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Faculté des Sciences appliquées
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Enhancing Human Motion Simulation with Neural Networks

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Pottier, Laura ULiège
Promotor(s) : Bruls, Olivier ULiège ; Sacré, Pierre ULiège
Date of defense : 4-Sep-2023/5-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/18331
Details
Title : Enhancing Human Motion Simulation with Neural Networks
Author : Pottier, Laura ULiège
Date of defense  : 4-Sep-2023/5-Sep-2023
Advisor(s) : Bruls, Olivier ULiège
Sacré, Pierre ULiège
Committee's member(s) : Ruffoni, Davide ULiège
Schwartz, Cédric ULiège
Language : English
Discipline(s) : Engineering, computing & technology > Multidisciplinary, general & others
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil biomédical, à finalité spécialisée
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] Realistic simulation of human movement plays a crucial role across various domains, including medicine, robotics training, video games, and virtual reality. The primary challenge in these domains lies in generating physiologically plausible human movements. This challenge is typically addressed via trajectory optimisation, either in the muscle actuation-space – producing human-like movements but demanding computationally intensive simulations – or in the space of joint-actuation, reducing the complexity of the simulations but often generating movements that are non-feasible for humans to achieve.
In a recent study conducted by Jiang et al., a method was introduced to convert a muscle actuation-based optimisation problem into an equivalent joint actuation-based problem. This transformation involves leveraging neural networks trained to approximate joint torque limits and energy functions. This work aims to reproduce the neural training procedure with a 2D model, and then use it in a problem of predictive gait simulation. In particular, the work focuses on the correction function R which allows the torque limit to be deduced.
The initial objective involves grasping the neural network training procedure in order to adopt the methodology appropriately. This step is crucial in ensuring the training of adequate neural networks capable of accurately approximating the desired function.
The second objective entails employing trained neural networks to address a predictive gait simulation problem. In this second part of the work, the results obtained using this method are compared with the original problem – a comprehensive model encompassing a skeletal structure and muscles. Additionally, a comparison is drawn with an alternative problem variant employing a simplified model, comprising solely a torque-driven skeletal structure.


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Author

  • Pottier, Laura ULiège Université de Liège > Master ing. civ. biomed., à fin.

Promotor(s)

Committee's member(s)

  • Ruffoni, Davide ULiège Université de Liège - ULiège > Département d'aérospatiale et mécanique > Mécanique des matériaux biologiques et bioinspirés
    ORBi View his publications on ORBi
  • Schwartz, Cédric ULiège Université de Liège - ULiège > Département des sciences de la motricité > Kinésithérapie générale et réadaptation
    ORBi View his publications on ORBi
  • Total number of views 59
  • Total number of downloads 6










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