Numerical Identification of Deep Muscle Residual Tensions (Tones) Based on Multi-Directional Trunk Stiffness Data

This work proposes an identification methodology to estimate the residual tension values (tones) for the human trunk muscles, including the deep ones, using multidirectional trunk stiffness data in association with numerical modeling. The role of this residual muscle tension or contraction is to maintain posture and balance. Knowledge of the tone is useful for the diagnosis and treatment of several spinal diseases and is important for realistic modeling and numerical simulation of trunk behavior. Most of the existing techniques for the measurement and estimation of muscle tones, such as electromyography, are restricted to superficial muscles. Those designed for deep muscles are invasive and present risks of infection and pain. In contrast, the proposed identification approach is painless and safe for the subject. It proceeds by matching the experimental trunk stiffness with numerical upper and lower estimates of the stiffness, to construct an inclusive solution domain of possible tone values of superficial as well as deep trunk muscles. By dividing the trunk muscles into three classes, each assumed to share the same tone ratio, a reasonable solution domain is obtained that exhibits a significant overlap with ranges of muscle tones found in the literature.