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Details

Derived Spine Loads in Response to Multiple Risk Factors.

PB2012113426

Publication Date	2008
Personal Author	Marras, W. S.; Karwowski, W.; Zurada, J. M.; Davis, K. G.
Page Count	66
Abstract	Low back disorders are extensive and extremely expensive for industry. As such, there is a need for better techniques that can assess structural loading in the industrial setting. To date, investigation of the loading on the back has been with simple static models that neglected the complex coactivity patterns of the trunk musculature. Thus, the objective of the current project was to develop a neuron-fuzzy engine that could predict the muscle activation pattern for common lifting conditions. The predicted muscle activation data can then be input into the EMG-assisted model to predict the spine loads.
Keywords	Spine Biomechanics Anatomical models Back injuries Computer models Dynamic loads Ergonomics Manual lifting Materials handling Mathematical models Musculoskeletal system Risk factors Work environment Low back disorders
Source Agency	National Institute for Occupational Safety and Health
Corporate Authors	Ohio State Univ., Columbus.; Cincinnati Univ., OH.; Louisville Univ., KY.; National Inst. for Occupational Safety and Health, Washington, DC.
Supplemental Notes	Prepared in cooperation with Louisville Univ., KY. and Cincinnati Univ., OH. Sponsored by National Inst. for Occupational Safety and Health, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	201223

Derived Spine Loads in Response to Multiple Risk Factors.

Derived Spine Loads in Response to Multiple Risk Factors.
PB2012113426

Publication Date	2008
Personal Author	Marras, W. S.; Karwowski, W.; Zurada, J. M.; Davis, K. G.
Page Count	66
Abstract	Low back disorders are extensive and extremely expensive for industry. As such, there is a need for better techniques that can assess structural loading in the industrial setting. To date, investigation of the loading on the back has been with simple static models that neglected the complex coactivity patterns of the trunk musculature. Thus, the objective of the current project was to develop a neuron-fuzzy engine that could predict the muscle activation pattern for common lifting conditions. The predicted muscle activation data can then be input into the EMG-assisted model to predict the spine loads.
Keywords	Spine Biomechanics Anatomical models Back injuries Computer models Dynamic loads Ergonomics Manual lifting Materials handling Mathematical models Musculoskeletal system Risk factors Work environment Low back disorders
Source Agency	National Institute for Occupational Safety and Health
Corporate Authors	Ohio State Univ., Columbus.; Cincinnati Univ., OH.; Louisville Univ., KY.; National Inst. for Occupational Safety and Health, Washington, DC.
Supplemental Notes	Prepared in cooperation with Louisville Univ., KY. and Cincinnati Univ., OH. Sponsored by National Inst. for Occupational Safety and Health, Washington, DC.
Document Type	Technical Report
NTIS Issue Number	201223