Literature DB >> 31247556

Subject-Exoskeleton Contact Model Calibration Leads to Accurate Interaction Force Predictions.

Gil Serrancoli, Antoine Falisse, Christopher Dembia, Jonas Vantilt, Kevin Tanghe, Dirk Lefeber, Ilse Jonkers, Joris De Schutter, Friedl De Groote.   

Abstract

Knowledge of human-exoskeleton interaction forces is crucial to assess user comfort and effectiveness of the interaction. The subject-exoskeleton collaborative movement and its interaction forces can be predicted in silico using computational modeling techniques. We developed an optimal control framework that consisted of three phases. First, the foot-ground (Phase A) and the subject-exoskeleton (Phase B) contact models were calibrated using three experimental sit-to-stand trials. Then, the collaborative movement and the subject-exoskeleton interaction forces, of six different sit-to-stand trials were predicted (Phase C). The results show that the contact models were able to reproduce experimental kinematics of calibration trials (mean root mean square differences - RMSD - coordinates ≤ 1.1° and velocities ≤ 6.8°/s), ground reaction forces (mean RMSD≤ 22.9 N), as well as the interaction forces at the pelvis, thigh, and shank (mean RMSD ≤ 5.4 N). Phase C could predict the collaborative movements of prediction trials (mean RMSD coordinates ≤ 3.5° and velocities ≤ 15.0°/s), and their subject-exoskeleton interaction forces (mean RMSD ≤ 13.1° N). In conclusion, this optimal control framework could be used while designing exoskeletons to have in silico knowledge of new optimal movements and their interaction forces.

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Year:  2019        PMID: 31247556     DOI: 10.1109/TNSRE.2019.2924536

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  8 in total

1.  Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking.

Authors:  Antoine Falisse; Maarten Afschrift; Friedl De Groote
Journal:  PLoS One       Date:  2022-01-25       Impact factor: 3.240

2.  Evaluating Knee Mechanisms for Assistive Devices.

Authors:  Shawanee' Patrick; Namita Anil Kumar; Pilwon Hur
Journal:  Front Neurorobot       Date:  2022-05-30       Impact factor: 3.493

3.  Model-Based Comparison of Passive and Active Assistance Designs in an Occupational Upper Limb Exoskeleton for Overhead Lifting.

Authors:  Xianlian Zhou; Liying Zheng
Journal:  IISE Trans Occup Ergon Hum Factors       Date:  2021-07-26

4.  Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach.

Authors:  Nicos Haralabidis; Gil Serrancolí; Steffi Colyer; Ian Bezodis; Aki Salo; Dario Cazzola
Journal:  PeerJ       Date:  2021-03-08       Impact factor: 2.984

5.  OpenSim Moco: Musculoskeletal optimal control.

Authors:  Christopher L Dembia; Nicholas A Bianco; Antoine Falisse; Jennifer L Hicks; Scott L Delp
Journal:  PLoS Comput Biol       Date:  2020-12-28       Impact factor: 4.475

6.  Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury.

Authors:  Míriam Febrer-Nafría; Benjamin J Fregly; Josep M Font-Llagunes
Journal:  Front Neurorobot       Date:  2022-01-24       Impact factor: 2.650

7.  Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait.

Authors:  Russell T Johnson; Nicholas A Bianco; James M Finley
Journal:  PLoS Comput Biol       Date:  2022-09-09       Impact factor: 4.779

8.  Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study.

Authors:  Nicos Haralabidis; Steffi L Colyer; Gil Serrancolí; Aki I T Salo; Dario Cazzola
Journal:  Sci Rep       Date:  2022-09-23       Impact factor: 4.996

  8 in total

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