean CV% TE Δ Mean CV% TE
Variable G % % G ICC G % % G ICC G % % G ICC G % % G ICC
Weight-acceptance COG-GPSports 2.56 160.1 22.13 0.35 0.16 2.04 84.2 19.25 0.47 0.39 1.30 41.0 19.77 0.63 0.46 1.94 103.7 32.66 0.61 −0.26 3DGPS-GPSports 0.60 17.3 6.19 0.21 0.89 0.43 10.9 5.12 0.20 0.94 0.24 5.6 16.20 0.69 0.72 0.41 11.9 12.68 0.44 0.22 Thoracic-GPSports 1.99 91.8 18.96 0.41 0.00 1.56 53.3 14.12 0.41 0.56 1.57 53.7 16.68 0.49 0.68 1.69 71.7 19.11 0.45 0.02 GRF-GPSports 2.41 138.5 17.84 0.31 0.47 2.17 94.2 18.58 0.43 0.50 1.64 57.5 21.83 0.62 0.31 2.05 107.2 27.10 0.52 −0.11 Propulsion COG-GPSports 0.55 30.3 6.75 0.12 0.71 0.22 10.9 9.38 0.19 0.31 −0.06 −2.9 12.29 0.24 0.29 0.22 11.8 13.65 0.26 −0.07 3DGPS-GPSports 0.61 34.2 6.06 0.11 0.86 0.55 31.9 6.10 0.10 0.85 0.57 43.3 13.12 0.17 0.78 0.57 33.3 7.62 0.13 0.61 Thoracic-GPSports 1.02 73.2 13.78 0.19 0.28 0.79 53.5 21.16 0.31 −0.32 0.54 40.3 20.30 0.27 0.38 0.77 53.8 20.51 0.30 −0.33 GRF-GPSports −0.09 −3.7 10.61 0.26 −0.36 −0.53 −19.2 10.92 0.30 −0.15 −0.91 −32.6 10.74 0.30 −0.02 −0.53 −20.5 14.20 0.37 −0.32 CV% = coefficient of variation expressed as a percentage; TE = typical error of measurement; ICC = intra-class correlation;G = gravitational constant (9.81m.s−2)
harness is a key contributor to the acceleration overestimation measured by trunk-mounted accelerometry. The elasticised harness effect on the time from IC to peak vertical acceleration displayed inconsistent results across speeds. It is recom-mended that an alternative method of attachment for the player tracking device is devised in order to reduce the extra-neous accelerations caused by the “whipping” movement of the elastic harness.
Limited research exists investigating the validity of trunk-mounted accelerometers housed within player tracking devices to estimate thoracic vertical acceleration during running. Wundersitz et al. (2013) investigated this research question only in the weight-acceptance phase during treadmill running at one speed with only one retro-reflective marker located on the skin at the T6 vertebra. A single marker measurement is a less accurate representation of the entire thoracic segment acceleration and its 3D orientation within space, hence why the current study created a 3D-GPS segment for a more accurate representation of the unit’s acceleration. This representation of trunk acceleration is in con-trast to methods and findings of Nedergaard et al. (2017) who reported that player tracking resultant acceleration measured lower accelerations than an accelerometer placed on the player tracking device. Nevertheless, in accordance with these authors, we observed that the player tracking accelerometer overesti-mated peak vertical accelerations at all speeds, and the timing of this peak during slow speed running (though not at medium- or fast-speeds) compared to the gold-standard. Higher peak vertical acceleration of the 3D-GPS segment than the 3D-Thoracic seg-ment during weight-acceptance was not replicated in the propul-sion phase. It is likely this overestimation is due to the IC causing a substantial movement artefact of the 3D-GPS segment during weight-acceptance that is absent in the propulsion phase. This suggests increased validity to estimate peak acceleration variables in the propulsion than weight-acceptance phase, when there is no movement artefact and a smaller peak magnitude. This sugges-tion is variable-specific, as similar durasugges-tions of time from IC to peak acceleration were observed throughout weight-acceptance, but not in propulsion phase. This is likely due to the lower sampling rate of the GPSports accelerometer vs 3D motion analysis system that caused aliasing errors due to violation of the sampling theo-rem. The sampling theorem states that the signal must be sampled at a rate at least twice as high as its highest frequency (Winter,2009). With the highest frequency recorded between 60 to 90 Hz during treadmill running (Shorten & Winslow,1992), this suggests that increasing the sampling rate of accelerometers may improve its validity.
Despite the assumption that estimation of energy expendi-ture (Rosenberger et al., 2013) via accelerometry should be measured from an accelerometer attached on the pelvic region, close to the centre of gravity (Le Huec et al., 2011), trunk-mounted accelerometers are being utilised for this pur-pose (Walker et al.,2016). The current study observed that the vertical acceleration of the 3D-COG increased with speed in a linear manner, and acceleration variables derived from the thoracic region (3D-Thoracic and GPSports) did not follow this pattern. This is most likely due to the changing effect of limbs with gait speed on the vertical COG accelerations (Lee & Farley, 1998), leading to its overestimation. Recent research has observed peak resultant centre-of-mass acceleration
estimated via force platforms was most strongly predicted by player tracking and trunk accelerometry (Nedergaard et al.,
2017), however, the current study did not support this obser-vation. When the acceleration of GPSports was compared to 3D-COG, it was higher and was not reliable across all speeds. A lack of significant difference and a large correlation between peak 3D-Thoracic and 3D-COG suggests that if the attachment method of the player tracking device is improved to minimise the effect of harness movement, then player tracking accel-erometry has the potential to estimate energy expenditure.
For the accelerometer to estimate peak vGRF, it is recom-mended that it be attached to the shank (Hennig & Lafortune,
1991). This current study observed the trunk-mounted GPSports accelerometer was unable to accurately estimate peak vGRF for any speed during the weight-acceptance phase, nor during fast speed in the propulsion phase, and only explained 20% of the variance of peak vGRF. As it is known that the GRF vector is the summation for the mass-acceleration products for all body segments (Winter,2009), it is not surprising to find that the 3D-COG was a valid repre-sentation to estimate vGRF at all running speeds.
It is known that GRF (Nilsson & Thorstensson, 1989) and whole body vertical COG peak displacement (Lee & Farley,
1998) increase with an increase in gait speed. Unexpectedly, this current study observed that peak vertical acceleration of GPSports did not increase between medium- and fast-speed during weight-acceptance, suggesting an “acceleration pla-teau”. This plateau is most likely due to the thoracic vertical acceleration estimated from the GPSports and 3D-Thoracic not accounting for extremity movements, whereas the 3D-COG does. This suggest that player tracking accelerometry variables are not sensitive enough to detect changes at higher running speeds. It was also of concern to identify that the level of reliability was inconsistent across the three running speeds (ICC range 0.22–0.94 for GPSports to estimate the 3D-GPS peak acceleration). It is recommended that if new regression algorithms are developed to estimate vertical acceleration of the COG or vGRF, that they take into account the changes in speed, as fluctuating speed, particularly as maximal speed is approached, appears to be a significant factor in the validity and reliability of player tracking accelerometers.
Several limitations of this current study are acknowledged. The current study on trunk-mounted accelerometers only assessed the instantaneous variable of the differences in the peak magnitude of the vertical acceleration. It should be noted that trunk-mounted accelerometers have also been used to estimate other instantaneous variables such as vGRF as well as cumulative variables such as energy expenditure (Walker et al.,2016). Future research should assess the mean differences between the various acceleration signals in all three planes of acceleration over a cumulative period of time to conclude on the reliability and validity of trunk-mounted accelerometers to estimate player load and energy expendi-ture during a training session and/or game.
Conclusions
Based on this current study’s findings, caution is advised when extrapolating trunk-mounted accelerometer data to estimate
variables based on vertical acceleration in order to quantify and monitor workloads and/or injury risk during training and/or com-petition. Inconsistency in reliability across speeds and the accel-eration plateau suggest that a new regression algorithm that takes into account fluctuating speeds, particularly as maximal speed is approached, be developed. The elasticised harness was a substan-tial factor in the overestimation of peak vertical acceleration dur-ing runndur-ing, therefore a new attachment method involvdur-ing the removal/reduction of elastic material is urgently warranted to improve its reliability and validity.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Suzi Edwards http://orcid.org/0000-0002-5790-0232
Robert Robergs http://orcid.org/0000-0002-7741-8136
Nicholas O’Dwyer http://orcid.org/0000-0003-2715-7991
References
Alexander, J. P., Hopkinson, T., Wundersitz, D., Serpell, B. G., Mara, J., & Ball, N. B. (2016). Validity of a wearable accelerometer device to measure average acceleration values during high speed running. Journal of Strength & Conditioning Research, 30, 3007–3013.
Boyd, L. J., Ball, K., & Aughey, R. J. (2011). The reliability of MinimaxX accelerometers for measuring physical activity in Australian football. International Journal of Sports Physiology & Performance, 6, 311–321. Buchheit, M., Gray, A., & Morin, J.-B. (2015). Assessing stride variables and
vertical stiffness with GPS-embedded accelerometers: Preliminary insights for the monitoring of neuromuscular fatigue on the field. Journal of Sports Science & Medicine, 14, 698–701.
Colby, M. J., Dawson, B., Heasman, J., Rogalski, B., & Gabbett, T. J. (2014). Accelerometer and GPS-derived running loads and injury risk in elite Australian Footballers. Journal of Strength & Conditioning Research, 28, 2244–2252.
Edwards, S., Austin, A., & Bird, S. P. (2017). The role of the trunk control in athletic performance of a reactive change-of-direction task. Journal of Strength & Conditioning Research, 31, 126–139.
Fleiss, J. L. (1999). The design and analysis of clinical experiments. New York: Wiley.
Hennig, E. M., & Lafortune, M. A. (1991). Relationships between ground reaction force and tibial bone acceleration parameters. International Journal of Sport Biomechanics, 7, 303–309.
Hopkins, W. G. (2000). Measures of reliability in sports medicine and science. Sports Medicine, 30, 1–15.
Hopkins, W. G., Marshall, S. W., Batterham, A. M., & Hanin, J. (2009). Progressive statistics for studies in sports medicine and exercise science. Medicine & Science in Sports & Exercise, 41, 3–13.
Lafortune, M. A., Lake, M. J., & Hennig, E. M. (1996). Differential shock transmission response of the human body to impact severity and lower limb posture. Journal of Biomechanics, 29, 1531–1537.
Laursen, P. B., Francis, G. T., Abbiss, C. R., Newton, M. J., & Nosaka, K. (2007). Reliability of time-to-exhaustion versus time-trial running tests in run-ners. Medicine & Science in Sports & Exercise, 39, 1374–1379.
Le Huec, J. C., Saddiki, R., Franke, J., Rigal, J., & Aunoble, S. (2011). Equilibrium of the human body and the gravity line: The basics. European Spine Journal, 20(Suppl 5), 558–563.
Lee, C. R., & Farley, C. T. (1998). Determinants of the center of mass trajectory in human walking and running. Journal of Experimental Biology, 201, 2935–2944.
McLean, S. G., Walker, K., Ford, K. R., Myer, G. D., Hewett, T. E., & van Den Bogert, A. J. (2005). Evaluation of a two dimensional analysis method as a screening and evaluation tool for anterior cruciate ligament injury. British Journal of Sports Medicine, 39, 355–362.
Nedergaard, N. J., Robinson, M. A., Eusterwiemann, E., Drust, B., Lisboa, P. J., & Vanrenterghem, J. (2017). The relationship between whole-body external loading and body-worn accelerometry during team-sport movements. International Journal of Sports Physiology & Performance, 12, 18–26.
Nigg, B. M., & Wakeling, J. M. (2001). Impact forces and muscle tuning: A new paradigm. Exercise & Sport Sciences Reviews, 29, 37–41.
Nilsson, J., & Thorstensson, A. (1989). Ground reaction forces at different speeds of human walking and running. Acta Physiologica Scandinavica, 136, 217–227.
Rosenberger, M., Haskell, W., Albinali, F., Mota, S., Nawyn, J., & Intille, S. (2013). Estimates of energy expenditure from an accelerometer worn on the wrist versus the hip in adults. Medicine & Science in Sports & Exercise, 45, 964–975.
Schaefer, A., O’Dwyer, N., Ferdinands, R. E. D., & Edwards, S. (2018). Consistency of kinematic and kinetic patterns during a prolonged spell of cricket fast bowling: An exploratory laboratory study. Journal of Sports Sciences, 36, 679–690.
Shorten, M. R., & Winslow, D. S. (1992). Spectral analysis of impact shock during running. International Journal of Sport Biomechanics, 8, 288–304. Simons, C., & Bradshaw, E. J. (2016). Reliability of accelerometry to assess impact
loads of jumping and landing tasks. Sports Biomechanics, 15, 1–10. Walker, E. J., McAinch, A. J., Sweeting, A., & Aughey, R. J. (2016). Inertial
sensors to estimate the energy expenditure of team-sport athletes. Journal of Science & Medicine in Sport, 19, 177–181.
Watanabe, T., Saito, H., Koike, E., & Nitta, K. (2011). A preliminary test of measurement of joint angles and stride length with wireless inertial sensors for wearable gait evaluation system. Computational Intelligence & Neuroscience, (2011, 975193.
Winter, D. A. (2009). Biomechanics and motor control of human movement (p. 27). New York, NY: Wiley.
Wundersitz, D. W., Gastin, P. B., Robertson, S., Davey, P. C., & Netto, K. J. (2015). Validation of a trunk-mounted accelerometer to measure peak impacts during team sport movements. International Journal of Sports Medicine, 36, 742–746.
Wundersitz, D. W. T., Gastin, P. B., Richter, C., & Netto, K. J. (2013). Validity of wearable technology to measure peak impact during high-intensity treadmill running. In T.-Y. Shiang, W.-H. Ho, P. C. Huang, & C.-L. Tsai (Eds.), 31st International Conference on Biomechanics in Sports, Taipei, Taiwan.
Wundersitz, D. W. T., Gastin, P. B., Richter, C., Robertson, S. J., & Netto, K. J. (2015). Validity of a trunk-mounted accelerometer to assess peak accel-erations during walking, jogging and running. European Journal of Sport Science, 15, 382–390.
Wundersitz, D. W. T., Netto, K. J., Aisbett, B., & Gastin, P. B. (2013). Validity of an upper-body-mounted accelerometer to measure peak vertical and resultant force during running and change-of-direction tasks. Sports Biomechanics, 12, 403–412.
Yang, C. C., & Hsu, Y. L. (2010). A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors, 10, 7772–7788.