Dataset Information

An analysis of the foot in turnout using a dance specific 3D multi-segment foot model.

ABSTRACT: Introduction:Recent three-dimensional (3D) kinematic research has revealed foot abduction is the strongest predictor of standing functional and forced turnout postures. However, it is still unknown how the internal foot joints enable a large degree of foot abduction in turnout. The primary purpose of this study was to use a dance specific multi-segment foot model to determine the lower leg and foot contributions to turnout that female university-level ballets use to accentuate their turnout. Methods:Eighteen female dance students (mean age, 18.8?±?1.6?years) volunteered for this study. Retro-reflective markers were attached to the dancers' dominant foot. Each dancer performed three repetitions of functional turnout, forced turnout and ten consecutive sautés in first position. Repeated measures ANOVA with Bonferroni adjustments for the multiple comparisons were used to determine the kinematic adjustments, hindfoot eversion, midfoot and forefoot abduction, navicular drop (i.e. lowering of the medial longitudinal arch) and first metatarsophalangeal joint abduction between natural double leg up-right posture and the first position conditions. Results:Hindfoot eversion (4.6°, p?

SUBMITTER: Carter SL

PROVIDER: S-EPMC6360724 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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An analysis of the foot in turnout using a dance specific 3D multi-segment foot model.

Carter Sarah L SL Bryant Alan R AR Hopper Luke S LS

Journal of foot and ankle research 20190204

<h4>Introduction</h4>Recent three-dimensional (3D) kinematic research has revealed foot abduction is the strongest predictor of standing functional and forced turnout postures. However, it is still unknown how the internal foot joints enable a large degree of foot abduction in turnout. The primary purpose of this study was to use a dance specific multi-segment foot model to determine the lower leg and foot contributions to turnout that female university-level ballets use to accentuate their turn ...[more]

PMID: 30740146

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Project description:BackgroundFoot and ankle joint kinematics are measured during clinical gait analyses with marker-based multi-segment foot models. To improve on existing models, measurement errors due to soft tissue artifacts (STAs) and marker misplacements should be reduced. Therefore, the aim of this study is to define a clinically informed, universally applicable multi-segment foot model, which is developed to minimize these measurement errors.MethodsThe Amsterdam foot model (AFM) is a follow-up of existing multi-segment foot models. It was developed by consulting a clinical expert panel and optimizing marker locations and segment definitions to minimize measurement errors. Evaluation of the model was performed in three steps. First, kinematic errors due to STAs were evaluated and compared to two frequently used foot models, i.e. the Oxford and Rizzoli foot models (OFM, RFM). Previously collected computed tomography data was used of 15 asymptomatic feet with markers attached, to determine the joint angles with and without STAs taken into account. Second, the sensitivity to marker misplacements was determined for AFM and compared to OFM and RFM using static standing trials of 19 asymptomatic subjects in which each marker was virtually replaced in multiple directions. Third, a preliminary inter- and intra-tester repeatability analysis was performed by acquiring 3D gait analysis data of 15 healthy subjects, who were equipped by two testers for two sessions. Repeatability of all kinematic parameters was assessed through analysis of the standard deviation (σ) and standard error of measurement (SEM).ResultsThe AFM was defined and all calculation methods were provided. Errors in joint angles due to STAs were in general similar or smaller in AFM (≤2.9°) compared to OFM (≤4.0°) and RFM (≤6.7°). AFM was also more robust to marker misplacement than OFM and RFM, as a large sensitivity of kinematic parameters to marker misplacement (i.e. > 1.0°/mm) was found only two times for AFM as opposed to six times for OFM and five times for RFM. The average intra-tester repeatability of AFM angles was σ:2.2[0.9°], SEM:3.3 ± 0.9° and the inter-tester repeatability was σ:3.1[2.1°], SEM:5.2 ± 2.3°.ConclusionsMeasurement errors of AFM are smaller compared to two widely-used multi-segment foot models. This qualifies AFM as a follow-up to existing foot models, which should be evaluated further in a range of clinical application areas.

Project description:Kinetic multi-segment foot models have been proposed to evaluate the forces and moments generated in the foot during walking based on inverse dynamics calculations. However, these models did not consider the plantar aponeurosis (PA) despite its potential importance in generation of the ground reaction forces and storage and release of mechanical energy. This study aimed to develop a novel multi-segment foot model incorporating the PA to better elucidate foot kinetics. The foot model comprised three segments: the phalanx, forefoot, and hindfoot. The PA was modeled using five linear springs connecting the origins and the insertions via intermediate points. To demonstrate the efficacy of the foot model, an inverse dynamic analysis of human gait was performed and how the inclusion of the PA model altered the estimated joint moments was examined. Ten healthy men walked along a walkway with two force plates placed in series close together. The attempts in which the participant placed his fore- and hindfoot on the front and rear force plates, respectively, were selected for inverse dynamic analysis. The stiffness and the natural length of each PA spring remain largely uncertain. Therefore, a sensitivity analysis was conducted to evaluate how the estimated joint moments were altered by the changes in the two parameters within a range reported by previous studies. The present model incorporating the PA predicted that 13%-45% of plantarflexion in the metatarsophalangeal (MTP) joint and 8%-29% of plantarflexion in the midtarsal joints were generated by the PA at the time of push-off during walking. The midtarsal joint generated positive work, whereas the MTP joint generated negative work in the late stance phase. The positive and negative work done by the two joints decreased, indicating that the PA contributed towards transfer of the energy absorbed at the MTP joint to generate positive work at the midtarsal joint during walking. Although validation is limited due to the difficulty associated with direct measurement of the PA force in vivo, the proposed novel foot model may serve as a useful tool to clarify the function and mechanical effects of the PA and the foot during dynamic movements.

Project description:BackgroundMulti-segment foot models (MFMs) for assessing three-dimensional segmental foot motions are calculated via various analytical methods. Although validation studies have already been conducted, we cannot compare their results because the experimental environments in previous studies were different from each other. This study aims to compare the kinematics, repeatability, and reproducibility of five MFMs in the same experimental conditions.MethodsEleven healthy males with a mean age of 26.5 years participated in this study. We created a merged 29-marker set including five MFMs: Oxford (OFM), modified Rizzoli (mRFM), DuPont (DFM), Milwaukee (MiFM), and modified Shriners Hospital for Children Greenville (mSHCG). Two operators applied the merged model to participants twice, and then we analysed two relative angles of three segments: shank-hindfoot (HF) and hindfoot-forefoot (FF). Coefficients of multiple correlation (CMC) and mean standard errors were used to assess repeatability and reproducibility, and statistical parametric mapping (SPM) of the t-value was employed to compare kinematics.ResultsHF varus/valgus of the MiFM and mSHCG models, which rotated the segment according to radiographic or goniometric measurements during the reference frame construction, were significantly more repeatable and reproducible, compared to other models. They showed significantly more dorsiflexed HF and plantarflexed FF due to their static offset angles. DFM and mSHCG showed a greater range of motion (ROM), and some models had significantly different FF points of peak angle.ConclusionsUnder the same conditions, rotating the segment according to the appropriate offset angle obtained from radiographic or goniometric measurement increased reliability, but all MFMs had clinically acceptable reliability compared to previous studies. Moreover, in some models, especially HF varus/valgus, there were differences in ROM and points of peak angle even with no statistical difference in SPM curves. Therefore, based on the results of this study, clinicians and researchers involved in the evaluation of foot and ankle dysfunction need an understanding of the specific features of each MFM to make accurate decisions.

Project description:PurposeWe present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1+-inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone.MethodsSimulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well.ResultsIn comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs.ConclusionThe proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.

Dataset Information

An analysis of the foot in turnout using a dance specific 3D multi-segment foot model.

Publications

An analysis of the foot in turnout using a dance specific 3D multi-segment foot model.

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