Elsevier

Journal of Biomechanics

Volume 48, Issue 10, 16 July 2015, Pages 1965-1971
Journal of Biomechanics

Soft tissue artifact distribution on lower limbs during treadmill gait: Influence of skin markers' location on cluster design

https://doi.org/10.1016/j.jbiomech.2015.04.007Get rights and content

Abstract

Segment poses and joint kinematics estimated from skin markers are highly affected by soft tissue artifact (STA) and its rigid motion component (STARM). While four marker-clusters could decrease the STA non-rigid motion during gait activity, other data, such as marker location or STARM patterns, would be crucial to compensate for STA in clinical gait analysis. The present study proposed 1) to devise a comprehensive average map illustrating the spatial distribution of STA for the lower limb during treadmill gait and 2) to analyze STARM from four marker-clusters assigned to areas extracted from spatial distribution. All experiments were realized using a stereophotogrammetric system to track the skin markers and a bi-plane fluoroscopic system to track the knee prosthesis. Computation of the spatial distribution of STA was realized on 19 subjects using 80 markers apposed on the lower limb. Three different areas were extracted from the distribution map of the thigh. The marker displacement reached a maximum of 24.9 mm and 15.3 mm in the proximal areas of thigh and shank, respectively. STARM was larger on thigh than the shank with RMS error in cluster orientations between 1.2° and 8.1°. The translation RMS errors were also large (3.0 mm to 16.2 mm). No marker-cluster correctly compensated for STARM. However, the coefficient of multiple correlations exhibited excellent scores between skin and bone kinematics, as well as for STARM between subjects. These correlations highlight dependencies between STARM and the kinematic components. This study provides new insights for modeling STARM for gait activity.

Introduction

Soft tissue artifact (STA) is a complex effect of skin viscoelasticity, muscle contractions, and wobbling masses that influences the analysis of the underlying bone movement. Over the last decade, STA was reported in the literature in terms of errors in marker location, segment pose, and joint kinematics; however, no consensus regarding its behavior has been reached due to the disparity of the results (Peters et al., 2010). Recently, based on experimental and simulation data, the mathematical representation of STA affecting a single marker belonging to a cluster was largely improved (Andersen et al., 2012, Ball and Pierrynowski, 1998, Camomilla et al., 2013, Dumas and Cheze, 2009, Dumas et al., 2014, Grimpampi et al., 2014, de Rosario et al., 2012). STA can be summarized as a translation and rotation of the cluster with respect to the bone (STA rigid motion: STARM) and a change of the cluster in terms of scaling, homotethy, deformation, and stretch (STA non-rigid motion: STANRM). Several studies assessed STA on the lower limb during gait activity (Akbarshahi et al., 2010, Barré et al., 2013, Benoit et al., 2006, Cappozzo et al., 1996, Holden et al., 1997, Houck et al., 2004, Manal et al., 2000, Reinschmidt et al., 1997, Tsai et al., 2009). Several of the studies suggested the importance of STARM compared to STANRM (Grimpampi et al., 2014, Manal et al., 2003, Reinschmidt et al., 1997), while others quantified it (Andersen et al., 2012, Barré et al., 2013). Using a large number of markers on each segment, it was recently shown that some kinematic components of STARM for thigh and shank had similar patterns between subjects during a treadmill gait task (Barré et al., 2013). Such information would be valuable for developing a compensation method. A large number of markers spread on a whole segment can provide a better estimation of STARM (Monnet et al., 2012, Barré et al., 2013). However, this property will not be usable in laboratory practice, where generally a minimum of four markers is used to extract this component (Cappozzo et al., 1997). Several studies have already illustrated the STARM patterns between subjects using four marker-clusters (Benoit et al., 2006, Benoit et al., 2007) but without quantification. When using a small number of markers, a supplementary question arises regarding their location on the limb, as it influences the estimation of STARM (Cereatti et al., 2006). Several studies have analyzed the STA effect on individual marker displacement during gait (Akbarshahi et al., 2010, Tsai et al., 2009); however, the number of markers in their studies was limited. Instead, the analysis on the whole limb, similar to Stagni et al. (2005), would be more beneficial to determine regions differently affected by STA. Several four marker-clusters could be then selected in these regions and compared in terms of STARM waveforms.

The goal of this study was twofold. First, we aimed to determine and compare the spatial distribution of STA between subjects in terms of individual marker placement for thigh and shank during gait task. For this purpose, it was necessary to devise a normalized map representing the STA distribution for each subject. Secondly, based on these distributions, multiple four-marker clusters were compared to determine the similarities between inter-subject STARM waveforms.

Section snippets

Experimental design

A bi-plane fluoroscopic system (2 Philips BV Pulsera 300, 30 Hz, 60 kV and 5.95 mA), a motion capture system (7 MX3+ cameras, Vicon, 240 Hz), an X-ray detector (Monitor 4EC, S.E. International, Inc., USA), and a treadmill (Cadence M5, Weslo, USA) were combined to assess STA during the treadmill gait (Fig. 1) (Barré et al., 2013).

A total of 19 subjects (11♀ and 8♂) with knee F.I.R.S.T prosthesis (Symbios, CH) were evaluated with this measurement system. The mean and standard deviation of age,

Results

The STA distribution maps for the thigh and shank (Fig. 4) were computed for the 19 subjects. The average height and width for the maps were (440 mm, 500 mm) and (443 mm, 370 mm) for thigh and shank, respectively. For both segments, the coordinates (0,0) correspond to AF's origin. The white area represented the untracked area, and the negative horizontal coordinates represented the lateral side of the segment. The individual marker displacement was between 4.4 and 24.9 mm with maxima in the proximal

Discussion

In the current study, we analyzed the effect of the rigid motion component of soft tissue artifact (STARM) during treadmill gait on the knee kinematics estimated from multiple clusters of four markers selected among 80 reflective markers placed on the lower limb. Considering differences in lower-limb sizes and marker locations, a new method was proposed to normalize the STA distribution and to compare cluster areas between subjects. Kinematics obtained with these clusters were compared to the

Conflict of interest statement

The authors have no conflicts of interest to declare.

Acknowledgments

This study was supported partly by the Swiss National Science Foundation (SNSF Grants 205320-137940 and 205321-120136) and was partly financed by the Inter-institutional Centre of Translational Biomechanics (CBT) Foundation. The authors would like to acknowledge Kareem Boulos for patient recruitment and Angelina Poloni for administrative assistance.

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1

Present address: EPFL STI CBT LMAM-ELH 134, Station 11, CH-1015 Lausanne (Switzerland).

2

Present address: CHUV–Site Hôpital Orthopédique, 4, Avenue Pierre Decker, CH-1011 Lausanne (Switzerland).

3

Present address: Institut de Radiologie, Clinique Bois-Cerf, CH-1006 Lausanne (Switzerland).

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