Review Article

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

[+] Author and Article Information
Mickaël Begon

Département de Kinésiologie,
Université de Montréal,
1700 Jacques Tétreault,
Laval, QC H7N 0B6, Canada;
Centre de Recherche du Centre Hospitalier,
Universitaire Sainte-Justine,
3175 Chemin de la Côte-Sainte-Catherine,
Montréal, QC H3T 1C5, Canada
e-mail: mickael.begon@umontreal.ca

Michael Skipper Andersen

Department of Materials and Production,
Aalborg University,
Fibigerstrade 16,
Aalborg East DK-9220, Denmark
e-mail: msa@mp.aau.dk

Raphaël Dumas

Univ Lyon,
Université Claude Bernard Lyon 1, IFSTTAR,
Lyon F69622, France
e-mail: Raphael.dumas@ifsttar.fr

Manuscript received December 15, 2016; final manuscript received November 12, 2017; published online January 18, 2018. Assoc. Editor: Kenneth Fischer.

J Biomech Eng 140(3), 030801 (Jan 18, 2018) (11 pages) Paper No: BIO-16-1520; doi: 10.1115/1.4038741 History: Received December 15, 2016; Revised November 12, 2017

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

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