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Lower limb musculoskeletal modeling during normal walking, one-legged forward hopping, side jumping and knee flexion: A Validation study of the AnyBody Modeling System for optimizing Anterior Cruciate Ligament Reconstruction

Wibawa, A. 2014 [S.l.]: s.n.. 129 p.

Research output: ThesisThesis fully internal (DIV)

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  • Adhi Wibawa
This study is focusing on validating a musculoskeletal modelling of human motion by using EMG as part of our grand project in optimizing Anterior Cruciate Ligament (ACL) reconstruction by using a numerical model. The AnyBody™ Modeling System (AMS) is one of the advanced modelling tools which can simulate human motion and give prediction on important kinetic data such as muscle force, muscle activity and knee joint force. One of the AMS models, the GaitLowerExtremity model (GLEM) was used in this study. Ten healthy subjects performed four activity tests: normal walking (NW), one-legged forward hopping (FH), side jumping (SJ) and knee flexion motion. They were recorded by a Vicon camera system. Based on these data AMS was used to model the activities. Muscle activity prediction from AMS was then validated using 8 EMG electrodes which were attached to 8 muscles in lower limb: Rectus Femoris, Vastus Medialis and Lateralis, Bicep Femoris, Semitendinosus, Gastrocnemius Medialis dan Lateralis and Tibialis anterior. Cohen kappa value and Pearson correlation coefficient were used to calculate the level of agreement between AMS and EMG by using the variables: number of onset, offset and hill. Knee joint force prediction from AMS was also validated through literature study during NW.
The overall result showed that the level of agreement between AMS and EMG in all three variables was not so satisfying due to the nature of modelling (simplification of the knee, ankle and foot and assumption of muscle recruitment criteria) and other minor causes. Knee joint force prediction during NW was confirmed well with previous studies. The role of ACL during FH and SJ was described well by AMS. In conclusion, despite the inevitable simplifications, AMS is a powerful tool for modeling human motion. More accurate predictions can be obtained when improvements are implemented.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Supervisors/Advisors
  • Verkerke, Bart, Supervisor
  • Verdonschot, Nico, Co-supervisor, External person
  • Diercks, Ronald, Co-supervisor
  • Bulstra, Sjoerd, Assessment committee
  • Koopman, H.F.J.M., Assessment committee, External person
  • Molenaar, Willemina, Assessment committee
Award date11-Jun-2014
Place of Publication[S.l.]
Publisher
Print ISBNs978-90-367-7025-5
StatePublished - 2014

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