By a News Reporter-Staff News Editor at Biotech Week -- Investigators discuss new findings in Inverse Kinematics. According to news reporting originating in Columbia, Missouri, by NewsRx journalists, research stated, "Knowledge of the forces acting on musculoskeletal joint tissues during movement benefits tissue engineering, artificial joint replacement, and our understanding of ligament and cartilage injury. Computational models can be used to predict these internal forces, but musculoskeletal models that simultaneously calculate muscle force and the resulting loading on joint structures are rare."
The news reporters obtained a quote from the research from the University of Missouri, "This study used publicly available gait, skeletal geometry, and instrumented prosthetic knee loading data [11 to evaluate muscle driven forward dynamics simulations of walking. Inputs to the simulation were measured kinematics and outputs included muscle, ground reaction, ligament, and joint contact forces. A full body musculoskeletal model with subject specific lower extremity geometries was developed in the multibody framework. A compliant contact was defined between the prosthetic femoral component and tibia insert geometries. Ligament structures were modeled with a nonlinear force strain relationship. The model included 45 muscles on the right lower leg. During forward dynamics simulations a feedback control scheme calculated muscle forces using the error signal between the current muscle lengths and the lengths recorded during inverse kinematics simulations. Predicted tibio-femoral contact force, ground reaction forces, and muscle forces were compared to experimental measurements for six different gait trials using three different gait types (normal, trunk sway, and medial thrust). The mean average deviation (MAD) and root mean square deviation (RMSD) over one gait cycle are reported. The muscle driven forward dynamics simulations were computationally efficient and consistently reproduced the inverse kinematics motion. The forward simulations also predicted total knee contact forces (166 N< MAD < 404 N, 212 N< RMSD < 448 N) and vertical ground reaction forces (66 N< MAD < 90 N, 97 N< RMSD < 128 N) well within 28% and 16% of experimental loads, respectively. However the simplified muscle length feedback control scheme did not realistically represent physiological motor control patterns during gait."
According to the news reporters, the research concluded: "Consequently, the simulations did not accurately predict medial/lateral tibio-femoral force distribution and muscle activation timing."
For more information on this research see: Evaluation of a musculoskeletal model with prosthetic knee through six experimental gait trials. Medical Engineering & Physics, 2014;36(3):335-344. Medical Engineering & Physics can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Medical Engineering & Physics - www.elsevier.com/wps/product/cws_home/30456)
Our news correspondents report that additional information may be obtained by contacting M. Kia, University of Missouri, Dept. of Orthopaed Surg, Columbia, MO 65211, United States. Additional authors for this research include A.P. Stylianou and T.M. Guess (see also Inverse Kinematics).
Keywords for this news article include: Columbia, Missouri, United States, Machine Learning, Inverse Kinematics, Emerging Technologies, North and Central America
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