Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review.

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions.

A sensorimotor enhanced neuromusculoskeletal model for simulating postural control of upright standing.

The human's upright standing is a complex control process that is not yet fully understood. Postural control models can provide insights into the body's internal control processes of balance behavior. Using physiologically plausible models can also help explaining pathophysiological motion behavior.

Methods for integrating postural control into biomechanical human simulations: a systematic review.

Understanding of the human body's internal processes to maintain balance is fundamental to simulate postural control behaviour. The body uses multiple sensory systems' information to obtain a reliable estimate about the current body state. This information is used to control the reactive behaviour to maintain balance.

Method for Using IMU-Based Experimental Motion Data in BVH Format for Musculoskeletal Simulations via OpenSim.

Biomechanical simulation allows for in silico estimations of biomechanical parameters such as muscle, joint and ligament forces. Experimental kinematic measurements are a prerequisite for musculoskeletal simulations using the inverse kinematics approach. Marker-based optical motion capture systems are frequently used to collect this motion data.