Elsevier

Journal of Biomechanics

Volume 49, Issue 3, 8 February 2016, Pages 364-370
Journal of Biomechanics

A novel approach to study human posture control: “Principal movements” obtained from a principal component analysis of kinematic marker data

https://doi.org/10.1016/j.jbiomech.2015.12.030Get rights and content

Abstract

Human upright posture is maintained by postural movements, which can be quantified by “principal movements” (PMs) obtained through a principal component analysis (PCA) of kinematic marker data. The current study expands the concept of “principal movements” in analogy to Newton׳s mechanics by defining “principal position” (PP), “principal velocity” (PV), and “principal acceleration” (PA) and demonstrates that a linear combination of PPs and PAs determines the center of pressure (COP) variance in upright standing. Twenty-one subjects equipped with 27-markers distributed over all body segments stood on a force plate while their postural movements were recorded using a standard motion tracking system. A PCA calculated on normalized and weighted posture vectors yielded the PPs and their time derivatives, the PVs and PAs. COP variance explained by the PPs and PAs was obtained through a regression analysis. The first 15 PMs quantified 99.3% of the postural variance and explained 99.60%±0.22% (mean±SD) of the anterior–posterior and 98.82±0.74% of the lateral COP variance in the 21 subjects. Calculation of the PMs thus provides a data-driven definition of variables that simultaneously quantify the state of the postural system (PPs and PVs) and the activity of the neuro-muscular controller (PAs). Since the definition of PPs and PAs is consistent with Newton׳s mechanics, these variables facilitate studying how mechanical variables, such as the COP motion, are governed by the postural control system.

Introduction

The human body is a multi-segmental mechanical system whose inter-segment movements are generated and modified by actuators (muscles) controlled by a complex neuronal network. How this system achieves and maintains postural stability has been an important question in biomechanics and neuroscience over many decades.

The center of pressure (COP) excursion is a frequently used variable to assess balance and stability in humans. The COP offers a direct measure of mechanical stability in the sense that a COP position too close to the border of the base of support indicates an instability that must be corrected in order to prevent a fall. Furthermore, the characteristics of the COP motion provide information about the neuro-muscular control, particularly in cases of neuro-muscular deficits, for example, cerebral palsy (Donker et al., 2008, Rose et al., 2002), stroke (Corriveau et al., 2004, Roerdink et al., 2006), concussion (Cavanaugh et al., 2005, Cavanaugh et al., 2006, Rubin et al., 1995), or frailty (Lipsitz, 2002) and fall risk (Maki et al., 1994) in the elderly.

How postural movements govern the COP has been described for the inverted pendulum model (Winter et al., 1996, Winter et al., 1993). In this model the COP motion is determined by two aspects. First, the COP position depends on the position of the center of mass (CM) – if the body sways forward, then the COP will also move forward. Second, the COP depends on the acceleration of the body – when leaning forward, the neuro-muscular postural control system needs to produce a moment of force that pushes the body back into an upright position. This moment is created by muscle action moving the COP further forward. Hence, even in this simplified model a forward motion of the COP can be caused by either a forward sway or a backward acceleration of the body. In actual postural movements the COP motion is additionally influenced by other motion patterns such as hip-, knee, or upper body strategies (Hsu et al., 2007, Pinter et al., 2008), physiologic movements such as breathing (Hodges et al., 2002), and movements triggered by cognitive processes such as arousal level (Maki and McLlroy, 1996) or emotional state (Hillman et al., 2004).

The neuro-muscular control of the COP motion has been analyzed by correlating magnitudes of muscle synergies [M-modes (Krishnamoorthy et al., 2003a)] with changes in COP position. Muscle synergies are calculated by performing a principal component analysis (PCA) on normalized electromyographic (EMG) data obtained from several muscles. For voluntary postural sway, M-modes explained 71% (Klous et al., 2011) and 88% (Krishnamoorthy et al., 2003b) of COP variance, however, explained variance dropped markedly when sway frequency was increased (Danna-dos-Santos et al., 2007).

Kinematic synergies obtained from performing a PCA on, for example, joint angles (Alexandrov et al., 1998, Freitas et al., 2006, Tricon et al., 2007, Vernazza et al., 1996) or marker coordinates (Federolf et al., 2013a, Federolf et al., 2012b), were also used to study aspects of postural control. When applied to marker coordinates, the PCA transforms the complex, high-dimensional movements of all markers into a set of one-dimensional movement components. These PCA-generated movement components have been called “principal movements” (PMs) (Eskofier et al., 2013, Federolf et al., 2014, Federolf et al., 2012b, Maurer et al., 2012). To date, kinematic synergies or PMs are usually considered as theoretical constructs that relate to, but that do not directly quantify the mechanics of the postural control system.

The purposes of the current paper are to define postural PMs consistent with Newton׳s mechanics; to validate that these PMs represent the mechanics of human postural motion by testing the hypothesis that a linear combination of PMs explain the COP variance; and to outline implications of this methodologic approach for postural control research.

Section snippets

Participants

Twenty-one volunteers (11 males, 10 females, age 26.4±2.4, height 176±8 cm, weight 71±10 kg [mean±standard deviation]) with good self-reported general health and no recent injury or other condition that that could affect balance were recruited. All subjects provided written informed consent prior to participating and the study protocol was approved by the Norwegian Regional Ethical Committee.

Measurement procedures

Measurements started with the volunteers standing in front of the force plate. The subjects were

Characterization of the first 15 principal movements

The eigenvalues and an interpretation of what aspect of the whole motion each of the first 15 PMs represented is given in Table 1. Together these 15 PMs quantified 99.3% of the postural variance. For the first 4 PMs, a visual representation of the changes in posture and of the PPk and PAk time series is shown in Fig. 1.

Qualitatively, the following movement components can be distinguished (Table 1): PM1, PM2, PM3, and PM5 closely represented postural control movements that are usually described

Discussion

The most important novelty of the current paper is the formulation of kinematics in posture space, which is made possible by factoring in the relative mass that each marker represents in the normalization of the posture vectors. The resultant PMs explained COP variance with better precision (>97%) than previous methods (71–88%) (Danna-dos-Santos et al., 2007, Klous et al., 2011, Krishnamoorthy et al., 2003b). This is important for postural control research, since the PMs directly link the

Conflict of interest statement

I declare that I am not aware of any potential or actual conflicts of interest concerning the manuscript.

Acknowledgments

Vidar Jakobsen set up the measurement equipment. Heidi Kallerud and David Haakonsen conducted the measurements. Beatrix Vereijken, Espen Ihlen, Aude-Clémence Doix, and Thomas Haid reviewed and advised on various stages of the manuscript. Their contribution to this study is gratefully acknowledged. No external funding was received for this study.

References (40)

  • M. Salavati et al.

    Test–retest reliabty of center of pressure measures of postural stability during quiet standing in a group with musculoskeletal disorders consisting of low back pain, anterior cruciate ligament injury and functional ankle instability

    Gait Posture

    (2009)
  • V. Tricon et al.

    Balance control and adaptation of kinematic synergy in aging adults during forward trunk bending

    Neurosci. Lett.

    (2007)
  • S. Vernazza et al.

    Is the center of gravity controlled during upper trunk movements?

    Neurosci. Lett.

    (1996)
  • A. Alexandrov et al.

    Axial synergies during human upper trunk bending

    Exp. Brain Res.

    (1998)
  • J.T. Cavanaugh et al.

    Detecting altered postural control after cerebral concussion in athletes with normal postural stability

    Br. J. Sports Med.

    (2005)
  • J.T. Cavanaugh et al.

    Recovery of postural control after cerebral concussion: new insights using approximate entropy

    J. Athl. Train.

    (2006)
  • A. Danna-dos-Santos et al.

    Muscle modes and synergies during voluntary body sway

    Exp. Brain Res.

    (2007)
  • S. Donker et al.

    Children with cerebral palsy exhibit greater and more regular postural sway than typically developing children

    Exp. Brain Res.

    (2008)
  • B.M. Eskofier et al.

    Marker-based classification of youngelderly gait pattern differences via direct PCA feature extraction and SVMs

    Comput. Methods Biomech. Biomed. Eng.

    (2013)
  • P. Federolf et al.

    The application of principal component analysis to quantify technique in sports

    Scand. J. Med. Sci. Sports

    (2014)
  • Cited by (72)

    View all citing articles on Scopus
    View full text