A biomechanical basis for low back injury risk in high exertion tasks

2005 ◽  
Author(s):  
Don B. Chaffin
2004 ◽  
Vol 10 (4) ◽  
pp. 255-272 ◽  
Author(s):  
W. G. Allread ◽  
J. R. Wilkins III ◽  
T. R. Waters ◽  
W. S. Marras

Author(s):  
Steven A. Lavender ◽  
William S. Marras ◽  
Sue A. Ferguson ◽  
Riley E. Splittstoesser ◽  
Gang Yang ◽  
...  

Low back disorders continue to be the most common and significant work-related musculoskeletal disorders in the US. Identifying what constitutes a “safe” physical workload has been the biggest challenge facing injury prevention efforts. Prior low back injury risk models have focused on manufacturing activities where there is limited variability in the parameters used to describe the exposures to low back disorder risk factors. Lifting tasks in distribution centers can have considerably more variability in load and physical layout. The goal of this project was to identify and quantify measures that characterize the biomechanical risk factors, including measures of the load moment exposure, and measures that characterize the duty cycle that are predictive of low back disorders in distribution centers. Thus, our hypothesis was that we could define a relationship between moment exposure parameters and the low back disorder incidence rates. A cross-sectional study was designed to examine the mechanical risk factors responsible for reported low back injury in distributions centers. The physical exposure was measured on 195 workers on 50 jobs in 21 distribution centers using a sonic-based Moment Exposure Tracking System (METS). The METS measures load, force, load moment, torso kinematics, and temporal parameters of the job simultaneously. For each job, low back injury rates were collected retrospectively from the company's records over the prior 3-year period. The data were used to develop a risk model designed to predict back injury risk based upon direct measures of load and load moment exposure. The model incorporates biomechanical variables which include the load moment and horizontal sliding forces, as well as a temporal variable indicating the opportunity for micro-breaks during the work process. Overall, the presented model has very good sensitivity (87%) and specificity (73%).


2007 ◽  
Vol 50 (9) ◽  
pp. 687-696 ◽  
Author(s):  
Catherine Trask ◽  
Kay Teschke ◽  
Judy Village ◽  
Yat Chow ◽  
Peter Johnson ◽  
...  

Author(s):  
Brian J. Carnahan ◽  
Mark S. Redfern

Injury risk models can play a key role in ergonomic worksite analysis directed at preventing low back disorders. Such models can be used to classify lifting tasks as having the same characteristics as those tasks which have had a high (or low) incidence rate of back injuries. Two evolutionary computation techniques (genetic algorithms GA, and genetic programming GP) were used to construct low back injury risk models. A GA model, GP model, logistic regression model, and an artificial neural network were constructed and tested using 235 documented lifting task cases. Results indicated that the evolutionary approaches were superior to the other models in terms of classification performance and parsimony.


AAOHN Journal ◽  
1995 ◽  
Vol 43 (9) ◽  
pp. 489-493 ◽  
Author(s):  
Sally L. Lusk ◽  
Marion Gillen

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