Enclosing 95% confidence area and volume to center of pressure and center of mass in posturography using optimization algorithm and coordinate ascent methods.

Balance control has been evaluated using center of pressure (CoP) and center of mass (CoM). One of the most common approaches in stabilometry is enclosing ellipse to 95% of data using principal component analysis (PCA) or covariance methods. However, these methods have limitations, including normality assumption, lack of accuracy, and sample size influence. This study aims to use a coordinate-ascent optimization algorithm to address the above limitations. Fifteen healthy young adults were recruited and performed six sit-to-stand trials. The optimization algorithm, data trimmer, PCA, and covariance methods were used. For robust analysis, 15000 CoM and CoP data were simulated for five distribution types. Repeated measure ANOVA was used to evaluate the difference in area and volume enclosed using different methods. The quality of fit (accuracy) and the precision of the methods were assessed using the percentage goodness of fit and coefficient of variation. Robustness was also explored with the 95% confidence interval of the mean absolute error and coefficient variation of error. The optimization enclosed smaller ellipse/ellipsoids to CoP and CoM data than other methods with higher accuracy, precision, and robustness. Although the optimization enclosed larger ellipses than the data trimmer, its results were more accurate and precise. The coefficient of variation of error indicated that the optimization performs with marginally higher estimation error for leptokurtic data compared to the data trimmer. Overall, our results demonstrated that the optimization method performs better than other methods, leading to higher sensitivity in detecting changes in CoP and CoM due to pathological conditions.