Prediction of the Spinal Load during Static Loading Conditions using EMG model and Three Optimization models

정적 부하 작업에서 EMG 모델과 세가지 최적화 모델을 이용한 척추 부하 평가

  • Song, Young Woong (Dept. of Occupational Health, Catholic University of Daegu) ;
  • Chung, Min Keun (Dept. of Industrial Engineering, Pohang University of Science and Technogy)
  • 송영웅 (대구가톨릭대학교 산업보건학과) ;
  • 정민근 (포항공과대학교 산업공학과)
  • Received : 2005.02.18
  • Accepted : 2005.03.21
  • Published : 2005.03.30

Abstract

This study investigated the spinal loads(L5/S1 disc compression and shear forces) predicted from four biomechanical models: one EMG model and three optimization models. Three objective functions used in the optimization models were to miminize 1) the cubed muscle forces : MF3, 2) the cubed muscle stress : MS3, 3) maximum muscle intensity : MI. Twelve healthy male subjects participated in the isometric voluntary exertion tests to six directions : flexion/extension, left/right lateral bending, clockwise/ counterclockwise twist. EMG signals were measured from ten trunk muscles and spinal loads were assessed at 10, 20, 30, 40, 50, 60, 70, 80, 90%MVE(maximum voluntary exertion) in each direction. Three optimization models predicted lower L5/S1 disc compression forces than the EMG model, on average, by 31%(MF3), 27%(MS3), 8%(MI). Especially, in twist and extension, the differences were relatively large. Anterior-posterior shear forces predicted from optimization models were lower, on average, by 27%(MF3), 21%(MS3), 9%(MI) than by the EMG model, especially in flexion(MF3 : 45%, MS3 : 40%, MI : 35%). Lateral shear forces were predicted far less than anterior-posterior shear forces(total average = 124 N), and the optimization models predicted larger values than the EMG model on average. These results indicated that the optimization models could underestimate compression forces during twisting and extension, and anterior-posterior shear forces during flexion. Thus, future research should address the antagonistic coactivation, one major reason of the difference between optimization models and the EMG model, in the optimization models.

Keywords

Acknowledgement

Supported by : 대구가톨릭대학교

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