Mathematical description of proprioception through muscle activation signal generation in core musculoskeletal system

Central Pattern Generators (CPGs) produce the majority of muscle activation signals during gait whereas, reflexive signals from proprioception deal with perturbations. The reflexive neuromuscular signals are rather difficult to measure directly. The objective of this study is to mathematically estimate the reflexive neuromuscular signals of the core muscles during a movement similar to a normal gait. 1-Musculoskeletal model: a 2-D musculoskeletal model is designed for the upper body. 2-Neuromuscular model: mathematical models for neuromuscular reflexes are derived based on their physiological function descriptions. 3-Simulation: mathematical equations are extracted from the model and solved using numerical methods. Kinematics, muscle forces, and activation signals are obtained from the simulation. 4-Verification: verification of the model is conducted through a sample study. Five IMU sensors and four surface EMG electrodes are utilized to gather data from the experiment. Upper body kinematics, core muscle forces, and activation signals of the subject are estimated through model simulation for the intended movement. The verification process indicates similarity between simulation outcomes and test results. The proposed mathematical description of proprioception can successfully simulate and estimate reflexive activation signals of core muscles. The estimated activation signals can be employed in conjunction with a musculoskeletal model to predict body kinematics. Reflexive signal estimation of core muscles can further enhance studies concerning body stability control. Such predictive models have also the aptitude to be employed as an auxiliary clinical tool to help the therapist choose appropriate treatment protocols.