A hybrid neuro-fuzzy approach for spinal force evaluation in manual materials handling tasks

Details

• Personal Author:
  Hou Y ; Karwowski W ; Marras WS ; Zurada JM
• Description:
  Evaluation of the spinal forces from kinematics data is very complicated because it involves the handling of relationship between kinematic variables and electromyography (EMG) responses, as well as the relationship between EMG responses and the forces. A recurrent fuzzy neural network (RFNN) model is proposed to establish the kinematics-EMG-force relationship and model the dynamics of muscular activities. The EMG signals are used as an intermediate output and are fed back to the input layer. Since the EMG signal is a direct reflection of muscular activities, the feedback of this model has a physical meaning. It expresses the dynamics of muscular activities in a straightforward way and takes advantage from the recurrent property. The trained model can then have the forces predicted directly from kinematic variables while bypassing the procedure of measuring EMG signals and avoiding the use of biomechanics model. A learning algorithm is derived for the RFNN. [Description provided by NIOSH]
• Subjects:
  Air Pollutants, Occupational Air Pollution, Indoor Men Mining Occupational Health Personal Protective Equipment Respiratory Protective Devices Risk Assessment Safety Women
• Keywords:
  Biomechanical-modeling Biomechanics Computer-models Manual-lifting Manual-materials-handling Materials-handling Mathematical-models Models Musculoskeletal-system
• ISBN:
  9783540283201
• ISSN:
  0302-9743
• 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:
  3612
• NIOSHTIC Number:
  nn:20041201
• Citation:
  Advances in Natural Computation: Proceedings of the First International Conference, ICNC 2005, August 27-29, 2005, Changsha, China. Wang L, Chen K, Ong YS, eds., New York: Springer, 2005 Jan; 3612(Pt III):1216-1225
• Editor(s):
  Wang L; Chen K; Ong YS
• Federal Fiscal Year:
  2005
• Performing Organization:
  Ohio State University
• Peer Reviewed:
  True
• Part Number:
  III
• Start Date:
  20020930
• Source Full Name:
  Advances in Natural Computation: Proceedings of the First International Conference, ICNC 2005, August 27-29, 2005, Changsha, China
• End Date:
  20070929
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:f46cf24e849caa747aad6e41127d33907deed1a47563a33890dfb2bd6a79e71995e7d5c1c473fbcbda6802d16715fc7f593f6e68d79b020f7cd12a14cf1f74f1
• Download URL:
  https://stacks.cdc.gov/view/cdc/193811/cdc_193811_DS1.pdf
• File Type:
  [PDF - 3.42 MB ]