Addition of torso musculature to a simplified musculoskeletal model for 2D predictive simulation of the sit-to-walk movement

Abstract

The ability of musculoskeletal models to acquire metrics and simulate human motion enables researchers to perform biomechanical analysis of daily activities. The improved analysis of human motion could for instance help recognize physical decline early. The sit-to-stand movement, which is performed 60 times daily on average, is one greatly affected by physical decline. Predictive simulation could enhance comprehension of the influence of physical decline, but most musculoskeletal models are not suitable for predictive simulation as they lack computational speed. In this project, a simplified musculoskeletal model called the H1126 with 11 joints and 26 muscular actuators suitable for sit-to-walk simulations is developed. Muscle moment arms at vertebral levels T12 through S1 are based on literature and optimized in OpenSimCreator using via points. Muscle-tendon minus tendon slack length, normalized fiber length, and maximum joint moments of the H1126 are compared to three state-of-the-art OpenSim models containing torso musculature. Results show that the H1126 performs well in terms of muscle geometry and force output. The H1126 muscle moment arms are within a 2 SD margin compared to the literature. The H1126 muscle-tendon length minus tendon slack length and normalized fiber length are within a 10\% margin compared to the state-of-the-art comparison models. Maximum joint moments of the H1126 are also within a 2 SD margin compared to the state-of-the-art torso models and literature. The H1126 is more suitable for predictive simulation than the comparison models as it is computationally faster due to the minimized number of muscle fascicles and the use of purely via points while performing similarly in terms of force output.