Please use this identifier to cite or link to this item:
Title: Application of recurrent neural networks for motion analysis with OpenSim
Authors: Yam, Li Hao
Keywords: Computer and Information Science
Issue Date: 2024
Publisher: Nanyang Technological University
Source: Yam, L. H. (2024). Application of recurrent neural networks for motion analysis with OpenSim. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: C146 
Abstract: This paper explores the application of Recurrent Neural Networks (RNN) – Long Short-Term Memory (LSTM) networks in particular, to predict muscle force and movement during stoop lift motions with data processed using OpenSim software. The study includes machine learning techniques such as biomechanical data analysis, feature selection via Random Forest and hyperparameter optimization with Optuna study to improve the accuracy of the LSTM model. The created LSTM model is able to process complex sequential biomechanical data which has the potential to positively impact stroke patients’ lower limb rehabilitation. By providing more precise and individualized treatment insights, machine learning approaches can prospectively revolutionize rehabilitation practices in the 21st century.
Schools: School of Mechanical and Aerospace Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
1.67 MBAdobe PDFView/Open

Page view(s)

Updated on Jun 17, 2024


Updated on Jun 17, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.