Assessing Scaling and Kinematic Errors in a Coupled Experimental-Computational Infant Musculoskeletal Model

Abstract

Musculoskeletal models are valuable tools that enable the study and quantification of biomechanical parameters, allowing researchers to better understand the mechanisms influencing or contributing to human movement. Furthermore, musculoskeletal models have the potential to serve as diagnostic tools for identifying pathologies and disorders, such as developmental dysplasia of the hip. However, current musculoskeletal models are developed using adult subjects, with only a few studies focusing on infant populations, despite the greatest growth rate being in early infancy. Therefore, the objective of this study was to evaluate the impact of multiple linear scaling approaches of increasing complexity on the development of an infant musculoskeletal model. Motion capture technology was used to collect data from the spontaneous kicking movement of a 2.4-month-old infant lying supine. The experimental motion capture data and anthropometric measurements were used to scale the generic gait2392 OpenSim model. Four linear scaling methods of increasing complexity were used: uniform (Uni), nonuniform (Non), nonuniform with knee and ankle joint centers (NAKJCs), and nonuniform with knee, ankle, and regression-derived hip joint centers (NHJCs). Results suggest that the maximum marker errors decreased with the increasing complexity of the scaling approach. The Uni scaling approach resulted in the largest scaling and kinematic errors, with maximum marker errors of 4.92 cm and 5.30 cm, respectively. The NHJCs scaling approach had the lowest maximum marker errors, with errors of 4.17 cm and 4.36 cm, respectively. The scaling method used to develop infant musculoskeletal models should be considered carefully, especially when using linearly scaling generic models developed using adult cadaveric data.

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