Comparison between Helical Axis and SARA Approaches for the Estimation of Functional Joint Axes on Multi-Body Modeling Data

Carlo De Benedictis

doi:10.3390/app12031274

Abstract

Functional methods usually allow for a flexible and accurate representation of joint kinematics and are increasingly implemented both for clinical and biomechanics research purposes. This paper presents a quantitative comparison between two widely adopted methods for functional axis estimation, that is, the helical axis theory and the symmetrical axis of rotation approach (SARA). To this purpose, a multi-body model was developed to simulate the lower limb of a subject. This model was designed to reproduce different motion patterns, that is, by selecting the active degrees of freedom of the simulated ankle joint. Thanks to virtual markers attached to each segment, the multi-body model was used to generate simulated motion capture data that were then analyzed by instantaneous helical axes and SARA algorithms. To achieve a synthetic representation of joint kinematics, a mean helical axis and an average SARA functional axis were estimated, along with dispersion parameters and rms distance data that were used to quantitatively assess the performance of each method. The sensitivity of each algorithm to different combinations of range and speed of motion, scattering of marker clusters, sampling rate, and additive noise on markers’ trajectories, was finally evaluated.

Highlights

Human motion analysis is relevant nowadays both for clinical purposes and for biomechanics research
For each set of simulations, the angular and linear dispersion parameters, as well as the rms distance values between the exact and linear dispersion parameters, as well as the rms distance values between the exact and and estimated axis of rotation (AoR), were calculated. This process was repeated for each algorithm tested, estimated AoRs, were calculated. This process was repeated for each algorithm tested, that that is, the instantaneous helical axis (IHA), with three different weighting factors (Table 2), and symmetrical axis of rotation approach (SARA)
As far as the rms distance is concerned, the SARA outperforms the helical axis for all the combinations tested in configuration A, whereas the helical axis algorithm shows better estimation accuracy for four out of nine trials performed in configuration D

Summary

Introduction

Human motion analysis is relevant nowadays both for clinical purposes and for biomechanics research. Different systems are available today to perform the acquisition of the human movement, such as inertial measurement units whose applications can be increasingly observed in the recent literature [1], most of the analyses are still conducted, especially at clinical level, through motion capture (stereo-photogrammetry) systems [2]. These systems typically involve passive reflective markers that are fixed on the surface of body segments (such as the trunk or a limb) and that are tracked by multiple cameras in a controlled environment. Commercial motion capture systems can already implement the most accepted and widely recognized marker positioning protocols and related biomechanical modeling [5,6,7,8], there is still interest in the development of new techniques and models for the investigation of human motion, especially when subject-specific analyses are required

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Discussion

Conclusion