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Skill level constrains the coordination of posture and upper-limb movement in a pistol-aiming task
Human Movement Science 55 (2017) 255–263
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Human Movement Science journal homepage: www.elsevier.com/locate/humov
Skill level constrains the coordination of posture and upper-limb movement in a pistol-aiming task
MARK
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Ji Hynu Koa, Dong Wook Hana, , Karl M. Newellb a b
Department of Sport Science, Chonbuk National University, Jeonju, South Korea Department of Kinesiology, University of Georgia, Athens, USA
AR TI CLE I NF O
AB S T R A CT
Keywords: Postural control Arm movement Pistol-aiming PCA
The purpose of the experiment was to investigate whether skill level differentially organizes the coordination of the postural system and upper limb kinematics in a pistol-aiming task. Participants aimed an air-pistol at a target center in 30 s trials as accurately as possible while standing on a force platform with shooting arm joint kinematics recorded. The novice group had greater motion of the pistol end point, arm joints and the center of pressure than the skilled group. Principal components analysis (PCA) showed that the skilled group required 2 components as opposed to the 3 components of the novice group to accommodate the variance. Coherence analysis in the 0–1 Hz bandwidth revealed that the coupling between posture and upper-limb movement was stronger in the skilled than the novice group. The findings are consistent with the view that skill acquisition reduces the kinematic variables into a lower dimensional functional unit that in pistol-aiming is defined over the collective posture and upper-limb system.
1. Introduction A goal-directed aiming task such as pistol-shooting involves multi-joint arm motions and minimal fluctuations of movement amplitude at its end point (e.g., gun barrel) to achieve a high degree of performance accuracy. It has been shown that there are a number of processes contributing to the realization of successful performance in this task including psychological (e.g., intention and stress; Landers, Qi, & Courtet, 1985; Loze, Collins, & Holmes, 2001) and neurophysiological (e.g., cardiovascular adaptation and physiological tremor; Fenici, Ruggieri, Brisinda, & Fenici, 1999; Kelleran, Morrison, & Russell, 2016; Lakie, Villagra, Bowman, & Wilby, 1995; Tang, Zhang, Huang, Young, & Hwang, 2008). Here we focus on how the skill level influences the coordination and control of upper-limb and posture motion in a pistol-aiming task. 1.1. Pistol aiming studies To achieve accuracy in pistol-aiming fundamentally requires the shooter to align the gun barrel with the target. This means that the redundant kinematic arm joint motions (Bernstein, 1967) need to be effectively constrained and coordinated to manage the orientation of the gun with the target. This was initially investigated in the classic studies of Arutyunyan, Gurfinkel, and Mirskii (1968, 1969) that compared pistol shooting in experienced and inexperienced marksmen. They showed that the experienced group had a smaller dispersion of the gun motion compared to the inexperienced group. Furthermore, the reduced gun motion of the
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Corresponding author at: Department of Sport Science, 520, Natural Science Building 15-0, Chonbuk National University, Jeonju 54896, South Korea. E-mail address: [email protected] (D.W. Han).
http://dx.doi.org/10.1016/j.humov.2017.08.017 Received 7 March 2017; Received in revised form 20 July 2017; Accepted 21 August 2017 0167-9457/ © 2017 Elsevier B.V. All rights reserved.
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experienced group was related to the released joint motions of the arm and the presence of compensatory arm-motion synergies. In contrast, the inexperienced group controlled the gun with tightly fixed kinematic linkages of the arm joints and was, in effect, controlling the act of pistol-shooting from the shoulder joint. 1.2. Postural control in aiming tasks Postural control is a critical factor for the control of arm motion in accurate pistol aiming. Successful performance of aiming and pointing tasks requires the integration of posture and limb control and the coordination of multiple degrees of freedom (DOF) (Bernstein, 1967). In shooting tasks, a number of studies have shown that stable postural balance control is significantly related to high-level performance. For example, in rifle (Era, Konttinen, Mehto, Saarela, & Lyytinen, 1996) and sharp (Konttinen, Lyytinen, & Era, 1999) shooting studies experienced shooters showed a reduced amount of motion of the center of pressure (COP) compared to inexperienced shooters. Mononen, Konttinen, Viitasalo, and Era (2007) have suggested that an unstable postural balance (e.g., larger COP motion) influences the greater motion of the gun barrel and results in a poor performance score. The relation between postural COP control and the gun barrel movement or shooting score has been analyzed by the respective linear motion parameters (e.g., standard deviation, velocity, and root mean square, etc.) of these variables. How the upper-limb kinematics are co-related or co-varied with postural control during a pistol-shooting task has not been directly studied. The analysis of coordination and control between postural balance and arm joint motion as a function of skill level was the central issue of the present study. In particular, we sought to understand how the COP differentially supports multi-joint arm movements for minimizing fluctuations of the end point (e.g., gun barrel) between novice and skilled shooting groups. 1.3. Hypotheses The purpose of the experiment was to investigate whether there are different: 1) organizational structures in the upper limb kinematics and postural control system between novice and skilled groups during a pistol aiming task; and 2) coordination patterns between the postural control and arm joint kinematics for the two skill level groups. The hypotheses examined in the study were as follows. There would be a different organizational structure of posture-arm motion between two groups with the skilled group showing: (1) a smaller number of dynamical DOF as indicated by the reduced estimate of independent components in the principal components analysis (PCA), and (2) a tighter coupling between the postural control and upper limb kinematics. The PCA was applied to examine the qualitative properties of movement organization. PCA allows one to compress the original pistol, arm joint and postural COP motions in a higher dimensional space into a lower dimension where it is a functional control space with independent components produced by a weighted combination of the variables (Daffertshofer, Lamoth, Meiher, & Beek, 2004; Ko & Newell, 2015). The PCA provides a method to estimate the number of dynamical DOF in the lower dimensional control space (Ko, Challis, & Newell, 2013; Li, 2006; Newell & Vaillancourt, 2001). 2. Methods 2.1. Participants Sixteen healthy male volunteers participated in the study. The participants were assigned to two groups based on their experience of air-pistol shooting. A skilled group consisted of 8 well-trained air-pistol shooting players who were currently participating in university level shooting competitions. The skilled group had at least 6 years (6–8 years) of air-pistol shooting training and their mean age was 22.5 years (range: 21–24 yr, SD = 1.2). A novice group consisted of 8 sex- and age-matched university students with no prior air-pistol or gun shooting experience. The mean age of the novice group was 22.2 years (range: 21–24 yr, SD = 1.0). The participants were all right-handed and indicated that they were free of any neurological disease or musculoskeletal dysfunction that might negatively influence the air-pistol shooting performance. The study was approved by the Chonbuk National University Review Board and all participants gave informed written consent prior to the testing. 2.2. Apparatus For the pistol-aiming task, an air pistol (weight 1.5 kg, 4.5 mm caliber) that is used for real 10 m air pistol competition was provided for the participants. Prior to testing, 4 reflective markers were attached on the barrel of the pistol, wrist (ulnar styloid), elbow (radius head), and shoulder (acromion process), respectively (Fig. 1). The Optotrak Certus (Northern Digital Inc., Waterloo, Canada) with 3 cameras, uniformly placed on the floor, captured these markers in the anterior-posterior (AP), medial-lateral (ML), and vertical (VER) directions. A Bertec force platform (Bertec Corp., FP4060-08, Columbus, OH, USA) placed on an even floor was used to measure the total body COP both in the AP (COPAP) and ML (COPML) directions. The motion capture system and the force platform were synchronized for data collection (sampled at 100 Hz). The raw data were filtered after collection by a low-pass forthorder Butterworth filter with cut-off frequency at 5 Hz for further analyses. 2.3. Procedures Participants were required to aim the air-pistol at the origin of target as accurately as possible without any motion. To perform the 256
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Fig. 1. Experimental setup. ML: medio-lateral, AP: anterior-posterior, VER: vertical.
task, participants stood on the force platform with bare feet, side-by-side with a comfortable feet width apart, and eyes open. Then, the participants grabbed the air-pistol and turn their head right to aim the gun at a center of the target that was shown up a wall 10 m away from the force platform at eye level (Fig. 1). Each participant had a total of 2 trials (30 s for a trial) and was provided 1 min break between trials to prevent fatigue. 2.4. Data analysis 2.4.1. Variability The standard deviation (SD) of the COP was calculated in both AP and ML directions for each participant. The variation of motion at each joint together with the pistol motion was quantified by SD and total length of path (for more details see Prieto, Myklebust, Hoffmann, Lovett, & Myklebust, 1996) in the AP, ML, and VER directions. 2.4.2. Principal components analysis (PCA) PCA was applied to examine how the postural control and arm movements are organized together during the aiming task. For each subject and each trial a data matrix formed by 11 columns (3 joints in 3D and the COP in 2D) and 3000 rows corresponding to a collected data length for 30 s was analyzed. Prior to the running of the PCA, 11 variables were first standardized by demeaning and rescaling unit variance to eliminate a bias about larger amplitude of each variable. Then, the covariance of the matrix was calculated to obtain the eigenvectors and eigenvalues. The significant principal component (PC) was extracted when its eigenvalue was larger than 1-Kaiser criterion (Kaiser, 1960). The criterion have been frequently used based on the idea that if the eigenvalue is greater than 1, then each PC explains at least as much variance as 1 observed variable. Lastly, the loading factors of the variables within each PC were calculated to investigate the contribution of each variable to the corresponding PC. The significant loading coefficient was selected if it was over 0.4 (Kachigan, 1986). 2.4.3. Coherence analysis Coherence analysis was conducted to investigate the coordination pattern dynamics between the postural control and arm joint motion. It provides the spatial relation between two variables as a function frequency. The multi-taper spectral analysis was applied to decrease the spectrum estimation bias by picking up multiple independent estimates from the variables using discrete prolate spheroidal sequences (Ko & Newell, 2015; Thomson, 1982; Wang, Ko, Challis, & Newell, 2014). Depending on the sample rate and bandwidth of the data 5 tapers were selected for all pairs of coherence analysis and computed as follows:
coherence (f )2 =
|Sxy (f )|2 Sx (f )·Sy (f )
(1)
where Sx (f ) and Sy (f ) are the power spectral density of variable x and y and Sxy (f ) is the cross power spectral density of the two variables. The coherence in the frequency domain has a range from 0 to 1, no correlation and perfect correlation, respectively. According to the fast Fourier transform (FFT), most of frequency power (95%) was within 0–1 Hz for all variables, therefore, the coherence was reported in that frequency range. For statistics, the mean and SD of coherence in the range, where there are thirty coherence values based on the frequency resolution of 0.033, were calculated. 257
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Fig. 2. Group average of the standard deviation of center of pressure in the medio-lateral and anterior-posterior direction. The error bar is the standard error. represents a significant difference between groups.
*
2.5. Statistics The coherence between the COP and joint motions for the novice and skilled groups was compared by t test. The differences of the other dependent variables (SD of COP, joint and pistol; total length of joint and pistol motion; variance of PC) between two groups were analyzed by a two-way analysis of variance (ANOVA). For the post-hoc analysis, the Bonferroni test was applied. To verify that the number of extracted PCs (eigenvalue > 1) was significantly different between the two groups, Wilcoxon’s signed rank test was conducted. The significant alpha value was set at 0.05. 3. Results 3.1. Variability of COP Fig. 2 shows the average SD of COP motion in both the ML and AP directions for the novice and skilled groups. The ANOVA revealed that there were significant main effects of group, F(1, 28) = 30.67, p < 0.001 and direction, F(1, 28) = 93.01, p < 0.001. There was also an interaction effect between group and direction, F(1, 28) = 24.94, p < 0.001. The post-hoc analysis revealed that the variability of COP in the AP direction was significantly larger for the novice than the skilled group. 3.2. Variability of joint and pistol motions Fig. 3 presents arm joint motions (e.g., shoulder, elbow, and wrist) as a function of time from a representative participant from both groups, respectively. In general, the novice group showed a greater amount of upper limb motion compared to the skilled group during the aiming task. Table 1 presents the SD and total length of joint and pistol motion in the three directions. 3.2.1. Variability of shoulder For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 22.40, p < 0.001, and direction, F (2, 42) = 27.51, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 15.74, p < 0.001. The post-hoc analysis revealed that the SD in the AP direction was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 171.78, p < 0.001 and direction, F(2, 42) = 90.24, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 3.26, p < 0.049. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions. 3.2.2. Variability of elbow For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 26.84, p < 0.001 and direction, F (2, 42) = 17.07, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 12.15, p < 0.001. The post-hoc analysis revealed that the SD in the AP and VER direction was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 222.58, p < 0.001 and 258
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Fig. 3. Arm joint motion (shoulder: upper row, elbow: middle row, and wrist: bottom row) in the ML (left column), AP (middle column), and VER (right column) directions from a representative participant of the skilled and novice group, respectively. ML: medio-lateral, AP: anterior-posterior, VER: vertical.
Table 1 Group average of the standard deviation (standard error) and total length of joint and pistol motions. Standard deviation (cm)
Shoulder_ML Shoulder_AP Shoulder_VER Elbow_ML Elbow_AP Elbow_VER Wrist_ML Wrist_AP Wrist_VER Gun_ML Gun_AP Gun_VER
Total length (cm)
Skilled
Novice
p value
Skilled
0.34 0.31 0.12 0.33 0.31 0.18 0.34 0.35 0.21 0.34 0.35 0.21
0.32 0.94 0.22 0.32 0.86 0.38 0.31 0.84 0.43 0.32 0.82 0.46
p p p p p p p p p p p p
2.90 7.24 2.02 2.61 7.40 3.66 2.75 8.51 5.88 2.96 9.69 7.86
(0.03) (0.04)* (0.02) (0.03) (0.04)* (0.03)* (0.03) (0.03)* (0.04)* (0.03) (0.03)* (0.04)*
(0.05) (0.13) (0.03) (0.04) (0.12) (0.04) (0.05) (0.11) (0.05) (0.05) (0.11) (0.06)
> < > > < < > < < > < <
0.05 0.001 0.05 0.05 0.001 0.010 0.05 0.001 0.012 0.05 0.001 0.006
(0.22)* (0.51)* (0.35)* (0.19)* (0.47)* (0.63)* (0.22)* (0.49)* (0.96)* (0.24)* (0.53)* (0.88)*
Novice
p value
8.43 (0.65) 13.86 (0.50) 6.06 (0.65) 8.10 (0.60) 16.27 (0.61) 12.49 (1.01) 8.68 (0.62) 20.25 (0.89) 19.32 (1.07) 10.19 (0.78) 25.31 (1.17) 29.21 (1.56)
p p p p p p p p p p p p
< < < < < < < < < < < <
0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Note: AP – anterior-posterior; ML – medio-lateral; VER – vertical. * Represents significant difference between two groups (p < 0.05).
direction, F(2, 42) = 52.58, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 4.67, p < 0.016. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions.
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Table 2 The number of trials on which the number of principal components (PCs) has an eigenvalue > 1. Number of PC
1 2 3
Group Skilled
Novice
3 13 0
0 1 15
3.2.3. Variability of wrist For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 21.50, p < 0.001 and direction, F (2, 42) = 13.80, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 8.99, p < 0.002. The post-hoc analysis revealed that the SD in the AP and VER directions was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 273.91, p < 0.001 and direction, F(2, 42) = 71.12, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 13.16, p < 0.001. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions. 3.2.4. Variability of gun For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 21.88, p < 0.001 and direction, F (2, 42) = 12.07, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 8.52, p < 0.002. The post-hoc analysis revealed that the SD in the AP and VER direction was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 355.22, p < 0.001, and direction, F(2, 42) = 95.80, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) 8 27.52, p < 0.001. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions. 3.3. Principal components analysis Table 2 shows the number of trials that represents the number of significant principal components (PCs) that have an eigenvalue larger than 1. The participants ((n = 8) × (2 trials) = total of 16 trials) in the skilled group primarily showed two PCs (13 trials), whereas those in the novice group dominantly used three PCs (15 trials) to aim the pistol on the target during a performance. Wilcoxon’s signed rank test revealed that the number of trials of the extracted PCs was significantly different between two groups (p < 0.002). Therefore, the interpretations of the PCA analysis were focused on two PCs for the expert and three PCs for the novice group. Fig. 4 presents the average percentage of variance accounted by each PC for the two groups. The ANOVA revealed that there was a significant main effect of PCs, F(2, 42) = 490.61, p < 0.001, and an interaction effect between group and PCs, F(2, 42) = 31.48, p < 0.001. The post-hoc analysis revealed that the variance of PC1 was larger for the skilled than the novice group while that of PC3 was larger for the novice than the skilled group.
Fig. 4. Mean variance in the first three principal components. The error bar is the standard error.
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*
represents a significant difference between groups.
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Table 3 The number of trials with the loading coefficient greater than 0.4. Variables
Group Skilled
Shoulder_ML Shoulder_AP Shoulder_VER Elbow_ML Elbow_AP Elbow_VER Wrist_ML Wrist_AP Writs_VER COP_ML COP_AP
Novice
PC1
PC2
PC1
PC2
PC3
0 16 0 0 15 0 0 10 0 1 16
1 0 12 1 0 9 1 0 8 3 0
1 3 1 4 7 0 4 9 1 0 4
6 1 5 5 1 3 5 3 2 1 2
1 0 6 0 0 6 2 0 7 5 1
PC: principal component, COP: center of pressure, ML: medio-lateral, AP: anterior-posterior, and VER: vertical.
Table 3 shows the number of trials that included a loading coefficient greater than 0.4. For the skilled group, all joints and COP in the AP direction strongly contributed in PC1 and all joints in the VER direction were highly involved in PC2. The other variables generally did not provide any significant contribution for these two PCs. On the other hand, for the novice group, the elbow and wrist joints in the AP direction in PC1, all joints in the ML direction in PC2, and all joints in the VER direction in PC3 contributed to each corresponding PC with relatively larger contribution of the other variables compared to the skilled group. 3.4. Coherence Table 4 presents the coherence between the COP and each joint motion in the ML, AP, and VER directions in the frequency range from 0 to 1 Hz. Among a number of pairs there were significantly higher coherence of COPML–ElbowAP, t(7) = 2.79, p < 0.015, COPML–WristAP, t(7) = 3.25, p < 0.006, COPAP–ShoulderAP, t(7) = 2.96, p < 0.011, COPAP–ElbowAP, t(7) = 5.63, p < 0.001, and COPAP–WristAP, t(7) = 5.53, p < 0.001, for the skilled than for the novice group. 4. Discussion The experiment was set up to investigate in novice and skilled groups the movement organizational structure of the upper limb kinematics (shoulder, elbow, and wrist joints) and the postural control system (COP) in a pistol-aiming task. The couplings between the postural control and arm joint kinematics were also a focus of the study. Participants were instructed to use their preferred armhand complex to aim the pistol at the center of the target for 30 s while standing on a force plate with feet a comfortable distance Table 4 Group average (SE) coherence of the center of pressure (COP) and joint couplings in 0–1 Hz. Pairs
Group Coherence
CML-SML
Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice
CML-EML CML-WML CML-SAP CML-EAP* CML-WAP* CML-SVER CML-EVER CML-WVER
0.47 0.44 0.47 0.44 0.47 0.43 0.66 0.52 0.65 0.47 0.62 0.44 0.47 0.41 0.42 0.39 0.41 0.40
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.05) (0.04) (0.05) (0.03) (0.04) (0.03) (0.03) (0.02) (0.02) (0.02) (0.02) (0.02)
Pairs
Group Coherence
CAP-SML
Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice
CAP-EML CAP-WML CAP-SAP* CAP-EAP* CAP-WAP* CAP-SVER CAP-EVER CAP-WVER
Note: C – center of pressure (COP); S – Shoulder; E – Elbow; W – Wrist; AP – anterior-posterior; ML – medio-lateral; VER – vertical. * Represents significant difference between two groups (p < 0.05).
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0.41 0.38 0.40 0.38 0.40 0.39 0.92 0.84 0.86 0.73 0.79 0.66 0.46 0.44 0.36 0.42 0.35 0.41
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.01) (0.02) (0.01) (0.02) (0.02) (0.02) (0.03) (0.03) (0.02) (0.03) (0.02) (0.03)
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apart. The main results revealed that: 1) the modal number of principal components for the skilled group was less than that for the novice group (Table 2), and 2) the coherence between COP and arm joint motion in the AP direction, between COP in ML and the joint motion in AP (except for shoulder) were higher for the skilled than for the novice group (Table 4). 4.1. DOF problem in aiming task To accurately maintain the orientation of the pistol to the target center the system is in effect solving the fundamental movement DOF problem (Bernstein, 1967), as to how the large number of components across multiple levels of the system (e.g., neurons, motor units, muscles, and joints, etc.) is controlled. It seems that there are two general sub-system movement organizations working together to realize the specific goal-directed task. That is, the arm-joint movement that more directly controls the end-point (e.g., gun barrel) fluctuations and postural movement that supports human voluntary upper-limb behavior in supra-postural tasks (Riley, Stoffregen, Grocki, & Turvey, 1999; Ting, 2007). The many DOF reflected in the individual sub-movements allows for redundancy in the set of possible solutions to realize the task. Bernstein (1967) proposed that the redundancy problem was resolved by simplifying the control of multiple DOF by coupling or grouping the output of the kinematic variables. This hypothesis was examined in the current study by PCA that allows the determination of how the multiple joint motions in the upper limb and postural properties are coordinated and controlled to keep the end point of the gun at the target center. 4.2. Organizational structure between posture and upper-limb The PCA analysis showed that two PCs were generally needed to capture the dynamic organization of the total variance of data for the skilled group whereas three PCs were needed for the novice group (Table 2). The findings support previous studies that found different skill levels through the number of PCA components in several action contexts, including race walking (Dona, Preatoni, Cobelli, Rodano, & Harrison, 2009), cello bowing (Verrel, Pologe, Manselle, Lindenberger, & Woollacott, 2013), and playing piano (Winges & Furuya, 2015). The different number of PCs between the novice and skilled group supports the hypothesis that beginners need more dynamical DOF to coordinate and control movement in the pistol-aiming task. The smaller number of PCs for the skilled group also supports the findings of the early pistol aiming study that showed the experienced marksmen aimed the pistol at the target by a stronger positive connection between the shoulder and wrist joints in the horizontal plane (Arutyunyan et al., 1968). Thus, the dimension of the movement coordination and control in the skilled group was suppressed by the functional couplings of the multiple DOF into coordinated action (Haken, 1996; Newell & Van Emmerik, 1989; Verrel et al., 2013). However, it has been proposed that the direction of skill-induced change in dimension of the attractor dynamics is constraint specific, in particular with respect to the influence of task constraints (Newell & Vaillancourt, 2001). The loading coefficients of the PCA revealed differences in the nature of the independent control mechanisms (e.g., each PC) between two groups (Table 3). For PC1 the skilled group was robust between participants as it correlated mainly with the shoulder, elbow, wrist and COP motions in the AP direction. On the other hand, PC1 of the novice group was relatively variable which means that the elbow and wrist motions in the AP direction were moderately correlated but other variables such as the elbow and wrist joints in the ML direction had only a small contribution. PC2 of the skilled group was mainly accounted for by the shoulder, elbow, and wrist motions in the VER direction while that of the novice group was inconsistent between participants but slightly encompassing the upper limb joint motions in the ML direction. In PC3, for the novice group, where more consistent compared to PC2, the shoulder, elbow, and wrist motions in the VER direction were relatively correlated. 4.3. Different mechanisms as a function of skill level The findings from the PCA support the hypothesis that the two groups use different strategies to control the end-point in the aiming task. The dominant mechanism (e.g., PC1) for both groups was relatively similar in organization of the weighting of the input variables, that is, PC1 reflected synergies of variables in the AP direction where the largest motion of the end-point occurred. However, qualitative differences were found in that the skilled group showed higher contribution of postural control to the aiming accuracy than the novice group. The organization of the second mechanism (e.g., PC2) was clearly different between two groups. For the novice group, this was reflected by the upper limb motions in the ML direction whereas for the skilled group it was explained by couplings among the upper limb joints in the VER direction which were similar to the third mechanism (PC3) of the novice group. The variance accounted by each PC revealed that the skilled group, compared to the novice group, showed a significant increase in PC1 and decrease in PC3. This is consistent with the position that the different skill levels resulting from practice led to the strength of the correlations between variables in the AP direction resulting in being highly loaded on PC1. It has been proposed that an increase in the variance accounted by PCs with practice might represent an increased stability of the coordination modes (Hong & Newell, 2006). Practice induced an increase in the stability of the shoulder, elbow, wrist, and COP motions in the AP direction that might play a more important role in aiming the end-point accurately and precisely at the target center. 4.4. Coordination of posture and arm movements The analysis of coherence between the COP and upper limb joint motion revealed findings consistent with those of the general pattern of outcomes from other PCA analyses of movement skill. For the skilled group the postural control was more tightly coupled 262
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with the arm movements, particularly, the COP – arm joints (shoulder, elbow, and wrist) both in the AP direction and the COP in the ML – arm joints (elbow and wrist) in the AP direction (Table 4). In terms of the relation between the postural control and arm movements during voluntary pointing tasks it has been assumed that the postural control system compensates for the disturbance generated by the upper limb movements in an anticipatory (e.g., higher frequency) or reactive (e.g., lower frequency) manner (Massion, 1992). 5. Conclusions The present study has revealed differences in the organizational structures of the posture–upper limb dynamics between novice and skilled groups during a pistol-aiming task. Collectively, the findings showed that the more skilled group used fewer synergic posture-arm combinations to achieve the aiming task that was also reflected in the tight couplings among the posture and limb variables. In particular, the postural control system was coordinated with the arm movements to minimize the motion of the pistol particularly in the skilled group. The different control process between two groups is presumably a consequence of the different amounts of prior practice by the groups to realize the aiming task. Acknowledgements This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A5B5A01023821). Conflict of the interest statement The authors have declared that no competing interests exist. References Arutyunyan, G. A., Gurfinkel, V. S., & Mirskii, M. L. (1968). Investigation of aiming at a target. Biofizika, 13(3), 536–538. Arutyunyan, G. A., Gurfinkel, V. S., & Mirskii, M. L. (1969). Organization of movements on execution by man of an exact postural task. Biofizika, 14(6), 1103–1107. Bernstein, N. A. (1967). The co-ordination and regulation of movements. Oxford: Pergamon Press. Daffertshofer, A., Lamoth, J. C., Meiher, O. G., & Beek, P. J. (2004). PCA in studying coordination and variability: A tutorial. Clinical Biomechanics, 19(4), 415–428. Dona, G., Preatoni, E., Cobelli, C., Rodano, R., & Harrison, A. J. (2009). Application of functional principal component analysis in race walking: An emerging methodology. Sports Biomechanics, 8(4), 284–301. Era, P., Konttinen, N., Mehto, P., Saarela, P., & Lyytinen, H. (1996). Postural stability and skilled performance—A study on top-level and naive rifle shooters. Journal of Biomechanics, 29(3), 301–306. Fenici, R., Ruggieri, M. P., Brisinda, D., & Fenici, P. (1999). Cardiovascular adaptation during action pistol shooting. Journal of Sports Medicine and Physical Fitness, 39(3), 259–266. Haken, H. (1996). Principles of brain functioning: A synergetic approach to brain activity, behaviour and cognition. Berlin: Springer. Hong, S. L., & Newell, K. M. (2006). Change in the organization of degrees of freedom with learning. Journal of Motor Behavior, 38(2), 88–100. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. Kachigan, S. K. (1986). Multivariate statistical analysis. New York: Radius Press. Kelleran, K. J., Morrison, S., & Russell, D. M. (2016). Three-dimensional assessment of postural tremor during goal-directed aiming. Experimental Brain Research, 234(12), 3399–3409. Ko, J. H., Challis, J. H., & Newell, K. M. (2013). Postural coordination patterns as a function of rhythmical dynamics of the surface of support. Experimental Brain Research, 226(2), 183–191. Ko, J. H., & Newell, K. M. (2015). Organization of postural coordination patterns as a function of scaling the surface of support dynamics. Journal of Motor Behavior, 47(5), 415–426. Konttinen, N., Lyytinen, H., & Era, P. (1999). Brain slow potentials and postural sway behavior during sharpshooting performance. Journal of Motor Behavior, 31(1), 11–20. Lakie, M., Villagra, F., Bowman, I., & Wilby, R. (1995). Shooting performance is related to forearm temperature and hand tremor size. Journal of Sports Sciences, 13(4), 313–320. Landers, D. M., Qi, W. M., & Courtet, P. (1985). Peripheral narrowing among experienced and inexperienced rifle shooters under low-and high-stress conditions. Research Quarterly for Exercise and Sport, 56(2), 122–130. Li, Z. M. (2006). Functional degrees of freedom. Motor Control, 10(4), 301–310. Loze, G. M., Collins, D., & Holmes, P. S. (2001). Pre-shot EEG alpha-power reactivity during expert air-pistol shooting: A comparison of best and worst shots. Journal of Sports Sciences, 19(9), 727–733. Massion, J. (1992). Movement, posture and equilibrium: Interaction and coordination. Progress in Neurobiology, 38(1), 35–56. Mononen, K., Konttinen, N., Viitasalo, J., & Era, P. (2007). Relationships between postural balance, rifle stability and shooting accuracy among novice rifle shooters. Scandinavian Journal of Medicine & Science in Sports, 17, 180–185. Newell, K. M., & Vaillancourt, D. E. (2001). Dimensional change in motor learning. Human Movement Science, 20(4), 695–716. Newell, K. M., & Van Emmerik, R. E. A. (1989). The acquisition of coordination: Preliminary analysis of learning to write. Human Movement Science, 8(1), 17–32. Prieto, T. E., Myklebust, J. B., Hoffmann, R. G., Lovett, E. G., & Myklebust, B. M. (1996). Measures of postural steadiness: Differences between healthy young and elderly adults. IEEE Transactions on Biomedical Engineering, 43(9), 956–966. Riley, M. A., Stoffregen, T. A., Grocki, M. J., & Turvey, M. T. (1999). Postural stabilization for the control of touching. Human Movement Science, 18(6), 795–817. Tang, W. T., Zhang, W. Y., Huang, C. C., Young, M. S., & Hwang, I. S. (2008). Postural tremor and control of the upper limb in air pistol shooters. Journal of Sports Sciences, 26(14), 1579–1587. Thomson, D. J. (1982). Spectrum estimation and harmonic analysis. Proceedings of the IEEE, 70(9), 1055–1096. Ting, L. H. (2007). Dimensional reduction in sensorimotor systems: A framework for understanding muscle coordination of posture. Progress in Brain Research, 165, 299–321. Verrel, J., Pologe, S., Manselle, W., Lindenberger, U., & Woollacott, M. (2013). Coordination of degrees of freedom and stabilization of task variables in a complex motor skill: Expertise-related differences in cello bowing. Experimental Brain Research, 224(3), 323–334. Wang, Z., Ko, J. H., Challis, J. H., & Newell, K. M. (2014). The degrees of freedom problem in human standing posture: Collective and component dynamics. PLoS One, 9(1), e85414. Winges, S. A., & Furuya, S. (2015). Distinct digit kinematics by professional and amateur pianists. Neuroscience, 284, 643–652.
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Contents lists available at ScienceDirect
Human Movement Science journal homepage: www.elsevier.com/locate/humov
Skill level constrains the coordination of posture and upper-limb movement in a pistol-aiming task
MARK
⁎
Ji Hynu Koa, Dong Wook Hana, , Karl M. Newellb a b
Department of Sport Science, Chonbuk National University, Jeonju, South Korea Department of Kinesiology, University of Georgia, Athens, USA
AR TI CLE I NF O
AB S T R A CT
Keywords: Postural control Arm movement Pistol-aiming PCA
The purpose of the experiment was to investigate whether skill level differentially organizes the coordination of the postural system and upper limb kinematics in a pistol-aiming task. Participants aimed an air-pistol at a target center in 30 s trials as accurately as possible while standing on a force platform with shooting arm joint kinematics recorded. The novice group had greater motion of the pistol end point, arm joints and the center of pressure than the skilled group. Principal components analysis (PCA) showed that the skilled group required 2 components as opposed to the 3 components of the novice group to accommodate the variance. Coherence analysis in the 0–1 Hz bandwidth revealed that the coupling between posture and upper-limb movement was stronger in the skilled than the novice group. The findings are consistent with the view that skill acquisition reduces the kinematic variables into a lower dimensional functional unit that in pistol-aiming is defined over the collective posture and upper-limb system.
1. Introduction A goal-directed aiming task such as pistol-shooting involves multi-joint arm motions and minimal fluctuations of movement amplitude at its end point (e.g., gun barrel) to achieve a high degree of performance accuracy. It has been shown that there are a number of processes contributing to the realization of successful performance in this task including psychological (e.g., intention and stress; Landers, Qi, & Courtet, 1985; Loze, Collins, & Holmes, 2001) and neurophysiological (e.g., cardiovascular adaptation and physiological tremor; Fenici, Ruggieri, Brisinda, & Fenici, 1999; Kelleran, Morrison, & Russell, 2016; Lakie, Villagra, Bowman, & Wilby, 1995; Tang, Zhang, Huang, Young, & Hwang, 2008). Here we focus on how the skill level influences the coordination and control of upper-limb and posture motion in a pistol-aiming task. 1.1. Pistol aiming studies To achieve accuracy in pistol-aiming fundamentally requires the shooter to align the gun barrel with the target. This means that the redundant kinematic arm joint motions (Bernstein, 1967) need to be effectively constrained and coordinated to manage the orientation of the gun with the target. This was initially investigated in the classic studies of Arutyunyan, Gurfinkel, and Mirskii (1968, 1969) that compared pistol shooting in experienced and inexperienced marksmen. They showed that the experienced group had a smaller dispersion of the gun motion compared to the inexperienced group. Furthermore, the reduced gun motion of the
⁎
Corresponding author at: Department of Sport Science, 520, Natural Science Building 15-0, Chonbuk National University, Jeonju 54896, South Korea. E-mail address: [email protected] (D.W. Han).
http://dx.doi.org/10.1016/j.humov.2017.08.017 Received 7 March 2017; Received in revised form 20 July 2017; Accepted 21 August 2017 0167-9457/ © 2017 Elsevier B.V. All rights reserved.
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experienced group was related to the released joint motions of the arm and the presence of compensatory arm-motion synergies. In contrast, the inexperienced group controlled the gun with tightly fixed kinematic linkages of the arm joints and was, in effect, controlling the act of pistol-shooting from the shoulder joint. 1.2. Postural control in aiming tasks Postural control is a critical factor for the control of arm motion in accurate pistol aiming. Successful performance of aiming and pointing tasks requires the integration of posture and limb control and the coordination of multiple degrees of freedom (DOF) (Bernstein, 1967). In shooting tasks, a number of studies have shown that stable postural balance control is significantly related to high-level performance. For example, in rifle (Era, Konttinen, Mehto, Saarela, & Lyytinen, 1996) and sharp (Konttinen, Lyytinen, & Era, 1999) shooting studies experienced shooters showed a reduced amount of motion of the center of pressure (COP) compared to inexperienced shooters. Mononen, Konttinen, Viitasalo, and Era (2007) have suggested that an unstable postural balance (e.g., larger COP motion) influences the greater motion of the gun barrel and results in a poor performance score. The relation between postural COP control and the gun barrel movement or shooting score has been analyzed by the respective linear motion parameters (e.g., standard deviation, velocity, and root mean square, etc.) of these variables. How the upper-limb kinematics are co-related or co-varied with postural control during a pistol-shooting task has not been directly studied. The analysis of coordination and control between postural balance and arm joint motion as a function of skill level was the central issue of the present study. In particular, we sought to understand how the COP differentially supports multi-joint arm movements for minimizing fluctuations of the end point (e.g., gun barrel) between novice and skilled shooting groups. 1.3. Hypotheses The purpose of the experiment was to investigate whether there are different: 1) organizational structures in the upper limb kinematics and postural control system between novice and skilled groups during a pistol aiming task; and 2) coordination patterns between the postural control and arm joint kinematics for the two skill level groups. The hypotheses examined in the study were as follows. There would be a different organizational structure of posture-arm motion between two groups with the skilled group showing: (1) a smaller number of dynamical DOF as indicated by the reduced estimate of independent components in the principal components analysis (PCA), and (2) a tighter coupling between the postural control and upper limb kinematics. The PCA was applied to examine the qualitative properties of movement organization. PCA allows one to compress the original pistol, arm joint and postural COP motions in a higher dimensional space into a lower dimension where it is a functional control space with independent components produced by a weighted combination of the variables (Daffertshofer, Lamoth, Meiher, & Beek, 2004; Ko & Newell, 2015). The PCA provides a method to estimate the number of dynamical DOF in the lower dimensional control space (Ko, Challis, & Newell, 2013; Li, 2006; Newell & Vaillancourt, 2001). 2. Methods 2.1. Participants Sixteen healthy male volunteers participated in the study. The participants were assigned to two groups based on their experience of air-pistol shooting. A skilled group consisted of 8 well-trained air-pistol shooting players who were currently participating in university level shooting competitions. The skilled group had at least 6 years (6–8 years) of air-pistol shooting training and their mean age was 22.5 years (range: 21–24 yr, SD = 1.2). A novice group consisted of 8 sex- and age-matched university students with no prior air-pistol or gun shooting experience. The mean age of the novice group was 22.2 years (range: 21–24 yr, SD = 1.0). The participants were all right-handed and indicated that they were free of any neurological disease or musculoskeletal dysfunction that might negatively influence the air-pistol shooting performance. The study was approved by the Chonbuk National University Review Board and all participants gave informed written consent prior to the testing. 2.2. Apparatus For the pistol-aiming task, an air pistol (weight 1.5 kg, 4.5 mm caliber) that is used for real 10 m air pistol competition was provided for the participants. Prior to testing, 4 reflective markers were attached on the barrel of the pistol, wrist (ulnar styloid), elbow (radius head), and shoulder (acromion process), respectively (Fig. 1). The Optotrak Certus (Northern Digital Inc., Waterloo, Canada) with 3 cameras, uniformly placed on the floor, captured these markers in the anterior-posterior (AP), medial-lateral (ML), and vertical (VER) directions. A Bertec force platform (Bertec Corp., FP4060-08, Columbus, OH, USA) placed on an even floor was used to measure the total body COP both in the AP (COPAP) and ML (COPML) directions. The motion capture system and the force platform were synchronized for data collection (sampled at 100 Hz). The raw data were filtered after collection by a low-pass forthorder Butterworth filter with cut-off frequency at 5 Hz for further analyses. 2.3. Procedures Participants were required to aim the air-pistol at the origin of target as accurately as possible without any motion. To perform the 256
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Fig. 1. Experimental setup. ML: medio-lateral, AP: anterior-posterior, VER: vertical.
task, participants stood on the force platform with bare feet, side-by-side with a comfortable feet width apart, and eyes open. Then, the participants grabbed the air-pistol and turn their head right to aim the gun at a center of the target that was shown up a wall 10 m away from the force platform at eye level (Fig. 1). Each participant had a total of 2 trials (30 s for a trial) and was provided 1 min break between trials to prevent fatigue. 2.4. Data analysis 2.4.1. Variability The standard deviation (SD) of the COP was calculated in both AP and ML directions for each participant. The variation of motion at each joint together with the pistol motion was quantified by SD and total length of path (for more details see Prieto, Myklebust, Hoffmann, Lovett, & Myklebust, 1996) in the AP, ML, and VER directions. 2.4.2. Principal components analysis (PCA) PCA was applied to examine how the postural control and arm movements are organized together during the aiming task. For each subject and each trial a data matrix formed by 11 columns (3 joints in 3D and the COP in 2D) and 3000 rows corresponding to a collected data length for 30 s was analyzed. Prior to the running of the PCA, 11 variables were first standardized by demeaning and rescaling unit variance to eliminate a bias about larger amplitude of each variable. Then, the covariance of the matrix was calculated to obtain the eigenvectors and eigenvalues. The significant principal component (PC) was extracted when its eigenvalue was larger than 1-Kaiser criterion (Kaiser, 1960). The criterion have been frequently used based on the idea that if the eigenvalue is greater than 1, then each PC explains at least as much variance as 1 observed variable. Lastly, the loading factors of the variables within each PC were calculated to investigate the contribution of each variable to the corresponding PC. The significant loading coefficient was selected if it was over 0.4 (Kachigan, 1986). 2.4.3. Coherence analysis Coherence analysis was conducted to investigate the coordination pattern dynamics between the postural control and arm joint motion. It provides the spatial relation between two variables as a function frequency. The multi-taper spectral analysis was applied to decrease the spectrum estimation bias by picking up multiple independent estimates from the variables using discrete prolate spheroidal sequences (Ko & Newell, 2015; Thomson, 1982; Wang, Ko, Challis, & Newell, 2014). Depending on the sample rate and bandwidth of the data 5 tapers were selected for all pairs of coherence analysis and computed as follows:
coherence (f )2 =
|Sxy (f )|2 Sx (f )·Sy (f )
(1)
where Sx (f ) and Sy (f ) are the power spectral density of variable x and y and Sxy (f ) is the cross power spectral density of the two variables. The coherence in the frequency domain has a range from 0 to 1, no correlation and perfect correlation, respectively. According to the fast Fourier transform (FFT), most of frequency power (95%) was within 0–1 Hz for all variables, therefore, the coherence was reported in that frequency range. For statistics, the mean and SD of coherence in the range, where there are thirty coherence values based on the frequency resolution of 0.033, were calculated. 257
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Fig. 2. Group average of the standard deviation of center of pressure in the medio-lateral and anterior-posterior direction. The error bar is the standard error. represents a significant difference between groups.
*
2.5. Statistics The coherence between the COP and joint motions for the novice and skilled groups was compared by t test. The differences of the other dependent variables (SD of COP, joint and pistol; total length of joint and pistol motion; variance of PC) between two groups were analyzed by a two-way analysis of variance (ANOVA). For the post-hoc analysis, the Bonferroni test was applied. To verify that the number of extracted PCs (eigenvalue > 1) was significantly different between the two groups, Wilcoxon’s signed rank test was conducted. The significant alpha value was set at 0.05. 3. Results 3.1. Variability of COP Fig. 2 shows the average SD of COP motion in both the ML and AP directions for the novice and skilled groups. The ANOVA revealed that there were significant main effects of group, F(1, 28) = 30.67, p < 0.001 and direction, F(1, 28) = 93.01, p < 0.001. There was also an interaction effect between group and direction, F(1, 28) = 24.94, p < 0.001. The post-hoc analysis revealed that the variability of COP in the AP direction was significantly larger for the novice than the skilled group. 3.2. Variability of joint and pistol motions Fig. 3 presents arm joint motions (e.g., shoulder, elbow, and wrist) as a function of time from a representative participant from both groups, respectively. In general, the novice group showed a greater amount of upper limb motion compared to the skilled group during the aiming task. Table 1 presents the SD and total length of joint and pistol motion in the three directions. 3.2.1. Variability of shoulder For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 22.40, p < 0.001, and direction, F (2, 42) = 27.51, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 15.74, p < 0.001. The post-hoc analysis revealed that the SD in the AP direction was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 171.78, p < 0.001 and direction, F(2, 42) = 90.24, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 3.26, p < 0.049. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions. 3.2.2. Variability of elbow For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 26.84, p < 0.001 and direction, F (2, 42) = 17.07, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 12.15, p < 0.001. The post-hoc analysis revealed that the SD in the AP and VER direction was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 222.58, p < 0.001 and 258
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Fig. 3. Arm joint motion (shoulder: upper row, elbow: middle row, and wrist: bottom row) in the ML (left column), AP (middle column), and VER (right column) directions from a representative participant of the skilled and novice group, respectively. ML: medio-lateral, AP: anterior-posterior, VER: vertical.
Table 1 Group average of the standard deviation (standard error) and total length of joint and pistol motions. Standard deviation (cm)
Shoulder_ML Shoulder_AP Shoulder_VER Elbow_ML Elbow_AP Elbow_VER Wrist_ML Wrist_AP Wrist_VER Gun_ML Gun_AP Gun_VER
Total length (cm)
Skilled
Novice
p value
Skilled
0.34 0.31 0.12 0.33 0.31 0.18 0.34 0.35 0.21 0.34 0.35 0.21
0.32 0.94 0.22 0.32 0.86 0.38 0.31 0.84 0.43 0.32 0.82 0.46
p p p p p p p p p p p p
2.90 7.24 2.02 2.61 7.40 3.66 2.75 8.51 5.88 2.96 9.69 7.86
(0.03) (0.04)* (0.02) (0.03) (0.04)* (0.03)* (0.03) (0.03)* (0.04)* (0.03) (0.03)* (0.04)*
(0.05) (0.13) (0.03) (0.04) (0.12) (0.04) (0.05) (0.11) (0.05) (0.05) (0.11) (0.06)
> < > > < < > < < > < <
0.05 0.001 0.05 0.05 0.001 0.010 0.05 0.001 0.012 0.05 0.001 0.006
(0.22)* (0.51)* (0.35)* (0.19)* (0.47)* (0.63)* (0.22)* (0.49)* (0.96)* (0.24)* (0.53)* (0.88)*
Novice
p value
8.43 (0.65) 13.86 (0.50) 6.06 (0.65) 8.10 (0.60) 16.27 (0.61) 12.49 (1.01) 8.68 (0.62) 20.25 (0.89) 19.32 (1.07) 10.19 (0.78) 25.31 (1.17) 29.21 (1.56)
p p p p p p p p p p p p
< < < < < < < < < < < <
0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Note: AP – anterior-posterior; ML – medio-lateral; VER – vertical. * Represents significant difference between two groups (p < 0.05).
direction, F(2, 42) = 52.58, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 4.67, p < 0.016. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions.
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Table 2 The number of trials on which the number of principal components (PCs) has an eigenvalue > 1. Number of PC
1 2 3
Group Skilled
Novice
3 13 0
0 1 15
3.2.3. Variability of wrist For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 21.50, p < 0.001 and direction, F (2, 42) = 13.80, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 8.99, p < 0.002. The post-hoc analysis revealed that the SD in the AP and VER directions was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 273.91, p < 0.001 and direction, F(2, 42) = 71.12, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 13.16, p < 0.001. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions. 3.2.4. Variability of gun For the SD, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 21.88, p < 0.001 and direction, F (2, 42) = 12.07, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) = 8.52, p < 0.002. The post-hoc analysis revealed that the SD in the AP and VER direction was significantly larger for the novice than the skilled group. For the total length, the ANOVA revealed that there were significant main effects of group, F(1, 42) = 355.22, p < 0.001, and direction, F(2, 42) = 95.80, p < 0.001. There was also an interaction effect between group and direction, F(2, 42) 8 27.52, p < 0.001. The post-hoc analysis revealed that the total length was significantly larger for the novice than the skilled group for all three directions. 3.3. Principal components analysis Table 2 shows the number of trials that represents the number of significant principal components (PCs) that have an eigenvalue larger than 1. The participants ((n = 8) × (2 trials) = total of 16 trials) in the skilled group primarily showed two PCs (13 trials), whereas those in the novice group dominantly used three PCs (15 trials) to aim the pistol on the target during a performance. Wilcoxon’s signed rank test revealed that the number of trials of the extracted PCs was significantly different between two groups (p < 0.002). Therefore, the interpretations of the PCA analysis were focused on two PCs for the expert and three PCs for the novice group. Fig. 4 presents the average percentage of variance accounted by each PC for the two groups. The ANOVA revealed that there was a significant main effect of PCs, F(2, 42) = 490.61, p < 0.001, and an interaction effect between group and PCs, F(2, 42) = 31.48, p < 0.001. The post-hoc analysis revealed that the variance of PC1 was larger for the skilled than the novice group while that of PC3 was larger for the novice than the skilled group.
Fig. 4. Mean variance in the first three principal components. The error bar is the standard error.
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*
represents a significant difference between groups.
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Table 3 The number of trials with the loading coefficient greater than 0.4. Variables
Group Skilled
Shoulder_ML Shoulder_AP Shoulder_VER Elbow_ML Elbow_AP Elbow_VER Wrist_ML Wrist_AP Writs_VER COP_ML COP_AP
Novice
PC1
PC2
PC1
PC2
PC3
0 16 0 0 15 0 0 10 0 1 16
1 0 12 1 0 9 1 0 8 3 0
1 3 1 4 7 0 4 9 1 0 4
6 1 5 5 1 3 5 3 2 1 2
1 0 6 0 0 6 2 0 7 5 1
PC: principal component, COP: center of pressure, ML: medio-lateral, AP: anterior-posterior, and VER: vertical.
Table 3 shows the number of trials that included a loading coefficient greater than 0.4. For the skilled group, all joints and COP in the AP direction strongly contributed in PC1 and all joints in the VER direction were highly involved in PC2. The other variables generally did not provide any significant contribution for these two PCs. On the other hand, for the novice group, the elbow and wrist joints in the AP direction in PC1, all joints in the ML direction in PC2, and all joints in the VER direction in PC3 contributed to each corresponding PC with relatively larger contribution of the other variables compared to the skilled group. 3.4. Coherence Table 4 presents the coherence between the COP and each joint motion in the ML, AP, and VER directions in the frequency range from 0 to 1 Hz. Among a number of pairs there were significantly higher coherence of COPML–ElbowAP, t(7) = 2.79, p < 0.015, COPML–WristAP, t(7) = 3.25, p < 0.006, COPAP–ShoulderAP, t(7) = 2.96, p < 0.011, COPAP–ElbowAP, t(7) = 5.63, p < 0.001, and COPAP–WristAP, t(7) = 5.53, p < 0.001, for the skilled than for the novice group. 4. Discussion The experiment was set up to investigate in novice and skilled groups the movement organizational structure of the upper limb kinematics (shoulder, elbow, and wrist joints) and the postural control system (COP) in a pistol-aiming task. The couplings between the postural control and arm joint kinematics were also a focus of the study. Participants were instructed to use their preferred armhand complex to aim the pistol at the center of the target for 30 s while standing on a force plate with feet a comfortable distance Table 4 Group average (SE) coherence of the center of pressure (COP) and joint couplings in 0–1 Hz. Pairs
Group Coherence
CML-SML
Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice
CML-EML CML-WML CML-SAP CML-EAP* CML-WAP* CML-SVER CML-EVER CML-WVER
0.47 0.44 0.47 0.44 0.47 0.43 0.66 0.52 0.65 0.47 0.62 0.44 0.47 0.41 0.42 0.39 0.41 0.40
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.05) (0.04) (0.05) (0.03) (0.04) (0.03) (0.03) (0.02) (0.02) (0.02) (0.02) (0.02)
Pairs
Group Coherence
CAP-SML
Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice Skilled Novice
CAP-EML CAP-WML CAP-SAP* CAP-EAP* CAP-WAP* CAP-SVER CAP-EVER CAP-WVER
Note: C – center of pressure (COP); S – Shoulder; E – Elbow; W – Wrist; AP – anterior-posterior; ML – medio-lateral; VER – vertical. * Represents significant difference between two groups (p < 0.05).
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0.41 0.38 0.40 0.38 0.40 0.39 0.92 0.84 0.86 0.73 0.79 0.66 0.46 0.44 0.36 0.42 0.35 0.41
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.01) (0.02) (0.01) (0.02) (0.02) (0.02) (0.03) (0.03) (0.02) (0.03) (0.02) (0.03)
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apart. The main results revealed that: 1) the modal number of principal components for the skilled group was less than that for the novice group (Table 2), and 2) the coherence between COP and arm joint motion in the AP direction, between COP in ML and the joint motion in AP (except for shoulder) were higher for the skilled than for the novice group (Table 4). 4.1. DOF problem in aiming task To accurately maintain the orientation of the pistol to the target center the system is in effect solving the fundamental movement DOF problem (Bernstein, 1967), as to how the large number of components across multiple levels of the system (e.g., neurons, motor units, muscles, and joints, etc.) is controlled. It seems that there are two general sub-system movement organizations working together to realize the specific goal-directed task. That is, the arm-joint movement that more directly controls the end-point (e.g., gun barrel) fluctuations and postural movement that supports human voluntary upper-limb behavior in supra-postural tasks (Riley, Stoffregen, Grocki, & Turvey, 1999; Ting, 2007). The many DOF reflected in the individual sub-movements allows for redundancy in the set of possible solutions to realize the task. Bernstein (1967) proposed that the redundancy problem was resolved by simplifying the control of multiple DOF by coupling or grouping the output of the kinematic variables. This hypothesis was examined in the current study by PCA that allows the determination of how the multiple joint motions in the upper limb and postural properties are coordinated and controlled to keep the end point of the gun at the target center. 4.2. Organizational structure between posture and upper-limb The PCA analysis showed that two PCs were generally needed to capture the dynamic organization of the total variance of data for the skilled group whereas three PCs were needed for the novice group (Table 2). The findings support previous studies that found different skill levels through the number of PCA components in several action contexts, including race walking (Dona, Preatoni, Cobelli, Rodano, & Harrison, 2009), cello bowing (Verrel, Pologe, Manselle, Lindenberger, & Woollacott, 2013), and playing piano (Winges & Furuya, 2015). The different number of PCs between the novice and skilled group supports the hypothesis that beginners need more dynamical DOF to coordinate and control movement in the pistol-aiming task. The smaller number of PCs for the skilled group also supports the findings of the early pistol aiming study that showed the experienced marksmen aimed the pistol at the target by a stronger positive connection between the shoulder and wrist joints in the horizontal plane (Arutyunyan et al., 1968). Thus, the dimension of the movement coordination and control in the skilled group was suppressed by the functional couplings of the multiple DOF into coordinated action (Haken, 1996; Newell & Van Emmerik, 1989; Verrel et al., 2013). However, it has been proposed that the direction of skill-induced change in dimension of the attractor dynamics is constraint specific, in particular with respect to the influence of task constraints (Newell & Vaillancourt, 2001). The loading coefficients of the PCA revealed differences in the nature of the independent control mechanisms (e.g., each PC) between two groups (Table 3). For PC1 the skilled group was robust between participants as it correlated mainly with the shoulder, elbow, wrist and COP motions in the AP direction. On the other hand, PC1 of the novice group was relatively variable which means that the elbow and wrist motions in the AP direction were moderately correlated but other variables such as the elbow and wrist joints in the ML direction had only a small contribution. PC2 of the skilled group was mainly accounted for by the shoulder, elbow, and wrist motions in the VER direction while that of the novice group was inconsistent between participants but slightly encompassing the upper limb joint motions in the ML direction. In PC3, for the novice group, where more consistent compared to PC2, the shoulder, elbow, and wrist motions in the VER direction were relatively correlated. 4.3. Different mechanisms as a function of skill level The findings from the PCA support the hypothesis that the two groups use different strategies to control the end-point in the aiming task. The dominant mechanism (e.g., PC1) for both groups was relatively similar in organization of the weighting of the input variables, that is, PC1 reflected synergies of variables in the AP direction where the largest motion of the end-point occurred. However, qualitative differences were found in that the skilled group showed higher contribution of postural control to the aiming accuracy than the novice group. The organization of the second mechanism (e.g., PC2) was clearly different between two groups. For the novice group, this was reflected by the upper limb motions in the ML direction whereas for the skilled group it was explained by couplings among the upper limb joints in the VER direction which were similar to the third mechanism (PC3) of the novice group. The variance accounted by each PC revealed that the skilled group, compared to the novice group, showed a significant increase in PC1 and decrease in PC3. This is consistent with the position that the different skill levels resulting from practice led to the strength of the correlations between variables in the AP direction resulting in being highly loaded on PC1. It has been proposed that an increase in the variance accounted by PCs with practice might represent an increased stability of the coordination modes (Hong & Newell, 2006). Practice induced an increase in the stability of the shoulder, elbow, wrist, and COP motions in the AP direction that might play a more important role in aiming the end-point accurately and precisely at the target center. 4.4. Coordination of posture and arm movements The analysis of coherence between the COP and upper limb joint motion revealed findings consistent with those of the general pattern of outcomes from other PCA analyses of movement skill. For the skilled group the postural control was more tightly coupled 262
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with the arm movements, particularly, the COP – arm joints (shoulder, elbow, and wrist) both in the AP direction and the COP in the ML – arm joints (elbow and wrist) in the AP direction (Table 4). In terms of the relation between the postural control and arm movements during voluntary pointing tasks it has been assumed that the postural control system compensates for the disturbance generated by the upper limb movements in an anticipatory (e.g., higher frequency) or reactive (e.g., lower frequency) manner (Massion, 1992). 5. Conclusions The present study has revealed differences in the organizational structures of the posture–upper limb dynamics between novice and skilled groups during a pistol-aiming task. Collectively, the findings showed that the more skilled group used fewer synergic posture-arm combinations to achieve the aiming task that was also reflected in the tight couplings among the posture and limb variables. 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