Seminar entitled “Personalization of Biomechanical Models for Ergonomic Assessment Tools: An MRI Study” by Celal Güngör.

Abstract of the Seminar:

To calculate the forces acting on the human lumbar spine, accurate biomechanical model inputs are required. However, some model inputs are limited by assumptions. One of the most vital model inputs, the mechanical lever arm of the erector spinae muscle mass, (ESMLA), is typically approximated using a fixed value (5 cm or 2 inch) to simplify biomechanical models. In other words, this crude assumption assumes that a short and light lady has the same size ESMLA distance as a tall and heavy man, and she exposes to the same amount of spinal loading as he does. However, early Magnetic Resonance Imaging (MRI) studies have showed that ESMLA distance is sensitive to subject variables (characteristics and anthropometrics). The objective of this MRI study was to develop regression models to estimate the ESMLA distance based solely on subject variables (i.e., gender, age, height, weight and some additional anthropometric variables such as lean body mass, sitting height, shoulder width). This will allow currently available biomechanical models to incorporate subject specific parameters and should improve model predictions and then closely estimate low back pain risk for a given task. Results indicated that the ESMLA distance can be easily and reliably estimated using subject variables. The results of the present study found that using a fixed ESMLA value and/or using an empirically derived average value could cause higher errors than a regression model can provide. Regression models suggested in the present study yielded smaller error percentages, which results in smaller errors in spinal loading calculations. Regression models will allow designers to calculate subject specific spinal loading and set personalized loading limitations.