Analysis of Postural Control Using Principal Component Analysis: The Relevance of Postural Accelerations and of Their Frequency Dependency for Selecting the Number of Movement Components

Age and Visual Contribution Effects on Postural Control Assessed by Principal Component Analysis of Kinematic Marker Data

Postural control, the ability to control the body's position in space, is considered a critical aspect of health outcomes. This current study aimed to investigate the effects of age and visual contribution on postural control. To this end, principal component analysis (PCA) was applied to extract movement components/synergies (i.e., principal movements, PMs) from kinematic marker data of bipedal balancing on stable and unstable surfaces with eyes closed and open, pooled from 17 older adults (67.8 ± 6.6 years) and 17 young adults (26.6 ± 3.3 years), one PCA-analysis for each surface condition. Then, three PCA-based variables were computed for each PM: the relative explained variance of PM-position (PP_rVAR) and of PM-acceleration (PA_rVAR) for measuring the composition of postural movements and of postural accelerations, respectively, and the root mean square of PM-acceleration (PA_RMS) for measuring the magnitude of neuromuscular control. The results show the age and visual contribution effects observed in PM₁, resembling the anteroposterior ankle sway in both surface conditions. Specifically, only the greater PA₁_rVAR and PA₁_RMS are observed in older adults (p ≤ 0.004) and in closed-eye conditions (p < 0.001), reflecting their greater need for neuromuscular control of PM₁ than in young adults and in open-eye conditions.

Walking Stability and Risk of Falls

Walking stability is considered a necessary physical performance for preserving independence and preventing falls. The current study investigated the correlation between walking stability and two clinical markers for falling risk. Principal component analysis (PCA) was applied to extract the three-dimensional (3D) lower-limb kinematic data of 43 healthy older adults (69.8 ± 8.5 years, 36 females) into a set of principal movements (PMs), showing different movement components/synergies working together to accomplish the walking task goal. Then, the largest Lyapunov exponent (LyE) was applied to the first five PMs as a measure of stability, with the interpretation that the higher the LyE, the lower the stability of individual movement components. Next, the fall risk was determined using two functional motor tests-a Short Physical Performance Battery (SPPB) and a Gait Subscale of Performance-Oriented Mobility Assessment (POMA-G)-of which the higher the test score, the better the performance. The main results show that SPPB and POMA-G scores negatively correlate with the LyE seen in specific PMs (p ≤ 0.009), indicating that increasing walking instability increases the fall risk. The current findings suggest that inherent walking instability should be considered when assessing and training the lower limbs to reduce the risk of falling.

Distinct coordination patterns integrate exploratory head movements with whole-body movement patterns during walking

Visual guidance of gait is an important skill for everyday mobility. While this has often been studied using eye-tracking techniques, recent studies have shown that visual exploration involves more than just the eye; head movement and potentially the whole body is involved for successful visual exploration. This study aimed to assess coordinative patterns associated with head movement and it was hypothesized that these patterns would span across the body, rather than being localized. Twenty-one (after exclusions) healthy young adult volunteers followed a treadmill walking protocol designed to elicit different types of head movements (no stimuli compared to stimuli requiring horizontal, vertical, and mixed gaze shifts). Principal Component Analysis was used to establish whole-body correlated patterns of marker movement (Principal Movements; PMs) related to the activity of the head. In total 37 higher order PMs were found to be associated with head movement, two of these showed significant differences between trials associated with strong head rotations in the horizontal and sagittal plane. Both of these were associated with a whole-body pattern of activity. An analysis of the higher order components revealed that exploratory head movements are associated with distinct movement patterns, which span across the body. This shows that visual exploration can produce whole-body movement patterns that have a potentially destabilizing influence. These findings shed new light on established results in visual search research and hold relevance for fall and injury prevention.

Assessing Walking Stability Based on Whole-Body Movement Derived from a Depth-Sensing Camera

Stability during walking is considered a crucial aspect of assessing gait ability. The current study aimed to assess walking stability by applying principal component analysis (PCA) to decompose three-dimensional (3D) whole-body kinematic data of 104 healthy young adults (21.9 ± 3.5 years, 54 females) derived from a depth-sensing camera into a set of movement components/synergies called "principal movements" (PMs), forming together to achieve the task goal. The effect of sex as the focus area was tested on three PCA-based variables computed for each PM: the relative explained variance (rVAR) as a measure of the composition of movement structures; the largest Lyapunov exponent (LyE) as a measure of variability; and the number of zero-crossings (N) as a measure of the tightness of neuromuscular control. The results show that the sex effects appear in the specific PMs. Specifically, in PM₁, resembling the swing-phase movement, females have greater LyE (p = 0.013) and N (p = 0.017) values than males. Moreover, in PM₃, representing the mid-stance-phase movement, females have smaller rVAR (p = 0.020) but greater N (p = 0.008) values than males. These empirical findings suggest that the inherent sex differences in walking stability should be considered in assessing and training locomotion.

Sex Difference in Running Stability Analyzed Based on a Whole-Body Movement: A Pilot Study

A sex-specific manner in running tasks is considered a potential internal injury risk factor in runners. The current study aimed to investigate the sex differences in running stability in recreational runners during self-preferred speed treadmill running by focusing on a whole-body movement. To this end, principal component analysis (PCA) was applied to kinematic marker data of 22 runners (25.7 ± 3.3 yrs.; 12 females) for decomposing the whole-body movements of all participants into a set of principal movements (PMs), representing different movement synergies forming together to achieve the task goal. Then, the sex effects were tested on three types of PCA-based variables computed for individual PMs: the largest Lyapunov exponent (LyE) as a measure of running variability; the relative standard deviation (rSTD) as a measure of movement structures; and the root mean square (RMS) as a measure of the magnitude of neuromuscular control. The results show that the sex effects are observed in the specific PMs. Specifically, female runners have lower stability (greater LyE) in the mid-stance-phase movements (PM₄₋₅) and greater contribution and control (greater rSTD and RMS) in the swing-phase movement (PM₁) than male runners. Knowledge of an inherent sex difference in running stability may benefit sports-related injury prevention and rehabilitation.

Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach

Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.6% of the variance of the movements; (2) the number of PMs that represented 94.6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.

Principal postural acceleration and myoelectric activity: Interrelationship and relevance for characterizing neuromuscular function in postural control

One approach to investigating sensorimotor control is to assess the accelerations that produce changes in the kinematic state of the system. When assessing complex whole-body movements, structuring the multi-segmental accelerations is important. A useful structuring can be achieved through a principal component analysis (PCA) performed on segment positions followed by double-differentiation to obtain "principal accelerations" (PAs). In past research PAs have proven sensitive to altered motor control strategies, however, the interrelationship between PAs and muscle activation (surface electromyography, sEMG) have never been determined. The purpose of the current study was therefore to assess the relationship between PAs and sEMG signals recorded from muscles controlling the ankle joint during one-leg standing trials. It was hypothesized that medium correlation should be observed when accounting for neurophysiologic latencies (electro-mechanical delay). Unipedal balancing on a level-rigid ground was performed by 25 volunteers. sEMG activities were recorded from the tibialis anterior, peroneus longus, gastrocnemius medialis, and soleus muscles of the stance leg. The first eight PA-time series were determined from kinematic marker data. Then, a cross-correlation analysis was performed between sEMG and PA time series. We found that peak correlation coefficients for many participants aligned at time delays between 0.116 and 0.362 s and were typically in the range small to medium (|r| = 0.1 to 0.6). Thus, the current study confirmed a direct association between many principal accelerations PA(t) and muscle activation signals recorded from four muscles crossing the ankle joint complex. The combined analysis of PA and sEMG signals allowed exploring the neuromuscular function of each muscle in different postural movement components.

How Do Movement Patterns in Weightlifting (Clean) Change When Using Lighter or Heavier Barbell Loads?-A Comparison of Two Principal Component Analysis-Based Approaches to Studying Technique

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis (PCA). Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA (PCA^trial) analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA (PCA^posture) analyzed postural positions compiled over time, subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCA^trial scores differed in the first (p < 0.016, η_p² = 0.694) and fifth (p < 0.006, η_p² = 0.768) principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCA^posture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCA^trial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCA^posture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations.