The use of artificial neural networks to reduce data collection demands in determining spine loading: a laboratory based analysis

Abstract

The extensive data requirements of three-dimensional inverse dynamics and joint modelling to estimate spinal loading prevent the implementation of these models in industry and may hinder development of advanced injury prevention standards. This work examines the potential of feed forward artificial neural networks (ANNs) as a data reduction approach and compared predictions to rigid link and EMG-assisted models. Ten males and ten females performed dynamic lifts, all approaches were applied and comparisons of predicted joint moments and joint forces were evaluated. While the ANN under- predicted peak extension moments (p = 0.0261) and joint compression (p < 0.0001), predictions of cumulative extension moments (p = 0.8293) and cumulative joint compression (p = 0.9557) were not different. Therefore, the ANNs proposed may be used to obtain estimates of cumulative exposure variables with reduced input demands; however they should not be applied to determine peak demands of a worker's exposure.

Keywords:

• spine
• cumulative loading
• artificial neural network
• injury
• modelling

Acknowledgements

This work was funded by the Natural Sciences and Engineering Research Council Canada and the AUTO21 Network of Centers of Excellence, whose funding is provided by the Canadian federal government. Jack Callaghan is supported by a Canada Research Chair in Spine Biomechanics and Injury Prevention. The authors would also like to thank Katie Selman and Lynne Pronovost for their assistance in data processing.