Prediction of dynamic forces on lumbar joint using a recurrent neural network model

Details

• Personal Author:
  Hou Y ; Karwowski W ; Zurada JM
• Description:
  We propose a modified recurrent neural network model which establishes the relationship between kinematics and the dynamic forces on lumbar joint. By doing that we can have the forces predicted directly from kinematic variables while bypassing the costly procedure of measuring EMG (electromyography) signals and avoiding the use of biomechanics model. In the proposed model, we introduce the EMG signal as an intermediate output and loop it back to the input layer, instead of looping back the ultimate output, the forces. Since the EMG signal is a direct reflection of muscle activity, the most valuable point of this model is that the back-looping of the intermediate output has physical meaning. It solves the problem that the input and output of the system have no direct and explicit physical connection. At the same time, the advantages of recurrent neural network are utilized. [Description provided by NIOSH]
• Subjects:
  Air Pollutants, Occupational Dust Environmental Exposure Mining Occupational Health Safety Silicon Dioxide Workplace
• Keywords:
  Biomechanical-modeling Biomechanics Computer-models Manual-lifting Manual-materials-handling Materials-handling Mathematical-models Models Musculoskeletal-system
• ISBN:
  9780780388239
• Publisher:
  Institute of Electrical and Electronics Engineers
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Proceedings
• Place as Subject:
  Kentucky ; Ohio ; OSHA Region 4 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  360-365
• NIOSHTIC Number:
  nn:20041202
• Citation:
  Proceedings of the 2004 International Conference on Machine Learning and Applications, December 16-18, 2004, Louisville, Kentucky. Kantardzic-M; Nasraoui-O; Milanova-M, eds., Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2004 Dec; :360-365
• Editor(s):
  Kantardzic M; Nasraoui O; Milanova M
• Federal Fiscal Year:
  2005
• Performing Organization:
  Ohio State University
• Peer Reviewed:
  False
• Start Date:
  20020930
• Source Full Name:
  Proceedings of the 2004 International Conference on Machine Learning and Applications, December 16-18, 2004, Louisville, Kentucky
• End Date:
  20070929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:a9e4e746a39e981212e3e00616f1dd48e6fae0d0d840cc67da0215e37245eea60a85b173c9f39cc8bfb42849edcf2f2718b4dc38c70dae3d9a37a6f05512bc4b
• Download URL:
  https://stacks.cdc.gov/view/cdc/193812/cdc_193812_DS1.pdf
• File Type:
  [PDF - 3.45 MB ]