Literature DB >> 29238821

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

Mickaël Begon1,2, Michael Skipper Andersen3, Raphaël Dumas4.   

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).

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Year:  2018        PMID: 29238821     DOI: 10.1115/1.4038741

Source DB:  PubMed          Journal:  J Biomech Eng        ISSN: 0148-0731            Impact factor:   2.097


  10 in total

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

Authors:  Mahdi Zabat; Amina Ababou; Noureddine Ababou; Raphaël Dumas
Journal:  Med Biol Eng Comput       Date:  2019-08-30       Impact factor: 2.602

2.  Manual wheelchair biomechanics while overcoming various environmental barriers: A systematic review.

Authors:  Théo Rouvier; Aude Louessard; Emeline Simonetti; Samuel Hybois; Joseph Bascou; Charles Pontonnier; Hélène Pillet; Christophe Sauret
Journal:  PLoS One       Date:  2022-06-23       Impact factor: 3.752

3.  Reliability Study of Inertial Sensors LIS2DH12 Compared to ActiGraph GT9X: Based on Free Code.

Authors:  Jaime Martín-Martín; Ariadna Jiménez-Partinen; Irene De-Torres; Adrian Escriche-Escuder; Manuel González-Sánchez; Antonio Muro-Culebras; Cristina Roldán-Jiménez; María Ruiz-Muñoz; Fermín Mayoral-Cleries; Attila Biró; Wen Tang; Borjanka Nikolova; Alfredo Salvatore; Antonio I Cuesta-Vargas
Journal:  J Pers Med       Date:  2022-05-05

4.  Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises.

Authors:  Jessica Colombel; Vincent Bonnet; David Daney; Raphael Dumas; Antoine Seilles; François Charpillet
Journal:  Sensors (Basel)       Date:  2020-05-17       Impact factor: 3.576

Review 5.  A Systematic Review of Diagnostic Accuracy and Clinical Applications of Wearable Movement Sensors for Knee Joint Rehabilitation.

Authors:  Robert Prill; Marina Walter; Aleksandra Królikowska; Roland Becker
Journal:  Sensors (Basel)       Date:  2021-12-09       Impact factor: 3.576

Review 6.  A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport.

Authors:  Cortney Armitano-Lago; Dominic Willoughby; Adam W Kiefer
Journal:  Front Sports Act Living       Date:  2022-01-25

7.  Accuracy of a markerless motion capture system in estimating upper extremity kinematics during boxing.

Authors:  Bhrigu K Lahkar; Antoine Muller; Raphaël Dumas; Lionel Reveret; Thomas Robert
Journal:  Front Sports Act Living       Date:  2022-07-25

8.  Three-Dimensional Quantitative Evaluation of the Scapular Skin Marker Movements in the Upright Posture.

Authors:  Yuki Yoshida; Noboru Matsumura; Yoshitake Yamada; Minoru Yamada; Yoichi Yokoyama; Azusa Miyamoto; Masaya Nakamura; Takeo Nagura; Masahiro Jinzaki
Journal:  Sensors (Basel)       Date:  2022-08-29       Impact factor: 3.847

9.  Age-related differences of inter-joint coordination in elderly during squat jumping.

Authors:  Sébastien Argaud; Benoit Pairot de Fontenay; Yoann Blache; Karine Monteil
Journal:  PLoS One       Date:  2019-09-09       Impact factor: 3.240

10.  Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping-Are Bone Geometry Predictors Helpful?

Authors:  Joris De Roeck; Kate Duquesne; Jan Van Houcke; Emmanuel A Audenaert
Journal:  Front Bioeng Biotechnol       Date:  2021-07-12
  10 in total

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