Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses

in Motor Control

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Heiko Wagner Movement Science, University of Münster, Münster, Germany
Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany
Center for Interdisciplinary Prevention of Diseases related to Professional Activities, Friedrich-Schiller-Universität, Jena, Germany

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Kim Joris Boström Movement Science, University of Münster, Münster, Germany

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Marc H.E. de Lussanet Movement Science, University of Münster, Münster, Germany
Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany

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Myriam L. de Graaf Movement Science, University of Münster, Münster, Germany
Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Münster, Germany

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Christian Puta Center for Interdisciplinary Prevention of Diseases related to Professional Activities, Friedrich-Schiller-Universität, Jena, Germany
Department of Sports Medicine and Health Promotion, Friedrich-Schiller-Universität, Jena, Germany

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Luis Mochizuki School of Arts, Sciences, and Humanities, University of Sao Paulo, São Paulo, SP, Brazil

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https://orcid.org/0000-0002-7550-2537
DOI:
https://doi.org/10.1123/mc.2021-0110
Keywords:
knee prosthesis; joint loading; computational model; inverse kinematics; muscle model; optimization criterion
First Published Online:
17 Oct 2022
In Print:
Volume 27: Issue 2
Page Range:
161–178
Restricted access

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.

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