OpenBalance: An Interoperable Computational Framework, Progressive 8-Stage Protocol, and Outlier Metric for Dynamic Posturography—A Technical Proof-of-Concept

Human postural control assessment is currently characterized by methodological inconsistencies in data processing, measurement protocols, and spatial sway area calculations, often resulting in potential ceiling effects when evaluating cohorts with high baseline balance stability. To address these limitations, we developed OpenBalance, an interoperable, open-source Python framework designed to standardize signal processing across diverse commercial and custom force plate formats. Alongside this computational architecture, we propose a progressive 8-stage measurement protocol designed to scale postural difficulty and introduce a dimensionless metric, the Outlier Excursion Ratio (OER). The OER mathematically leverages the spatial disparity between the Convex Hull Area and the 95% Prediction Ellipse Area to quantify extreme spatial trajectory excursions. A single-subject dataset was processed as a technical proof-of-concept to verify the software pipeline’s execution and parameter calculation under empirical measurement conditions. Furthermore, automated extraction demonstrated the OER to be mathematically sensitive to transient spatial trajectory excursions, illustrating the methodological inconsistencies of comparing ambiguous sway area metrics across studies. By shifting from rigid data-upload services to an adaptable modular library, the OpenBalance framework provides an open-access infrastructure to support standardized signal processing and reproducible data analysis.