bowling in cricket. A pilot study.
4.1.1 Introduction
Previously in this thesis an in-depth analysis of current literature pertaining to fast bowling lower limb kinetics, as well as methodologies used for their analyses was presented. Current analysis of fast bowling kinetics has been limited to force plate analysis (Elliott and Foster, 1984; Foster and Elliott, 1985; Foster et al. 1989; Mason et al. 1989; Elliott et al. 1992; Hurrion et al. 2000; Portus et al. 2004; Crewe et al. 2013; Worthington et al. 2013). Whilst this enables accurate analysis of the ground reaction force (GRF), it is limited to the laboratory environment and is inherently expensive, therefore a more portable and cost effective method of kinetic analysis is needed if routine take-up at club level is desired (Hurrion et al. 2000; Portus et al. 2004; Stuelcken and Sinclair, 2009; Crewe et al. 2013; Worthington et al. 2013; Spratford and Hicks, 2014; Bayne et al. 2016; King et al. 2016; Middleton et al. 2016). This would enable coaches to provide real-time feedback in representative bowling environments. Accelerometers at the tibia and sacrum have previously been validated as a representative measure of kinetic variables, such as peak and time-to-peak acceleration during high impact movements including running, jumping and falling (Crowell et al. 2010; Theobald et al. 2010; Tran et al. 2010; Sell et al. 2014; Henriksen et al. 2004). Tran and colleagues (2010) report that accelerometer data resulted in acceptable measurement error and showed moderate correlations when compared with GRF data in jumping and landing tasks, suggesting accelerometers may be a valid method for measuring impacts in the field (Tran et al. 2010). Furthermore, running literature has highlighted a very strong relationship between tibial accelerations recorded by tibial mounted accelerometers and GRF obtained using force plates (r2 = 0.95) (Hennig et al.
1993). Furthermore, the use of both tibial and sacral accelerometry has been utilised to describe impact attenuation during running (Mizrahi et al. 2000). Whilst accelerometers are becoming a more commonly utilised method, they have not yet been used for the analysis of lower limb impact during fast bowling. Prior to the uptake of any new technology, a reliability and validity analysis is warranted. Variables such as peak and time-to-peak acceleration at the tibia and sacrum may provide important information in
relation to impact during fast bowling, as well as knowledge regarding impact attenuation, providing coaches and practitioners with insights into the accelerations experienced during fast bowling.
Three-dimensional spinal kinematics during fast bowling have been reported in a number of studies with reference to performance and injury (Burnett et al. 1998; Ranson et al. 2008; Ferdinands et al. 2009; Ranson et al. 2009; Stuelcken et al. 2010; Crewe et al. 2013; Bayne et al. 2016). Whilst studies have drawn individual conclusions based on their findings, limitations and heterogeneity of current methodologies have made collective synthesis difficult. Studies reporting three-dimensional spinal kinematics in fast bowling typically use multi-camera optoelectronic motion analysis systems (Burnett et al. 1998; Ranson et al. 2008; Ferdinands et al. 2009; Ranson et al. 2009; Stuelcken et al. 2010; Crewe et al. 2013; Bayne et al. 2016). These systems allow the collection of a wealth of data, to a high degree of accuracy (Windolf et al. 2008). However, the fact that these systems are expensive and typically limited to a laboratory environment prevent the routine live analysis of bowling kinematics. Thus, alternative technologies that overcome previous limitations are desirable.
Burnett and colleagues (1998) reported on the use of an electromagnetic tracking device for the analysis of three-dimensional spinal kinematics during fast bowling. Whilst this overcomes some of the limitations of optoelectronic systems such as line of sight and cost, the operating volume for electromagnetic systems are small unless the electromagnetic source is attached to the individual (as in this study). Using the source as a sensor yields a comparatively large and heavy sensor resulting in significant inertial properties during such a ballistic task like fast bowling and may limit or alter the bowler’s natural movement.
Inertial sensor technology, consisting of gyroscopes, accelerometers and magnetometers, have been validated for the use in clinical analysis of three-dimensional spinal kinematics and more dynamic sporting movements (Charry et al. 2011; van den Noort et al. 2009; Hu et al. 2014; Williams et al. 2013; Williams et al. 2014; Swaminathan et al. 2016). As inertial sensors are not dependant on cameras or line of sight, they offer the potential for ‘in-field’ data collection, whilst being a smaller and lighter option to electromagnetic systems. Previous studies reporting spinal kinematics from inertial sensors show very strong correlation with values reported from
electromagnetic systems in clinical settings (as high as R2 = .999) (Ha et al. 2013). Furthermore, a review analysing the validity of using inertial sensors for human movement highlighted good validity and reliability but also acknowledged that this is task specific (Cuesta-Vargas et al. 2010). The validity and reliability of inertial sensors for the analysis of three-dimensional spinal kinematics and impacts during fast bowling has not been previously investigated. Therefore, before this technology can be recommended for analysis of fast bowling spinal kinematics a reliability and validity analysis is warranted.
4.1.2 Aim of the Study
This study aimed to assess the reliability and validity of using accelerometry and inertial sensors to measure fast bowling impacts and three-dimensional spinal kinematics during fast bowling in cricket.
4.1.3 Results Validity
Pearson’s correlations highlighted significant correlations (p<0.003) in 79% of all compared acceleration and GRF variables at both BFI and FFI (See tables 4.1.1, 4.1.2 and 4.1.3). Strong to very strong correlations (r>0.7 and r>0.9 respectively) were observed in all variables except time to peak resultant acceleration and GRF which highlighted a significant moderate correlation (r=.640).
Table 4.1.1. Comparison and correlation of mean tibial acceleration and ground reaction force at back-foot impact of n=30 deliveries.
*Denotes p<0.003
GRF Variable at BFI Mean (±SD) Accelerometer Variable at
BFI
Mean (±SD) r
Vertical peak GRF (N) 1738.4 (391.2) Along-tibial peak acceleration (g) 14.1 (6.6) .974* Anterior-posterior peak GRF (N) 845.8 (138.1) Anterior-posterior peak acceleration (g) 11.7 (6.4) .977* Mediolateral peak GRF (N) 254.2 (150.8) Mediolateral peak acceleration (g) 3.5 (3.2) .966* Resultant peak GRF (N)
1875.5 (379.8) Resultant peak acceleration (g)
20.4 (9.4) .968* Time to peak vertical
GRF (ms)
30.4 (16.8) Time to peak along-tibial acceleration (ms)
25.8 (8.5) .979* Time to peak resultant
GRF (ms)
34.5 (15.4) Time to peak resultant acceleration (ms)
Table 4.1.2. Comparison and correlation of mean tibial acceleration and ground reaction force at front-foot impact of n=30 deliveries.
*Denotes p<0.003
Pearson’s correlations, highlight strong to very strong correlations across all lumbar kinematics at both BFI and FFI. As metrics for lumbar kinematics were identical between devices a one-way ANOVA was also conducted to investigate if any differences in measurements are present. Lumbar rotation was significantly larger at FFI when using Vicon (p = 0.029). Mean bias highlighted inertial sensor data (IMU) overestimated kinematics between 1.9-4° (Table 4.1.3). The largest difference was seen in lumbar rotation at FFI which displayed mean bias of -5° (negative values denote higher values in the Vicon data compared with IMU). Consequently, root mean square error of prediction (RMSEP) ranged from 0.3-1.5°.
Table 4.1.3. Comparison and correlation of mean spinal kinematics, mean bias and RMSEP between inertial sensors and optoelectronic motion analysis at back and front-foot impact of n=30 deliveries.
Variable Vicon (°±SD) IMU (°±SD) r Mean Bias (°) RMSEP(°) Shoulder counter-rotation 24.9 (7.7) 24.0 (7.7) .948* -0.9 0.3 Lumbar Flexion at BFI 5.7 (5.6) 7.5 (4.7) .986* 1.9 0.5 Lumbar Lateral Flexion at BFI 5.8 (2.1) 9.8 (6.6) .949* 4.0 1.2 Lumbar Rotation at BFI 10.3 (6.4) 12.1 (9.9) .612 1.8 0.5 Lumbar Flexion at FFI 13.6 (8.8) 17.3 (5.0) .958* 3.6 1.1 Lumbar Lateral Flexion at FFI 10.8 (10.9) 13.9 (7.2) .954* 3.2 0.9 Lumbar Rotation at FFI 21.2 (7.5) 16.1 (7.3) .846* -5.1 1.5 *Denotes p<0.003 GRF Variable at FFI
Mean (±SD) Accelerometer Variable at
FFI