Comparison of the Statically Equivalent Serial Chain Center of Mass Estimation Method to OpenSim's Residual Reduction Algorithm

Links

URL: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1628009554478147

Abstract

Being able to determine a person’s center of mass (COM) location is very important and useful to many studies, but not easily calculated. COM is used in a variety of studies, especially those dealing with balance, as COM must be over the center of pressure (COP) for something to maintain balance in a static position. An open-source software called OpenSim has its own COM estimation built into its Residual Reduction Algorithm (RRA) and is widely used in the field of biomechanics. This study seeks to compare the accuracy of this estimation to a recently developed COM estimation method called the Statically Equivalent Serial Chain (SESC) estimation. This study uses data collected via motion capture and force plates to further validate the SESC method as well as compare its accuracy to that of the currently implemented RRA through motion capture and data processing in OpenSim and MATLAB. Motion capture data provided an accurate representation of subject kinematics used in both COM estimations, and force plate COP as the metric for comparison in the horizontal directions. For data collection, 1 PVC humanoid and 16 subjects between the ages of 18 and 50, stood in 40 static poses. The poses were initially processed through the motion capture software, Vicon Nexus, and then inverse kinematics, dynamics, and RRA in OpenSim. Finally, the SESC COM estimation was determined using this processed data through a custom MATLAB script, and the magnitude of the error in the horizontal plane of all subjects’ poses was analyzed.

The SESC estimation proved to be significantly better than the OpenSim estimation using RRA through analysis of variance (ANOVA) testing, with an average error of 7.82 mm for SESC and 10.69 mm for RRA (p<0.0001, P=0.99). Additionally, the SESC error maintained its accuracy within this new experimental study, being below the maximum error of previous COM estimation studies. This study differs from other studies because of its more developed breakdown of the human body into vectors for SESC, allowing for more free moving joints, as well as a much larger set of collection data for analysis. This significant difference has proven that SESC is a worthwhile COM estimation to be used in biomechanical studies, and could viably be implement into a software like OpenSim as an improvement to its COM estimation.

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