Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks

Accurate determination of body segment parameters is crucial for studying human movement and joint forces using musculoskeletal (MSK) models. However, existing methods for predicting segment mass have limited generalizability and sensitivity to body shapes. With recent advancements in machine learning, this study proposed a novel artificial neural network-based method for computing subject-specific trunk segment mass and center of mass (CoM) using only anthropometric measurements. We first developed, trained, and validated two artificial neural networks that used anthropometric measurements as input to predict body shape (ANN1) and tissue mass (ANN2). Then, we calculated trunk segmental mass for two volunteers using the predicted body shape and tissue mass. The body shape model (ANN1) was tested on 279 subjects, and maximum deviation between the predicted body shape and the original was 28 mm. The tissue mass model (ANN2) was evaluated on 223 subjects, which when compared to ground truth data, had a mean error of less than 0.51% in the head, trunk, legs, and arms. We also compared the two volunteer's trunk segment mass with experimental data and found similar trend and magnitude. Our findings suggested that the proposed method could serve as an effective and convenient tool for predicting trunk mass.