Home   >   CSC-OpenAccess Library   >    Manuscript Information
A Neural Network Based Diagnostic System for Classification of Industrial Carrying Jobs With Respect of Low and High Musculoskeletal Injury Risk
Rohit Sharma, Ranjit Singh
Pages - 24 - 37     |    Revised - 15-01-2012     |    Published - 21-02-2012
Volume - 6   Issue - 1    |    Publication Date - February 2012  Table of Contents
Musculoskeletal Injuries, Physiological Risk, Artificial Neural Network
Even with many years of research efforts, the occupational exposure limits of different risk factors for development of Musculoskeletal disorders (MSDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors of MSDs interact in causing the injury, as the nature and mechanism of these disorders are relatively unknown phenomena. The task of an industrial ergonomist is complicated because the potential risk factors that may contribute to the onset of the MSDs interact in a complex way, and require an analyst to apply elaborate data measurement and collection techniques for a realistic job analysis. This makes it difficult to discriminate well between the jobs that place workers at high or low risk of above disorders. The main objective of this study was to to develop an artificial neural network based diagnostic system which can classify industrial jobs according to the potential risk for physiological stressors due to workplace design. Such a system could be useful in hazard analysis and injury prevention due to manual handling of loads in industrial environments. The results showed that the developed diagnostic system can successfully classify jobs into low and high risk categories of above musculoskeletal disorders based on carrying task characteristics. The Neural network based system developed gave the correct classification of the analysed industrial jobs with low and high risk. So, the system can be used as an expert system which, when properly trained, will classify carrying load by male and female workers into two categories of low risk and high risk work, based on the available characteristics factors.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 Bielefeld Academic Search Engine (BASE) 
6 Scribd 
7 SlideShare 
8 PdfSR 
A. Burdorf and G. Sorock. “Positive and negative evidence of risk factors for back disorders,” Scand J Work Environ Health, 1997, vol. 23, pp. 243–256.
H. Demuth and M. Beale, “Neural network toolbox for use with MATLAB,” user guide version 4. The MathWorks, Inc. USA, 2004.
H. Shahnavaz. “Workplace injuries in the developing countries,” Ergonomics, 1987, vol. 30, pp. 397-404.
I.W. Habib. “Neuro computing in high-speed network,” IEEE Communications Magazine October, 1995, pp. 38-40.
J. Allen, and A. Murray. “Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms,” Physiol. Meas., 1993, vol. 14, pp. 13-22.
J. Heinrich and B.M. Blatter. RSI symptoms in the Dutch labour force. Trends, risk factors and explanations. TSG, 2005, vol. 83, pp.16–24.
J. M. Zurada. “Introduction to Artificial Neural Systems,” West Publishing, St. Paul, Minnesota, 1992.
J. Zurada, W. Karwowski and W. S. Marras. “A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design,” Applied Ergonomics, Elsevier, 1997, Vol. 28, No. 1, pp. 49-5
J.W. Frank, M.S. Kerr, A.S. Brooker, S.E. DeMaio, A. Maetzel, H.S. Shannon, T.J. Sullivan and R.W. Norman. R.P. Wells. Disability resulting from occupational low back pain. Part I: What do we know about primary prevention? A review of the scientific evidence on prevention before disability begins. Spine, 1996, vol. 21, pp. 2908–2917.
K. Kemmlert. “Labor inspectorate investigation for the prevention of occupational musculoskeletal injuries,” (licentiate thesis) Solna, Sweden: National Institute of Occupational Health, 1994.
K. Vanwonterghem. “Work-related musculoskeletal problems: some ergonomics consideration,” J Hum Ergol, 1996, vol. 25, pp. 5-13.
M. Lagerstrom, T. Hansson and M. Hagberg. “Work-related low-back problems in nursing,” Scand J Work Environ Health, 1998, vol. 24, pp. 449–464.
More, J. J. “The Levenberg - Maquardt Algorithm: Implementation and theory, Numerical Analysis,” G. A. Watson (Ed.), Lecture Notes in Mathematics, Springer Verlag, 1977, Vol. 630, pp.105-116.
P. Spielholz, B. Silverstein, M. Morgan, H. Checkoway, and J. Kaufman. “Comparison of self-report, video observation and direct measurement methods for upper extremity musculoskeletal disorder physical risk factors,” Ergonomics, 2001, vol. 44, pp. 588-613.
S. Asensio-Cuesta, J.A. Diego-Mas, J. Alcaide-Marzal. “Applying generalized feedforward neural networks to classifying industrial jobs in terms of risk of low back disorders,” International journal of Industrial Ergonomics, 2010, Vol. 40, pp. 629-635.
S. H. Snook. "The Costs of Back Pain in Industry," Occupational Medicine: State-of-the-Art Reviews, 1988, vol. 3, no. 1, pp. 1-50.
S. Laderas, A.L. Felsenfeld. “Ergonomics and the dental office: an overview and consideration of regulatory influences” J Calif Dent Assoc, 2002, vol. 30, no. 2, pp.7-8.
S. Scutter, D.S. Turker and R. Hall. “Headaches and neck pain in farmers,” Australian Journal of Rural Health, 1997, vol. 5, no. 1, pp. 2-5.
W.E. Hoogendoorn, M.N. van Poppel, P.M. Bongers, B.W. Koes and L.M. Bouter. “Physical load during work and leisure time as risk factors for backpain,” Scand J Work Environ Health, 1999, vol. 25, pp. 387–403.
W.E. Hoogendoorn, M.N. van Poppel, P.M. Bongers, B.W. Koes and L.M. Bouter. “Systematic review of psychosocial factors at work and private life as risk factors for back pain,” Spine, 2000, vol. 25, pp. 2114–2125.
Mr. Rohit Sharma
Dayalbagh Educational Institute - India
Professor Ranjit Singh
Education - India

View all special issues >>