Prediction of hand forces and moments using neural net modeling of ground reaction forces and kinematic data

Details

• Personal Author:
  McFerron J ; Sinsel E ; Wiker S
• Description:
  Occupational biomechanical models require reasonably accurate hand forces and moments to sequentially compute joint reaction forces and moments through the body producing ground forces and moments. We have pursued development of Artificial Neural Network (ANN) models to predict independent hand forces and moments using limited postural data and measured ground reaction forces. This paper summarizes our initial findings regarding the validity of the proposed modeling schema. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Ergonomics Musculoskeletal System Nervous System Occupational Health Safety
• Keywords:
  Biodynamics Biokinetics Biomechanical-modeling Biomechanics Body-mechanics Body-regions Kinetics Mathematical-models Measurement-equipment Neuromuscular-function Neuromuscular-system Physiological-response Physiological-stress Physiology Posture Simulation-methods

  More +
• ISSN:
  0021-9290
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  OSHA Region 3 ; West Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  40
• NIOSHTIC Number:
  nn:20039797
• Citation:
  J Biomech 2007 Jul; 40(S2):S28
• Email:
  sfwiker@mail.wvu.edu
• Federal Fiscal Year:
  2007
• Peer Reviewed:
  False
• Source Full Name:
  Journal of Biomechanics
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:87abbb4c75286c257f94aae03feddc01f32baa49d2054da41b53099f79e9d25c00f3d2404c45b9cb5da9b84f0b66e336d96f2283796fda1d779e1666f4776e0c
• Download URL:
  https://stacks.cdc.gov/view/cdc/192894/cdc_192894_DS1.pdf
• File Type:
  [PDF - 108.15 KB ]