Abstract

Automobile collisions in the United States lead to a staggering number of fatalities and injuries every year. Rear-end collisions account for over a quarter of all injury-producing collisions and often involve shoulder injuries. There is little, if any, research utilizing modern computational human body models to analyze rotator cuff injury risk during rear-end impacts. The goal of this research is to simulate rear-end impact crash forces using OpenSim and analyze the injury mechanism and risk to the rotator cuff muscles. OpenSim is an open-source software used for biomechanical modeling and simulation that enables the analysis of muscle behaviors that are difficult or invasive to measure in vivo. The upper extremity OpenSim model used in this research represents the 50th percentile male and is used to model the BioRID-II rear impact crash dummy. The simulations utilize inputs from a dynamic seat rating rear-impact test performed by the Insurance Institute for Highway Safety (IIHS), which simulates a 16km/h change in velocity. Two simulations were performed using the same acceleration pulse, one with deactivated muscles and another with fully activated muscles. The results from the deactivated simulation were compared against the published IIHS data and the rotator cuff muscles were analyzed for injury risks. In a deactivated state, the rotator cuff muscles measured near zero forces, stresses, and strains from the impact. The glenohumeral joint does become unstable; however, the resultant forces were well below values experienced during daily living tasks. In a fully activated state, the rotator cuff muscle strains were recorded between 2.5-3.5% and the maximum stress was 1.5MPa. These values are several times lower than the published ultimate stress and strain failure values for rotator cuff tendons. The glenohumeral joint was well stabilized with fully activated muscles. The results indicate that there are insufficient forces to damage healthy rotator cuff muscles for 50th percentile adult males, independent of muscle activation state for crash severities reaching 16.1km/h. Future research can be used to expand these results to various populations.

Date of publication

Spring 3-31-2023

Document Type

Thesis

Language

english

Persistent identifier

http://hdl.handle.net/10950/4175

Committee members

X. Neil Dong, Ph.D., Wycliffe Njororai, Ph.D., Chung Goh, Ph.D.

Degree

Master of Science in Kinesiology

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