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

Abstract

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).

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data

“…Joint angles of hip-and knee-joint functional movements and gait cycles were computed using inverse kinematics, also called multibody kinematic optimisation (Lu and O'Connor 1999;Begon et al 2017), with markers mentioned in Table 1. They had a weight factor of 1.…”

IMU-based sensor-to-segment multiple calibration for upper limb joint angle measurement—a proof of concept

“…Another possibility to compensate for the STA, yet more rarely applied to IMUs, is to introduce joint constraints and to perform a multibody kinematics optimization [43].…”

A screening method to analyse the sensitivity of a lower limb multibody kinematic model

“…Multibody kinematics optimisation, which consists in minimising the sum of the squared distances between measured and model-derived skin marker positions [3], was performed to compute the joint kinematics. The numerical cost was quite low, namely less than 2 s per model evaluation on a conventional computer (i.e., Intel R Core TM i7-6700/3.4GHz).…”

“…Therefore, the most influential parameters of inverse kinematics (also designated as multibody kinematics optimisation [3,21]) are not clearly identified. Moreover, detailled joint model such as parallel mechanisms, and typically subject-specific models based on medical imaging, are becoming more and more popular [5,10,11,39] while the sensitivity of the model parameters has been scarcely analysed [15,33].…”