Single-leg landing neuromechanical data following load and land height manipulations

Lower extremity sagittal kinematic and kinetic data are summarized alongside electrical muscle activities during single-leg landing trials completed in contrasting external load and landing height conditions. Nineteen subjects were analyzed during 9 landing trials in each of 6 experimental conditions computed as percentages of subject anthropometrics (bodyweight: BW and subject height: H; BW, BW+12.5%, BW+25%, and H12.5%, H25%). Twelve lower extremity variables (sagittal hip, knee, ankle angles and moments, vertical ground reaction force (GRFz), gluteus maximus, biceps femoris, vastus medials, medial gastrocnemius, and tibialis anterior muscles) were assessed using separate principal component analyses (PCA). Variable trends across conditions were summarized in “Neuromechanical synergies in single-leg landing reveal changes in movement control. Human Movement Science” (Nordin and Dufek, 2016) [1], revealing changes in landing biomechanics and movement control.


Type of data
Previously injured or individuals susceptible to injury may reveal different movement patterns following load and landing height manipulations.
Baseline comparisons may be useful for identifying atypical landing biomechanics.

Data
The six figures present patterns of change among 12 lower extremity variables collected in a single-leg landing task following external load and landing height manipulations. Ensemble time series plots and principal component analysis (PCA) results are shown for each variable. Principal component (PC) loading vectors and PC scores present magnitude and temporal changes among conditions, along with analysis of variance results (ANOVA, po 0.05).
Lower extremity single-leg landing biomechanical data were summarized among subjects for sagittal joint angles and moments (hip, knee, ankle), vertical ground reaction forces (GRFz), and muscle activation patterns (gluteus maximus, biceps femoris, vastus medialis, medial gastrocnemius, tibialis anterior). Subject-specific anthropometrics were used to calculate load and landing height conditions (bodyweight: BW, and subject height: H). Six experimental load and landing height combinations were completed: BW, BWþ 12.5%, and BWþ 25% at H12.5% and H25%. Principal component analyses (PCA) were performed for each lower extremity variable. PC loading vectors depicted movement patterns. PC scores summarized movement pattern differences among conditions.
Kinematic data were low-pass filtered (15 Hz cutoff; Matlab R2012a), ground reaction force data were low-pass filtered (50 Hz cutoff), and internal joint moments were computed using matched filter cutoffs (15 Hz). EMG data were band pass filtered (15-300 Hz cutoff), full-wave rectified, and low-pass filtered (15 Hz cutoff). We analyzed data across the landing phase (from GRFz 420 N to the time vertical center of mass velocity reached zero). We expressed joint angles in degrees, joint moments were normalized to bodyweight and subject height (BWH), and EMG data were normalized to mean dynamic baseline activity recorded in the experimental condition with the lowest task demands (BWH12.5) [2].
Prior to principal component analyses (PCA), we temporally normalized the landing phase to 101 points using cubic spline interpolation (Matlab R2012a). We then converted each time series waveform to z-scores by subtracting the subject's baseline mean and dividing by the baseline standard deviation (BWH12.5). For each of the 12 lower extremity variables, we assembled 1026 Â 101 dimension matrices (19 subjects Â 3 loads Â 2 heights Â 9 trials¼ 1026; times series length ¼ 101). Principal components (PCs) explaining greater than 90% cumulative explained variance (EV) were    extracted from the covariance matrix of each variable, and PC scores were computed for each trial and PC (Matlab R2012a). PC scorea means were aggregated for each subject from the 9 analyzed trials and used for inferential testing. Separate 3 Â 2 (load Â height) repeated measures analyses of variance (ANOVAs) were conducted for each extracted PC and used in evaluating movement pattern changes across conditions (IBM SPSS Statistics Version 20) [3,4]. We performed follow-up one-way repeated measures ANOVAs, simple main effects analysis, degree of freedom Huynh-Feldt corrections, and Bonferroni corrections during pairwise comparisons, as necessary [5].
Ensemble curves (aggregated among subjects) were plotted by condition for each lower extremity variable (Figs. 1-6, top). Extracted PCs were plotted below ensemble plots in order of descending EV. PC score means were presented beside each PC loading vector, indicating statistically significant condition differences where appropriate (α ¼ 0.05). Load and landing height PC score means were presented in each condition following significant interaction. Height PC score means were presented following a significant height main effect and load PC score means were presented following a significant load main effect. In each case, significant ANOVA results were included above PC score means, with significant pairwise comparisons highlighted (α ¼0.05). Hip angles and moments were show in Fig. 1, knee angles and moments were shown in Fig. 2, ankle angles and moments were shown in Fig. 3, GRFz and gluteus maximus were shown in Fig. 4, biceps femoris and vastus medialis were shown in Fig. 5, and medial gastrocnemius and tibialis anterior muscles were shown in Fig. 6. Significant pairwise differences and variable trends across conditions were summarized in Table 1 of Nordin and Dufek [1].

Transparency document. Supplementary material
Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.07.011.