Can Computationally Predicted Internal Loads Be Used to Assess Sitting Discomfort? Preliminary Results

  • Ilias Theodorakos
  • Léo Savonnet
  • Georges Beurier
  • Xuguang WangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 823)


Whether internal body loads such as muscle forces and joint forces could be used as objective factors to assess sitting discomfort remains an open research question. The present study investigated the potential relationship between computationally predicted internal loads and subjective discomfort ratings. Volunteers were recruited to provide discomfort ratings on a wide range of sitting configurations resulted in by altering the seat pan angle and the backrest angle of a multi-adjustable experimental seat. Moreover, two preferred seat pan angles were selected by the participants, starting from two different initial seat pan angles, allowing the classification of the trials to preferred and not preferred. Kinematic, force and pressure data served as inputs on a musculoskeletal model that enabled the computation of internal loads. Significant positive correlations were found between the subjective ratings and muscle force, compressive force between L4–L5 and seat pan shear force. Significant reduction in the seat pan shear force was found for the preferred compared to the not preferred trials, but no significant differences were found for the muscle and joint forces. Our results suggest that the seat pan shear force and potentially computationally predicted internal loads could be used to assess sitting discomfort. However, the outcome should be interpreted with caution due to limited number of observations.


Sitting discomfort Musculoskeletal modeling Inverse dynamics Internal loads 



The work is partly supported by Direction Générale de l’Aviation Civile (project n°2014 930818).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Univ Lyon, Université Lyon 1, IFSTTAR, LBMC UMR_T 9406LyonFrance

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