Prediction of EMG signals of trunk muscles in manual lifting using a neural network model

Details

• Personal Author:
  Hou Y ; Karwowski W ; Zurada JM
• Description:
  An EMG (electromyography) signal prediction model is built using artificial neural network. Kinematics variables and subject variables are selected as inputs of this model. A novel structure of feedforward neural network is proposed in This work to obtain better accuracy of prediction. By adding regional connections between the input and the output, the new architecture of the neural network can have both global features and regional features extracted from the input. The global connections put more emphasis on the whole picture and determine the global trend of the predicted curve, while the regional connections concentrate on each point and modify the prediction locally. Back-propagation algorithm is used in the modeling. A basic structure of neural network designed for this problem is discussed. Then to overcome its drawbacks, we propose a new structure. [Description provided by NIOSH]
• Subjects:
  Aerosols Coal Coal Mining Environmental Health Fungi Microbiology Mining Occupational Health Safety Workplace
• Keywords:
  Biomechanical-modeling Biomechanics Computer-models Manual-lifting Manual-materials-handling Materials-handling Mathematical-models Models Musculoskeletal-system
• ISBN:
  9780780383593
• 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
• Volume:
  3
• NIOSHTIC Number:
  nn:20041199
• Citation:
  Proceedings of International Joint Conference on Neural Networks, July 25-29, 2004, Budapest, Hungary. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2004 Jul; 3:1935-1940
• Federal Fiscal Year:
  2004
• Performing Organization:
  Ohio State University
• Peer Reviewed:
  False
• Start Date:
  20020930
• Source Full Name:
  Proceedings of International Joint Conference on Neural Networks, July 25-29, 2004, Budapest, Hungary
• End Date:
  20070929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7d8d41292db858878fb54b623afaa2a5afca5c953a8358001097dd136e2aea92a444cc06ef47dc4e128849d248aa9917c04743dcf7a4fc0e518fa34f4094f441
• Download URL:
  https://stacks.cdc.gov/view/cdc/193809/cdc_193809_DS1.pdf
• File Type:
  [PDF - 3.35 MB ]