Letter to the editor regarding “The assessment of center of mass and center of pressure during quiet stance: Current applications and future directions”

Federolf, Peter; Angulo-Barroso, Rosa; Busquets, Albert; Ferrer-Uris, Blai; Gløersen, Øyvind; Mohr, Maurice; Reilly, David Ó’; Promsri, Arunee; Andel, Steven van; Wachholz, Felix; Werner, Inge; Zago, Matteo

doi:10.1016/j.jbiomech.2021.110729

Cited by 6 publications

(9 citation statements)

References 26 publications

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“…For example, sex differences in kinematic gait characteristics exist in individuals with several types of pathologies, e.g., osteoarthritis [ 18 , 19 ], iliotibial band syndrome [ 20 ], and Down’s syndrome [ 21 ]. Hence, analyzing the stability of the main movement synergies decomposed from the whole-body walking movements may beneficially provide an understanding of the inherent neuromuscular control involved in individual gait patterns, since the neuromuscular system controls posture through muscles that generate relative movements between multiple segments of the body [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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

Promsri

2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Promsri

2022

Sensors

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“…Posturographic information obtained on this basis reflects the outcome of the ground reaction force and moment, indirectly measuring human postural control via COP-based variables, e.g., sway area, sway path length, and sway velocity [ 12 ]. However, monitoring two-dimensional COP movements alone provides insufficient information about neuromuscular control in terms of which postural muscles are involved in generating postural sway (i.e., driving COP motions) [ 13 ]. In other words, the mechanisms controlling human posture have been suggested to be examined directly via three-dimensional postural movements and muscle activations rather than indirectly via sensorimotor system-influenced variables like COP-based variables [ 13 ], since the neuromuscular system controls posture via muscles that produce relative movements across the body segments [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, monitoring two-dimensional COP movements alone provides insufficient information about neuromuscular control in terms of which postural muscles are involved in generating postural sway (i.e., driving COP motions) [ 13 ]. In other words, the mechanisms controlling human posture have been suggested to be examined directly via three-dimensional postural movements and muscle activations rather than indirectly via sensorimotor system-influenced variables like COP-based variables [ 13 ], since the neuromuscular system controls posture via muscles that produce relative movements across the body segments [ 14 ]. Hence, to shed light on the neuromuscular mechanisms underlying unipedal postural sway, examining temporal similarity (i.e., form) between electromyographic (EMG) activity and COP signals can describe an overview of EMG–COP correlation [ 15 , 16 ], revealing the relevant muscles engaged in maintaining equilibrium (i.e., generating postural sway) [ 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Modulation of Lower-Limb Muscle Activity in Maintaining Unipedal Balance According to Surface Stability, Sway Direction, and Leg Dominance

Promsri

2022

Sports

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Determining temporal similarity in shape between electromyographic (EMG) and center-of-pressure (COP) signals reflects neuromuscular control in terms of which relevant muscles are involved in maintaining balance. The current study aimed to investigate a cross-correlation between seven lower-limb EMG activities and COP displacements, simultaneously measured in 25 young adults unipedally balancing on stable and multiaxial-unstable surfaces. The effect of surface stability, sway direction, and leg dominance was then tested on two EMG–COP correlation levels: individual muscles and groups (patterns) of multi-muscles involved in postural sway, as determined by principal component analysis (PCA). The results show that two factors demonstrate their effects only at the level of individual muscles: sway direction (p ≤ 0.003) and leg dominance (p = 0.003). Specifically, the semitendinosus, tibialis anterior, peroneus longus, and soleus correlate more with the mediolateral postural sway than with the anteroposterior postural sway, except for the gastrocnemius medialis. Additionally, balancing on the non-dominant leg shows a lower correlation between the semitendinosus and postural sway than on the dominant leg. The current findings suggest that when achieving unipedal equilibrium, the postural control system may be constrained the most in the specific muscles of the least steady conditions, e.g., the frontal plane and non-preferred leg.

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“…Traditionally, researchers have taken the approach to determine measures that summarize the workings of this system by looking at single outcome variables like the Center of Mass or Center of Pressure ( Quijoux et al, 2020 ; Mehdizadeh et al, 2021 ; Richmond et al, 2021 ). However, this approach has also been criticized as such a low-dimensional variable inherently cannot contain all information available within a complex (multi-dimensional) system ( Federolf et al, 2021 ). As an alternative to this low-dimensional approach, the use of Principal Component Analysis (PCA) has been gaining traction in the study of whole-body movement control ( Troje, 2002 ; Daffertshofer et al, 2004 ; Federolf et al, 2012 ; Federolf, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

“…In gait, PCA has been frequently applied to decompose whole-body movement patterns into separate PMs (Ó′ Reilly, 2021; Promsri, 2022; Stetter et al, 2020; Zago et al, 2022). For example, in treadmill walking, it is typically seen that only a few PMs are needed to explain most of the variance in the movement ( Andel et al, 2022 ; Malloggi et al, 2021; Ó’Reilly & Federolf, 2021 ). Federolf et al (2012) determined that already 84.2% of variance could be explained by PM1, representing anterior-posterior arm and leg movement and only one other PM was required to reach more than 90% explained variance (i.e., PM2; 6.6%, representing knee flexion-extension and vertical body movement).…”

Section: Introductionmentioning

confidence: 99%

Whole-body movement analysis using principal component analysis: What is the internal consistency between outcomes originating from the same movement simultaneously recorded with different measurement devices?

Andel

Mohr

Schmidt

et al. 2022

Front. Bioeng. Biotechnol.

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A growing number of studies apply Principal Component Analysis (PCA) on whole-body kinematic data to facilitate an analysis of posture changes in human movement. An unanswered question is, how much the PCA outcomes depend on the chosen measurement device. This study aimed to assess the internal consistency of PCA outcomes from treadmill walking motion capture data simultaneously collected through laboratory-grade optical motion capture and field-suitable inertial-based motion tracking. Data was simultaneously collected using VICON (whole-body plug-in gait marker positions) and Xsens (body segment positions) from 20 participants during 2-min treadmill walking. Using PCA, Principal Movements (PMs) were determined using two commonly used practices: on an individual and a grouped basis. For both, correlation matrices were used to determine internal consistency between outcomes from either measurement system for each PM. Both individual and grouped approach showed excellent internal consistency between outcomes from the two systems among the lower order PMs. For the individual analysis, high correlations were only found along the diagonal of the correlation matrix while the grouped analysis also showed high off-diagonal correlations. These results have important implications for future application of PCA in terms of the independence of the resulting PM data, the way group-differences are expressed in higher-order PMs and the interpretation of movement complexity. Concluding, while PCA-outcomes from the two systems start to deviate in the higher order PMs, excellent internal consistency was found in the lower order PMs which already represent about 98% of the variance in the dataset.

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Letter to the editor regarding “The assessment of center of mass and center of pressure during quiet stance: Current applications and future directions”

Cited by 6 publications

References 26 publications

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

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

Modulation of Lower-Limb Muscle Activity in Maintaining Unipedal Balance According to Surface Stability, Sway Direction, and Leg Dominance

Whole-body movement analysis using principal component analysis: What is the internal consistency between outcomes originating from the same movement simultaneously recorded with different measurement devices?

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