Assessment of Dynamic Balancing Performance of Synchronized Ice Skaters With Sudden Provocation Test via Principal Component Analysis

in Journal of Motor Learning and Development

Click name to view affiliation

Zsófia PályaDepartment of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest, Hungary

Search for other papers by Zsófia Pálya in
Current site
Google Scholar
PubMed
Close
,
Bálint PetróDepartment of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest, Hungary

Search for other papers by Bálint Petró in
Current site
Google Scholar
PubMed
Close
, and
Rita M. KissDepartment of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest, Hungary

Search for other papers by Rita M. Kiss in
Current site
Google Scholar
PubMed
Close
*
Open access

Background: Balancing performance can be affected by regular and high-level athletic training, which has not been fully explored in synchronized ice skaters. This study aimed to analyze the dynamic balancing performance by assessing the principal and compensatory movements performed during the sudden provocation tests and evaluating the parameters that characterize the platform’s motion. Method: Twelve young female synchronized ice skaters and 12 female age-matched controls participated. Sudden provocation tests were completed three times in bipedal stance and in single-leg stances, and sport-specific fatigue session was inserted between the repetitions. Results: Significantly more time was necessary to recover balance for both groups after the fatiguing sessions (p < .05). Interestingly, skaters performed less effectively in the simplest condition (bipedal stance) than the control group (p < .05). The principal component analysis showed that the first principal movement was the same for both groups. The skater group used the upper body and arms more often to compensate, while the control group’s recovery strategy consisted mainly of abduction of the elevated leg. The damping ratio and the relative variance of the first principal movement showed a negative correlation (p < .05), suggesting that those with superior balancing effectiveness recruited more compensatory movements.

The ability to balance upright allows us to keep our body in a static position or maintain balance during locomotion. Numerous factors can affect balancing performance, such as regular and high-level athletic training. This effect has not been fully explored in synchronized ice skaters (Keller et al., 2014). On the other hand, several studies have investigated the balancing abilities of ballet dancers and figure skaters (Crotts et al., 1996; Hugel et al., 1999; Hutt & Redding, 2014; Perrin et al., 2002; Simmons, 2005; Sirois-Leclerc et al., 2017). These sports are related to each other in artistic performance and the exceptional balance training that is required. Still, the major difference is that skaters must maintain postural control during high-velocity movements across the ice (Alpini et al., 2008). Synchronized skaters’ performance emphasizes maintaining the group’s precise formation and timing and includes artistic components such as long-lasting spins and lifting elements (Menshikova et al., 2014).

Balancing abilities can be tested with functional approaches, posturography, or with dynamic balancing tasks. One such method to analyze dynamic balancing assessment is by imposing unidirectional perturbation, for example, with a horizontal translational platform like a therapeutic device the PosturoMed platform. The participant standing on the PosturoMed device and the platform together can be considered a coupled mechanical system (Chagdes et al., 2013; Goodworth & Peterka, 2010). Therefore, the dynamic balancing performance of the participant can be characterized by analyzing the platform’s motion (Kiss, 2011; Müller et al., 2004). The characteristics of the damped oscillatory system quantify the efficiency of the balancing strategy, while the trajectory of the oscillatory platform describes the mode of execution of the recovery action (Kiss, 2011; Petró et al., 2018). Based on previous observations, some compensatory movements (i.e., oscillations perpendicular to the lateral deflection, or sudden compensatory movements with the arms) were also recorded, especially under more challenging circumstances, such as single-leg stance or standing with closed eyes (Boeer et al., 2010; Kiss et al., 2017; Petró et al., 2018). These complex trajectories and deflections related to the compensatory movements might help the participants fulfill the more challenging tasks smoothly. On the other hand, using this strategy may also increase recovery efficiency. One of the aims of this present study was to qualitatively characterize these previously described compensatory movements and their effects on the balancing efficiency.

Principal component analysis (PCA) is an approach that is gaining traction for studying coordination in clinical biomechanics (Brandon et al., 2013; Daffertshofer et al., 2004; Lamoth et al., 2009). PCA is usually applied to kinematic data (position or joint angle values), electromyography, or mixed data sets. An interesting approach is to use PCA on three-dimensional position data, which determines so-called principal movements (PMs; Federolf et al., 2013, 2015; Lamoth et al., 2009). In this interpretation, the most important principal components (PCs) can be regarded as the dominant synergies of the complex motion, reducing the high dimensionality of the recorded data. Applied to joint angular time series, this provides a means to analyze the importance of individual joints to the whole of motion and to determine the magnitude and sign (i.e., phase) of the contribution of individual joints to the pattern (Cowley & Gates, 2017; Daniels et al., 2019; Noé et al., 2017).

Previous research has shown that different skating sports can also affect balancing performance, which is rarely examined in synchronized ice skaters. Unidirectional perturbation tests and the PCA have already been applied separately to characterize human balancing motion; however, the two methods together have not been used thus far. Therefore, the main goal of this present study was to examine the balancing strategy of synchronized skaters compared with an age-matched control group under dynamic circumstances (sudden unidirectional perturbation) with the help of PCA. For this purpose, our primary aim was to develop a measurement method that includes sport-specific rotational fatigue elements and is suitable to assess the dynamic balancing performance. Second, the balance recovery response provided by the unidirectional perturbation was examined with the previously described platform parameters (such as balancing time, directional ratio, and damping ratio) and PCA. Therefore, our secondary aim was to explore and understand the differences in the balancing strategy of the two measurement groups via the determination of the principal and compensatory movements and the help of the linear correlation analysis between the platform parameters and the first PM. Overall, the purpose of the present study was to determine the dynamic balancing performance of female ice skaters as compared to female age-matched controls by (a) assessing the principal and compensatory movements performed during the sudden provocation tests and (b) evaluating the parameters that characterize the platform’s motion. Since ice skating requires exceptional balancing performance due to the challenging tasks these athletes are exposed to, it is suggested that they have developed more complex compensatory movements throughout their training history. Therefore, we hypothesized that ice skaters versus nonskater controls will perform the sudden provocation tests with compensatory movements that involve more segments of their body. Moreover, it is expected that the ice skater group is less affected by the sport-specific fatigue session; thus, we hypothesized that they will perform the postfatigue balance tests more effectively as compared with the age-matched controls.

Materials and Methods

Participants

Twelve young female synchronized ice skaters (20.7 ± 3.1 years, 59.2 ± 5.9 kg, 166.3 ± 4.4 cm) and 12 healthy female age-matched nonskaters (21.5 ± 1.2 years, 61.1 ± 5.5 kg, 171.3 ± 3.5 cm) as control participated in the study. Members of the control group did not play other sports competitively; therefore, other high-level training in sports did not affect the results. The skaters have been skating for 8–19 years and are still active competitors of the Hungarian Synchronized Skating Team. All participants have given their written consent to participate in the experiment after being informed about its aspects. The study was approved by the Science and Research Ethics Committee of the Hungarian University of Physical Education (TE-KEB/17/2021).

Experimental Protocol

In general, dynamic balancing tests have been used to examine balancing performance by imposing external perturbations on participants or providing an unstable dynamic support surface condition (Petró et al., 2017). In this present study, sudden provocation tests were performed using the PosturoMed platform (Haider Bioswing®). The platform is a rigid plate suspended on eight elastic–plastic elements which allow horizontal movement. With the help of the fastening unit, it can be locked in a displaced position (approximately 20 mm from the resting position in the direction of the perturbation). The participant stands in the middle of the platform in this locked position. After releasing the fastening unit, the platform begins a pendulum-like oscillatory motion which the participant must bring to a stop. The effectiveness and the recovery motion’s trajectory can be assessed to quantify balancing capabilities (Petró et al., 2018).

An optical-based motion capture system (OptiTrack, NaturalPoint) was used to record the movements of the platform and the participants with a 100-Hz sampling frequency. The system’s accuracy is submillimeter (Nagymáté & Kiss, 2018). Eight markers were placed on the platform in an octagonal arrangement, whereas a full-body conventional marker-set was used consisting of 39 markers to capture the human motion (Clark et al., 2016). The markers were attached to the mocap suit on the appropriate anatomical landmarks with velcro. The PosturoMed device was placed in the middle of the captured area, and four of the eight suspension elements were locked. The perturbation direction was parallel to the global x-direction of the motion capture system (in a medio-lateral direction), whereas the global z-direction was perpendicular to the perturbation in the horizontal plane.

After the preparation, participants were given some time to become acquainted with the PosturoMed device and the spin-trainer. The spin-trainer is a sport-specific device (or spinner, a foot-sized plate with a curved contact surface to the ground) that was used during the rotational fatigue session to perform off-ice one-foot upright spins. Subsequently, participants were asked to decide which leg they considered their preferred leg (i.e., their laterally dominant lower limb). The spins had to be performed on this foot in each fatiguing session. The flowchart of the measurement procedure can be seen in Figure 1. Three sets of sudden provocation tests were performed, and fatiguing sessions (10 complete one-foot upright spins) were inserted in between them. Any attempt during the provocation test when the participant lost its balance and touched the safety barrier or put the elevated leg down was considered unsuccessful. Figure 1 also shows the allowed number of trials and the required successful ones. During the post1- and post2-tests, the number of allowed retries was reduced to minimize the effect of learning during the measurements and perform the balance recovery tests quickly after the rotations. The order of the bipedal and single-leg stances was randomized for each session, and the number of failed and successful trials were noted.

Figure 1
Figure 1

—Flowchart of the measurement procedure and the provided number of trials/successful trials in each test sessions (pretest, post1-test, and post2-test).

Citation: Journal of Motor Learning and Development 10, 3; 10.1123/jmld.2021-0059

Data Processing and Platform Parameters

The middle point of the oscillatory platform was calculated from the eight recorded markers. The parameters derived from the platform motion were the balancing time (Tend), the damping ratio (D), and the directional ratio (R), all calculated using a self-developed Matlab (version R2019b, The MathWorks Inc.) script. The balancing time (Tend) lasts from the beginning of the perturbation (the moment when the fastening unit is released) until the amplitude of the oscillation settles within ±2 mm of the final position (Giboin et al., 2015). The damping ratio (D), the ratio of the actual and the critical damping values (Schiehlen & Eberhard, 2014), was obtained by identifying the parameter values of a second-order underdamped system from the displacement values of the platform’s middle point in the direction of perturbation using the System Identification Toolbox in Matlab. Finally, the directional ratio (R) gives the ratio of the traveled path parallel to the perturbation and the total traveled path (parallel and perpendicular to the initial perturbation), respectively. Thus, a lower R value means a more circular trajectory (Petró et al., 2018). For each time session (pretest, post1-test, and post2-test), three sets of Tend, D, and R parameters were determined for each participant: one for the bipedal stance, one for the preferred single-leg stance, and one for the nonpreferred single-leg stance. The best attempt was obtained for the first balancing trial (pretest) based on the shortest Tend balancing time.

Principal Component Analysis

The PCA was applied to the 3D marker coordinates of the anatomical landmarks. Our goal with the PCA was to identify the first dominant PM and the less significant (compensatory) PMs during the balancing test. The PCA was performed with the PMAnalyzer software in Matlab (Haid et al., 2019). Before processing the data, a Tend long time series following the perturbation onset was cut from each trial’s originally recorded data set. This ensured that only the balancing task-related motion was analyzed during the PCA. With the help of the analyzer software, the marker data were filtered a fifth order Butterworth filter (fc = 15 Hz). Moreover, the marker displacements were normalized with the participant’s height, which was determined from the vertical displacement of the anterior head marker and ankle marker. The PCA calculation should be performed separately for each stance to obtain meaningful PMs. In the present paper, only the single-leg stance results are detailed, and these measurements had to be examined according to the left-leg and the right-leg stance. The PCA calculations were executed for the skater and control groups and the two single-leg stance types (left and right) separately. In addition to the eigenvalues of the PCs, the predefined subject-specific relative eigenvalues (or relative variance [rVAR]) were also calculated. The relative variance defines the relative amount of the trial-specific variance that can be explained with the respective PM (Haid et al., 2019). Since the PMs are ordered by decreasing overall eigenvalues, the cumulative variance can be derived by adding the explained relative variances. Later in the data postprocessing procedure, the first five PM (PM1–PM5) and their rVAR values will be discussed.

Statistical Analysis

Before the statistical analysis, the calculated variable sets (Tend, D, R) underwent normality tests. The normality of distributions was assessed with the Anderson–Darling test (α = .05) using the Statistics Toolbox of Matlab. The test showed a significant difference of 40%, which means that some variables are not normally distributed. A between-subject study design was adopted in order to compare the skater and control groups at the pretest stage. A Wilcoxon’s rank-sum test (also known as Mann–Whitney U test) was performed on the pretest trials with a significance level of α = .05. A within-subject study design was adopted to test the effect of the fatiguing sessions; Wilcoxon’s signed-rank test was used to compare pretests with posttest trials, also at the α = .05 significance level.

Since the PMs differed in the two groups except for the first PM, it was not possible to statistically compare the relative variances of all the five examined PMs (rVAR1–rVAR5). However, the relative variance of PM1 was examined with Wilcoxon’s signed-rank test to compare the rVAR of the pretests with posttest trials at a significance level of α = .05. Moreover, linear correlation analysis was conducted to examine the relation between the two applied methods (characterization of the platform’s trajectory and PCA). Since the PCA calculation could not be separated by the preferred and nonpreferred leg, the previously calculated platform parameters (Tend, D, and R) were also rearranged by the right and left leg for the correlation analysis. The correlation between the relative variance of PM1, the damping ratio (D), and the directional ratio (R) was investigated via the Spearman correlation test with a significance level of α = .05.

Results

Based on the noted successful trials, the success rate was calculated as the quotient of the successful trials and the used attempts (both failed and successful). It can be concluded that the skaters were more often successful on their nonpreferred leg (pretest: 97%; post1-test: 91%; post2-test: 92%) than nonskaters (pretest: 90%; post1-test: 86%; post2-test: 86%) before and after the rotations. The success rate in the case of the bipedal and preferred leg stance did not differ considerably between the two groups. The bipedal measurements were passed 100% by both groups, moreover, both skaters (pretest: 90%; post1-test: 86%; post2-test: 86%) and nonskaters (pretest: 88%; post1-test: 86%; post2-test: 86%) were less successful on their preferred leg compared with the nonpreferred one.

The statistical analysis comparing the two groups using the pretest data set did not find any significant differences in the calculated PosturoMed parameters (Figure 2a). Figure 2a shows the results of the between-subject study considering differences between the two groups in the calculated variables for the bipedal stance and single-leg stances. The balancing time increased while the directional ratio (R) and the damping ratio (D) decreased in the single-leg stances compared to the bipedal stance. This change in the variables suggests that balancing on a single leg is likely to recruit more anterior–posterior movement (lower R values). Moreover, the balancing recovery strategy is less effective as a smaller damping ratio characterizes it and requires a longer balancing time.

Figure 2
Figure 2

—(a) The between-subject study showing the boxplot of the calculated PosturoMed parameters in the pretest session; (b) the cumulative variance of the first five PMs of the two measurement groups; (c) the overall relative variance of PM1 for the left and right single-leg measurements; (d) the relative variance of PM1 broken down into the three test sessions (pretest, post1-test, and post2-test). p values are related to the results of the Wilcoxson’ s signed-rank test. PM1–PM5 = first five principal movement; rVAR = relative variance; Tend = balancing time; D = damping ratio; R = directional ratio.

Citation: Journal of Motor Learning and Development 10, 3; 10.1123/jmld.2021-0059

The calculated group median values and results of the within-subject study investigating the effect of the fatigue sessions are also summarized in Table 1. The signed-rank test showed that the balancing time increased significantly after the first fatigue session. Specifically, balancing time increased for the skater group in the bipedal and preferred single-leg stances (pT = .0049 and .0009, respectively), while increased for the control group in all stances (pT = .0024, .0425, and .0015). Notably, the skater group’s bipedal stance’s result significantly differed from the pretest result both after the first and second fatigue sessions.

Table 1

Median Values of the Measured Platform Parameters (Tend, D, and R) Calculated From the Best Successful Trials for Control and Skater Group

SkaterControlSkaterControlSkaterControl
TendDR
Medians
 Bipedal
  Pretest2.4602.7100.0710.0680.8340.844
  Post1-test2.8503.3850.0600.0650.8880.879
  Post2-test3.1252.9000.0630.0590.8730.873
 Preferred leg
  Pretest3.4303.8850.0510.0420.7580.748
  Post1-test4.0254.5100.0380.0340.7600.704
  Post2-test3.5804.7300.0390.0240.7830.731
 Nonpreferred leg
  Pretest3.7403.7050.0360.0420.7120.737
  Post1-test3.8455.3300.0260.0280.7510.735
  Post2-test3.8654.0700.0330.0290.7340.743
Differences
 Pretest vs. post1-test 
  Bipedal−0.390*−0.675*0.010*0.002−0.054*−0.035
  Preferred leg−0.595*−0.625*0.0130.008*−0.0020.043
  Nonpreferred leg−0.105−1.625*0.0100.014−0.039*0.002
 Pretest vs. post2-test 
  Bipedal−0.665*−0.1900.008*0.008*−0.039−0.029
  Preferred leg−0.150−0.845*0.0120.018−0.0250.016
  Nonpreferred leg−0.125−0.3650.0030.013−0.022−0.006

Note. The differences indicate the results of the Wilcoxon’s sign-rank test comparing the pre- and posttest time trials. Tend = balancing time; D = damping ratio; R = directional ratio.

*Wilcoxon signed-rank test (p < .05).

As a result of the PCA of the single-leg stances, it could be concluded that at least 85% of the total variance was explained by the first five PMs (Figure 2b). From the sixth PM onward, the averaged subject-specific variance did not reach the 5% threshold for any individual’s PM. Therefore, the presentation of the results was limited to these first five PCs. Note that one participant in the control group had to be excluded from the PCA, because several body markers were covered during the recordings, so the motion could not be reconstructed. Figure 2b shows the cumulative variance of the control and the skater groups for the right and the left single-leg stances, separately. A detailed description of the two groups’ first five PMs is provided in Table 2. The visualization of the first five PMs is presented in Figure 3.

Table 2

Description of the First Five Principal Movements (PMs)

NameDescription
Control
 PM1 (left)Rotation of the whole body around the supporting (left) leg’s hip joint in the frontal plane
 PM2 (left)Raised (right) leg’s flexion around the hip joint in the sagittal plane
 PM3 (left)Raised (right) leg’s abduction in the frontal plane around the hip joint, elevation of the arms in the frontal plane
 PM4 (left)Sway of the torso in the sagittal plane, flexion of the raised (right) leg around the hip joint in the sagittal plane
 PM5 (left)Elevation of the raised (right) leg around the hip joint (abduction and flexion) and the arms in the frontal plane
 PM1 (right)Rotation of the whole body around the supporting (right) leg’s hip joint in the frontal plane
 PM2 (right)Raised (left) leg’s abduction in the frontal plane around the hip joint, forward torso sway, elevation of the arms
 PM3 (right)Backwards sway of the torso, intense elevation of the left arm in the frontal and sagittal planes
 PM4 (right)Sway of the torso around the hip joint and flexion–extension of the elbows in the sagittal plane
 PM5 (right)Sway of the torso in the sagittal plane, raised (left) leg’s abduction in the frontal plane around the hip joint
Skater
 PM1 (left)Rotation of the whole body around the supporting (left) leg’s hip joint in the frontal plane
 PM2 (left)Rotation of the upper body around the transversal axis, rotation of the raised (right) leg in the transversal plane around the knee joint
 PM3 (left)Sway of the torso in the sagittal plane, elevation of the arms in the frontal plane, the raised (right) leg, and the contralateral arm (left) move diagonally
 PM4 (left)Rotation of the upper body around the sagittal axis, elevation of the arms in the frontal plane
 PM5 (left)Elevation of the raised (right) leg around the hip joint (abduction and flexion), arms elevation ahead in the sagittal plane
 PM1 (right)Rotation of the whole body around the supporting (right) leg’s hip joint in the frontal plane
 PM2 (right)Elevation of the raised (left) leg around the hip joint (abduction and flexion), elevation of the arms ahead in the sagittal plane
 PM3 (right)Sway of the torso in the sagittal plane, elevation of the arms in the frontal plane, the raised (left) leg, and the contralateral arm (right) move diagonally
 PM4 (right)Rotation of the upper body around the sagittal axis, elevation of the arms in the frontal plane
 PM5 (right)Rotation of the shoulders around the transversal axis, intense elevation of the arms in the frontal plane
Figure 3
Figure 3

—Visualization of the first five principal movements. (a) Control group and (b) skater group. L = left-leg stance; R = right-leg stance; PM1–PM5 = first five principal movement.

Citation: Journal of Motor Learning and Development 10, 3; 10.1123/jmld.2021-0059

Based on the PMs’ descriptions (Table 2) and visualization (Figure 3), it can be concluded that the first PM was the same for both groups and both stances. Figure 2c shows the overall relative variance of the first PM. Examining further, the role of the first PM increased after the fatigue sessions significantly (p = .0137) in the case of the control group (Figure 2d). On the contrary, the variances stagnated or even decreased in the skater group, and the signed-rank test did not show any significant changes (Figure 2d).

The relation between the two previously described methods was examined by linear correlation analysis. During the correlation analysis, the relative variance of PM1, the damping ratio, and the directional ratio were considered. The results of the Spearman correlation test are summarized in Table 3. The correlation analysis showed no correlation at a 95% confidence level between the platform’s movement and the relative variance of PM1 for the skater group. However, for the control group, a moderate (rPM1-D = −.500) to strong (rPM1-D = −.696) correlation was observed between the damping ratio and the relative variance of PM1 for the right and left single-leg stances, respectively (Table 3).

Table 3

Results of the Spearman Correlation Test

Right legLeft leg
CorrelationSkaterControlSkaterControl
rVAR1-D.049−.500*.016−.696*
rVAR1-R.222.223.081.261
DR.057.202.237.015

Note. rVAR1 = relative variance of the first principal movement. D = the damping ratio; R = directional ratio.

*The correlation was significant at the .05 level.

Discussion

Balancing performance can be affected by regular and high-level athletic training, which has not been fully explored in synchronized ice skaters. Moreover, several studies investigated the dynamic balancing performance with unilateral perturbation and PCA separately (Noé et al., 2017; Petró et al., 2017; Tanabe et al., 2014). The main goal of the present research was to analyze the assessment of the dynamic balancing performance of synchronized ice skaters compared with an age-matched control group. Therefore, the primary aim was to characterize the dynamic balancing performance with unilateral sudden perturbation tests (using the PosturoMed device). Between the three dynamic stability measurement periods, sport-specific fatigue sessions were inserted. During the data processing, the oscillatory platform’s displacement was used to quantify the balancing performance of the participants (Petró et al., 2018). As a secondary aim, PCA analysis was used to identify the PMs of the balancing strategies and compare the compensatory movement between the groups. Finally, the correlation of the two applied methods (characterization of the platform’s trajectory and PCA) was also explored via Spearman’s correlation analysis.

The result of the analysis of PosturoMed parameters showed that at the initial stage (pretest measurements), there was no significant difference between the control and the skater group. We hypothesized that ice skaters will perform the postfatigue balance tests more effectively as compared with the age-matched controls. However, contrary to our hypothesis, the skater group was significantly less effective in the easiest condition, that is, in bipedal stance (Table 1). The result of the within-subject study design is in line with the findings of Alpini’s research (Alpini et al., 2008). Examining the static postural control of synchronized skaters, they found that the postural pattern of skaters was characterized by lower general stability under less challenging conditions (i.e., standing on a rigid surface) in contrast with a control group. However, in more challenging conditions, the skater group showed significantly better performance. The observation was explained by the fact that skaters adapt more easily to challenging conditions as they are also exposed to do that during on-ice workouts. Considering that skaters are not directly connected to the surface during practice, they may rely more on their visual and vestibular systems as compared with the information from the somatosensory system (Alpini et al., 2008). This may support our findings so that ice skaters relied more on their somatosensory feedback only during the more challenging task, which resulted in more effective balancing performance during unilateral (i.e., nonpreferred leg) but not during bipedal stance as compared to controls.

Applying PCA on three-dimensional position data determined the PMs. The most important PCs can be regarded as the dominant synergies of the complex motion (Federolf et al., 2013, 2015). PCA was performed on single-leg measurements separately per stance and group. The most prominent PM (PM1), which was the same for both groups, had a significantly higher relative variance than the other, compensatory PMs (Figure 2b). Comparing rVAR1 in pretest with the posttest sessions, the role of the PM1 increased after the fatigue sessions significantly (p = .0137) in the control group (Figure 2d). Regarding the compensatory PMs (PM2–PM5), the skater group often used the upper body and arms to fulfill the balancing test (Table 2 and Figure 3). On the other hand, the control group applied movements with larger amplitudes with their torso and the elevated legs instead of compensating with the arms (Table 2 and Figure 3). Considering the success rate of the perturbation tests, skaters were more successful in most of the cases. Hence, their coordination seems to be more effective than the control group. A significant correlation between the PosturoMed parameters and the relative variance of PM1 (rVAR1 vs. D and rVAR1 vs. R) could only be observed in the control group (left leg: rPM1-D = −.696 and right leg: rPM1-D = −.500; Table 3). The negative correlation coefficient suggests that an increase in the relative variance of PM1 (which is equivalent to a decrease in PM2–PM5) leads to a lower damping ratio. As a lower damping ratio is associated with a less effective balancing strategy, it can be said that the nonskater individuals were able to increase their balance recovery effectiveness by utilizing more compensatory movements (Giboin et al., 2015; Kiss, 2011; Petró et al., 2018). On the other hand, the utilization of compensatory movements was uncorrelated with the balancing effectiveness or even the platform trajectory in the skater group (Table 3). This suggests that skaters may adopt compensatory strategies as required when performing a difficult balancing task. At the same time, these additional movements could be restricted when they are not strictly needed to optimize the appearance of their movements. Although we did not measure muscle activation patterns via electromyography in the present study, another possible explanation for the higher-order PM components could be the different neuromuscular control. Apart from the time- and cost-consuming electromyography measurement, Functional Movement Screen tests are the most widespread movement screen method that could be suitable for examining advanced neuromuscular control. According to Ross et al. (2018), it is possible to differentiate the movement patterns on the basis of athlete’s skill level via the PCA analysis, which means that the higher-order PMs could be so unique that they can accurately classify the athlete’s skill level (i.e., elite or novice). Hence, this analysis also offers an applicable method to identify different musculoskeletal control and potentially be utilized for training and rehabilitation.

The limitation of this study is that only the first PM’s correlation was examined with the PosturoMed variables. In the future, a more detailed analysis of the compensatory movement (i.e., linear correlation with the platform parameters) would be beneficial. Moreover, the main limitation of the PCA calculation was that the results could not be separated by the preferred and the nonpreferred leg. The analysis would be more profound if the PCA could be evaluated by preferred and nonpreferred stances. Even though numerical values quantify the PMs, the detailed description of the movements was only possible based on 2D or 3D visualization. These visualizations contain distinct “frozen” positions laced together, which might bring some subjectivity into the description. In general, there are only a few periodic movements among the PMs (PM1 is seemingly periodic), and there might be some simpler two-phase movements. As a further perspective of the presented method would be the detailed investigation of the PMs’ time trial.

Conclusion

This study aimed to analyze the dynamic balancing performance by assessing the PMs performed during the sudden provocation tests and evaluating the parameters that characterize platform motion. Contrary to our hypothesis, the skater group was significantly less effective in the easiest condition, that is, in bipedal stance, detected by results from the oscillatory platform’s motion. However, in more challenging conditions (i.e., nonpreferred leg), their acquired balancing skill is activated, and they are able to complete the task more effectively and successfully. Moreover, applying PCA, the dominant PM (PM1) and the less significant (compensatory) PMs were also identified during the balancing test. The correlation analysis between the PCA and balancing effectiveness also showed that skaters might adopt compensatory strategies as required when performing a difficult balancing task. Consequently, PM2–PM5 could also be called as a complementary movement in the case of the skater group, since these movements may be elective compensations and not necessarily instinctive compensatory movements. On the contrary, the control group showed a negative correlation between the effectiveness of balance recovery (D) and the relative variance of the first PM (rVAR1), which suggests that they can only increase their balance recovery effectiveness by utilizing more compensatory movements. Although the dominant PM was the same for both groups, the balancing strategies of the skater and nonskater groups may still be differentiated by how deliberately they use these complementary or compensatory movements.

Acknowledgments

The authors would like to thank the Hungarian National Team of Synchronized Skating for their participation and the help of Ms. Renáta Kiss and Ms. Orsolya Megyik with the data recordings. This research was funded by a grant (Grant No. OTKA K135042) from the Hungarian Scientific Research Fund of the National Research, Development, and Innovation Office (NRDI). The research reported in this paper and carried out at Budapest University of Technology and Economics has been supported by the NRDI Fund TKP2020 NC (Grant No. BME-NC) based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology, Hungary.

References

  • Alpini, D., Mattei, V., Schlecht, H., & Kohen-Raz, R. (2008). Postural control modifications induced by synchronized ice skating. Sport Sciences for Health, 3(1–2), 1117. https://doi.org/10.1007/s11332-008-0056-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boeer, J., Mueller, O., Krauss, I., Haupt, G., & Horstmann, T. (2010). Zuverlässigkeitsprüfung eines Messverfahrens zur Charakterisierung des Standverhaltens und Quantifizierung des Balancevermögens auf einer instabilen Plattform (PosturoMed). Sportverletzung Sportschaden, 24(01), 4045. https://doi.org/10.1055/s-0029-1245184

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandon, S.C.E., Graham, R.B., Almosnino, S., Sadler, E.M., Stevenson, J.M., & Deluzio, K.J. (2013). Interpreting principal components in biomechanics: Representative extremes and single component reconstruction. Journal of Electromyography and Kinesiology, 23(6), 13041310. https://doi.org/10.1016/j.jelekin.2013.09.010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chagdes, J.R., Rietdyk, S., Jeffrey, M.H., Howard, N.Z., & Raman, A. (2013). Dynamic stability of a human standing on a balance board. Journal of Biomechanics, 46(15), 25932602. https://doi.org/10.1016/j.jbiomech.2013.08.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, C.C.T., Barnes, C.M., Holton, M., Summers, H.D., & Stratton, G. (2016). A kinematic analysis of fundamental movement skills. Sport Science Review, 25(3–4), 261275. https://doi.org/10.1515/ssr-2016-0014

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowley, J.C., & Gates, D.H. (2017). Inter-joint coordination changes during and after muscle fatigue. Human Movement Science, 56, 109118. https://doi.org/10.1016/j.humov.2017.10.015

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crotts, D., Thompson, B., Nahom, M., Ryan, S., & Newton, R.A. (1996). Balance abilities of professional dancers on select balance tests. Journal of Orthopaedic and Sports Physical Therapy, 23(1), 1217. https://doi.org/10.2519/jospt.1996.23.1.12

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daffertshofer, A., Lamoth, C.J.C., Meijer, O.G., & Beek, P.J. (2004). PCA in studying coordination and variability: A tutorial. Clinical Biomechanics, 19(4), 415428. https://doi.org/10.1016/j.clinbiomech.2004.01.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daniels, K.A.J., Henderson, G., Strike, S., Cosgrave, C., Fuller, C., & Falvey, É. (2019). The use of continuous spectral analysis for the assessment of postural stability changes after sports-related concussion. Journal of Biomechanics, 97, Article 109400. https://doi.org/10.1016/j.jbiomech.2019.109400

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Federolf, P.A. (2015). A novel approach to study human posture control: “Principal movements” obtained from a principal component analysis of kinematic marker data. Journal of Biomechanics, 49(3), 364370. https://doi.org/10.1016/j.jbiomech.2015.12.030

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Federolf, P.A., Boyer, K.A., & Andriacchi, T.P. (2013). Application of principal component analysis in clinical gait research: Identification of systematic differences between healthy and medial knee-osteoarthritic gait. Journal of Biomechanics, 46(13), 21732178. https://doi.org/10.1016/j.jbiomech.2013.06.032

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giboin, L.S., Gruber, M., & Kramer, A. (2015). Task-specificity of balance training. Human Movement Science, 44, 2231. https://doi.org/10.1016/j.humov.2015.08.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodworth, A.D., & Peterka, R.J. (2010). Influence of stance width on frontal plane postural dynamics and coordination in human balance control. Journal of Neurophysiology, 104(2), 11031118. https://doi.org/10.1152/jn.00916.2009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haid, T.H., Zago, M., Promsri, A., Doix, A.C.M., & Federolf, P.A. (2019). PManalyzer: A software facilitating the study of sensorimotor control of whole-body movements. Frontiers in Neuroinformatics, 13, Article 24. https://doi.org/10.3389/fninf.2019.00024

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hugel, F., Cadopi, M., Kohler, F., & Perrin, P. (1999). Postural control of ballet dancers: A specific use of visual input for artistic purposes. International Journal of Sports Medicine, 20(2), 8692. https://doi.org/10.1055/s-2007-971098

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hutt, K., & Redding, E. (2014). The effect of an eyes-closed dance-specific training program on dynamic balance in elite pre-professional ballet dancers: A randomized controlled pilot study. Journal of Dance Medicine & Science: Official Publication of the International Association for Dance Medicine & Science, 18(1), 311. https://doi.org/10.12678/1089-313X.18.1.3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keller, M., Röttger, K., & Taube, W. (2014). Ice skating promotes postural control in children. Scandinavian Journal of Medicine and Science in Sports, 24(6), e456e461. https://doi.org/10.1111/sms.12230

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiss, B., Nagymáté, G., & Kiss, R.M. (2017). Examination of sailors’ balancing ability with means of motion analysing system. In 2017 13th IASTED International Conference on Biomedical Engineering, 195199. https://doi.org/10.2316/P.2017.852-050

    • Search Google Scholar
    • Export Citation
  • Kiss, R.M. (2011). A new parameter for characterizing balancing ability on an unstable oscillatory platform. Medical Engineering & Physics, 33(9), 11601166. https://doi.org/10.1016/j.medengphy.2011.04.017

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamoth, C.J.C., Daffertshofer, A., Huys, R., & Beek, P.J. (2009). Steady and transient coordination structures of walking and running. Human Movement Science, 28(3), 371386. https://doi.org/10.1016/j.humov.2008.10.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menshikova, G., Kovalev, A., Klimova, O., Chernorizov, A., & Leonov, S. (2014). Testing the vestibular function development in junior figure skaters using the eye tracking technique. Procedia – Social and Behavioral Sciences, 146, 252258. https://doi.org/10.1016/j.sbspro.2014.08.123

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, O., Günther, M., Krauß, I., & Horstmann, T. (2004). Physical characterization of the therapeutic device posturomed as a measuring device — Presentation of a procedure to characterize balancing ability. Biomedizinische Technik, 49(3), 5660. https://doi.org/10.1515/bmt.2004.011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagymáté, G., & Kiss, R.M. (2018). Motion capture system validation with surveying techniques. Materials Today: Proceedings, 5(13), 2650126506. https://doi.org/10.1016/j.matpr.2018.08.107

    • Search Google Scholar
    • Export Citation
  • Noé, F., García-Massó, X., & Paillard, T. (2017). Inter-joint coordination of posture on a seesaw device. Journal of Electromyography and Kinesiology, 34, 7279. https://doi.org/10.1016/j.jelekin.2017.04.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perrin, P., Deviterne, D., Hugel, F., & Perrot, C. (2002). Judo, better than dance, develops sensorimotor adaptabilities involved in balance control. Gait & Posture, 15(2), 187194. https://doi.org/10.1016/S0966-6362(01)00149-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petró, B., Nagy, J.T., & Kiss, R.M. (2018). Effectiveness and recovery action of a perturbation balance test—A comparison of single-leg and bipedal stances. Computer Methods in Biomechanics and Biomedical Engineering, 21(10), 593600. https://doi.org/10.1080/10255842.2018.1502278

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petró, B., Papachatzopoulou, A., & Kiss, R.M. (2017). Devices and tasks involved in the objective assessment of standing dynamic balancing—A systematic literature review. PLoS One, 12(9), Article e0185188. https://doi.org/10.1371/journal.pone.0185188

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ross, G.B., Dowling, B., Troje, N.F., Fischer, S.L., & Graham, R.B. (2018). Objectively differentiating movement patterns between elite and novice athletes. Medicine & Science in Sports & Exercise, 50(7), 14571464. https://doi.org/10.1249/MSS.0000000000001571

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiehlen, W., & Eberhard, P. (2014). Basic kinematics. In Applied dynamics (pp. 1165). Springer International Publishing. https://doi.org/10.1007/978-3-319-07335-4_2

    • Search Google Scholar
    • Export Citation
  • Simmons, R.W. (2005). Sensory organization determinants of postural stability in trained ballet dancers. International Journal of Neuroscience, 115(1), 8797. https://doi.org/10.1080/00207450490512678

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sirois-Leclerc, G., Remaud, A., & Bilodeau, M. (2017). Dynamic postural control and associated attentional demands in contemporary dancers versus non-dancers. PLoS One, 12(3), Article e0173795. https://doi.org/10.1371/journal.pone.0173795

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanabe, H., Fujii, K., & Kouzaki, M. (2014). Inter- and intra-lower limb joint coordination of non-expert classical ballet dancers during tiptoe standing. Human Movement Science, 34(1), 4156. https://doi.org/10.1016/j.humov.2013.12.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
  • View in gallery
    Figure 1

    —Flowchart of the measurement procedure and the provided number of trials/successful trials in each test sessions (pretest, post1-test, and post2-test).

  • View in gallery
    Figure 2

    —(a) The between-subject study showing the boxplot of the calculated PosturoMed parameters in the pretest session; (b) the cumulative variance of the first five PMs of the two measurement groups; (c) the overall relative variance of PM1 for the left and right single-leg measurements; (d) the relative variance of PM1 broken down into the three test sessions (pretest, post1-test, and post2-test). p values are related to the results of the Wilcoxson’ s signed-rank test. PM1–PM5 = first five principal movement; rVAR = relative variance; Tend = balancing time; D = damping ratio; R = directional ratio.

  • View in gallery
    Figure 3

    —Visualization of the first five principal movements. (a) Control group and (b) skater group. L = left-leg stance; R = right-leg stance; PM1–PM5 = first five principal movement.

  • Alpini, D., Mattei, V., Schlecht, H., & Kohen-Raz, R. (2008). Postural control modifications induced by synchronized ice skating. Sport Sciences for Health, 3(1–2), 1117. https://doi.org/10.1007/s11332-008-0056-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boeer, J., Mueller, O., Krauss, I., Haupt, G., & Horstmann, T. (2010). Zuverlässigkeitsprüfung eines Messverfahrens zur Charakterisierung des Standverhaltens und Quantifizierung des Balancevermögens auf einer instabilen Plattform (PosturoMed). Sportverletzung Sportschaden, 24(01), 4045. https://doi.org/10.1055/s-0029-1245184

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brandon, S.C.E., Graham, R.B., Almosnino, S., Sadler, E.M., Stevenson, J.M., & Deluzio, K.J. (2013). Interpreting principal components in biomechanics: Representative extremes and single component reconstruction. Journal of Electromyography and Kinesiology, 23(6), 13041310. https://doi.org/10.1016/j.jelekin.2013.09.010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chagdes, J.R., Rietdyk, S., Jeffrey, M.H., Howard, N.Z., & Raman, A. (2013). Dynamic stability of a human standing on a balance board. Journal of Biomechanics, 46(15), 25932602. https://doi.org/10.1016/j.jbiomech.2013.08.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, C.C.T., Barnes, C.M., Holton, M., Summers, H.D., & Stratton, G. (2016). A kinematic analysis of fundamental movement skills. Sport Science Review, 25(3–4), 261275. https://doi.org/10.1515/ssr-2016-0014

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cowley, J.C., & Gates, D.H. (2017). Inter-joint coordination changes during and after muscle fatigue. Human Movement Science, 56, 109118. https://doi.org/10.1016/j.humov.2017.10.015

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crotts, D., Thompson, B., Nahom, M., Ryan, S., & Newton, R.A. (1996). Balance abilities of professional dancers on select balance tests. Journal of Orthopaedic and Sports Physical Therapy, 23(1), 1217. https://doi.org/10.2519/jospt.1996.23.1.12

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daffertshofer, A., Lamoth, C.J.C., Meijer, O.G., & Beek, P.J. (2004). PCA in studying coordination and variability: A tutorial. Clinical Biomechanics, 19(4), 415428. https://doi.org/10.1016/j.clinbiomech.2004.01.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daniels, K.A.J., Henderson, G., Strike, S., Cosgrave, C., Fuller, C., & Falvey, É. (2019). The use of continuous spectral analysis for the assessment of postural stability changes after sports-related concussion. Journal of Biomechanics, 97, Article 109400. https://doi.org/10.1016/j.jbiomech.2019.109400

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Federolf, P.A. (2015). A novel approach to study human posture control: “Principal movements” obtained from a principal component analysis of kinematic marker data. Journal of Biomechanics, 49(3), 364370. https://doi.org/10.1016/j.jbiomech.2015.12.030

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Federolf, P.A., Boyer, K.A., & Andriacchi, T.P. (2013). Application of principal component analysis in clinical gait research: Identification of systematic differences between healthy and medial knee-osteoarthritic gait. Journal of Biomechanics, 46(13), 21732178. https://doi.org/10.1016/j.jbiomech.2013.06.032

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giboin, L.S., Gruber, M., & Kramer, A. (2015). Task-specificity of balance training. Human Movement Science, 44, 2231. https://doi.org/10.1016/j.humov.2015.08.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goodworth, A.D., & Peterka, R.J. (2010). Influence of stance width on frontal plane postural dynamics and coordination in human balance control. Journal of Neurophysiology, 104(2), 11031118. https://doi.org/10.1152/jn.00916.2009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haid, T.H., Zago, M., Promsri, A., Doix, A.C.M., & Federolf, P.A. (2019). PManalyzer: A software facilitating the study of sensorimotor control of whole-body movements. Frontiers in Neuroinformatics, 13, Article 24. https://doi.org/10.3389/fninf.2019.00024

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hugel, F., Cadopi, M., Kohler, F., & Perrin, P. (1999). Postural control of ballet dancers: A specific use of visual input for artistic purposes. International Journal of Sports Medicine, 20(2), 8692. https://doi.org/10.1055/s-2007-971098

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hutt, K., & Redding, E. (2014). The effect of an eyes-closed dance-specific training program on dynamic balance in elite pre-professional ballet dancers: A randomized controlled pilot study. Journal of Dance Medicine & Science: Official Publication of the International Association for Dance Medicine & Science, 18(1), 311. https://doi.org/10.12678/1089-313X.18.1.3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keller, M., Röttger, K., & Taube, W. (2014). Ice skating promotes postural control in children. Scandinavian Journal of Medicine and Science in Sports, 24(6), e456e461. https://doi.org/10.1111/sms.12230

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiss, B., Nagymáté, G., & Kiss, R.M. (2017). Examination of sailors’ balancing ability with means of motion analysing system. In 2017 13th IASTED International Conference on Biomedical Engineering, 195199. https://doi.org/10.2316/P.2017.852-050

    • Search Google Scholar
    • Export Citation
  • Kiss, R.M. (2011). A new parameter for characterizing balancing ability on an unstable oscillatory platform. Medical Engineering & Physics, 33(9), 11601166. https://doi.org/10.1016/j.medengphy.2011.04.017

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamoth, C.J.C., Daffertshofer, A., Huys, R., & Beek, P.J. (2009). Steady and transient coordination structures of walking and running. Human Movement Science, 28(3), 371386. https://doi.org/10.1016/j.humov.2008.10.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menshikova, G., Kovalev, A., Klimova, O., Chernorizov, A., & Leonov, S. (2014). Testing the vestibular function development in junior figure skaters using the eye tracking technique. Procedia – Social and Behavioral Sciences, 146, 252258. https://doi.org/10.1016/j.sbspro.2014.08.123

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, O., Günther, M., Krauß, I., & Horstmann, T. (2004). Physical characterization of the therapeutic device posturomed as a measuring device — Presentation of a procedure to characterize balancing ability. Biomedizinische Technik, 49(3), 5660. https://doi.org/10.1515/bmt.2004.011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagymáté, G., & Kiss, R.M. (2018). Motion capture system validation with surveying techniques. Materials Today: Proceedings, 5(13), 2650126506. https://doi.org/10.1016/j.matpr.2018.08.107

    • Search Google Scholar
    • Export Citation
  • Noé, F., García-Massó, X., & Paillard, T. (2017). Inter-joint coordination of posture on a seesaw device. Journal of Electromyography and Kinesiology, 34, 7279. https://doi.org/10.1016/j.jelekin.2017.04.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perrin, P., Deviterne, D., Hugel, F., & Perrot, C. (2002). Judo, better than dance, develops sensorimotor adaptabilities involved in balance control. Gait & Posture, 15(2), 187194. https://doi.org/10.1016/S0966-6362(01)00149-7

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petró, B., Nagy, J.T., & Kiss, R.M. (2018). Effectiveness and recovery action of a perturbation balance test—A comparison of single-leg and bipedal stances. Computer Methods in Biomechanics and Biomedical Engineering, 21(10), 593600. https://doi.org/10.1080/10255842.2018.1502278

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petró, B., Papachatzopoulou, A., & Kiss, R.M. (2017). Devices and tasks involved in the objective assessment of standing dynamic balancing—A systematic literature review. PLoS One, 12(9), Article e0185188. https://doi.org/10.1371/journal.pone.0185188

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ross, G.B., Dowling, B., Troje, N.F., Fischer, S.L., & Graham, R.B. (2018). Objectively differentiating movement patterns between elite and novice athletes. Medicine & Science in Sports & Exercise, 50(7), 14571464. https://doi.org/10.1249/MSS.0000000000001571

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schiehlen, W., & Eberhard, P. (2014). Basic kinematics. In Applied dynamics (pp. 1165). Springer International Publishing. https://doi.org/10.1007/978-3-319-07335-4_2

    • Search Google Scholar
    • Export Citation
  • Simmons, R.W. (2005). Sensory organization determinants of postural stability in trained ballet dancers. International Journal of Neuroscience, 115(1), 8797. https://doi.org/10.1080/00207450490512678

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sirois-Leclerc, G., Remaud, A., & Bilodeau, M. (2017). Dynamic postural control and associated attentional demands in contemporary dancers versus non-dancers. PLoS One, 12(3), Article e0173795. https://doi.org/10.1371/journal.pone.0173795

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanabe, H., Fujii, K., & Kouzaki, M. (2014). Inter- and intra-lower limb joint coordination of non-expert classical ballet dancers during tiptoe standing. Human Movement Science, 34(1), 4156. https://doi.org/10.1016/j.humov.2013.12.003

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 284 284 115
PDF Downloads 130 130 47