Capturing Compensatory Reserve in Sarcopenia: A Bioengineering Framework for Multidimensional Temporal Analysis of Center-of-Pressure Signals

TL;DR

This study introduces a new method to detect balance issues in sarcopenia patients by analyzing subtle changes in their center-of-pressure signals.

Contribution

A novel bioengineering framework using multidimensional temporal analysis of COP signals to quantify compensatory reserve in sarcopenia.

Findings

Semi-tandem stance COP analysis achieved 0.84 ± 0.04 accuracy in distinguishing sarcopenia patients from controls.

DTW-based features were identified as key indicators of compensatory reserve and correlated with clinical severity.

The framework outperformed conventional kinematic features in detecting balance impairments.

Abstract

Conventional balance assessments often miss subtle deficits in sarcopenia patients due to compensatory strategies. This study develops a computational framework using multidimensional temporal analysis of center-of-pressure (COP) signals to quantify variations in compensatory reserve—the capacity to mask balance impairments—within these patients. COP data were collected from 82 older adults (sarcopenia vs. controls) during static standing on a standard clinical force platform (routine for geriatric balance testing). The framework integrates Dynamic Time Warping distances from a healthy template, fixed-weight LSTM embeddings, and statistical metrics, with feature selection and 5-fold cross-validation (SMOTE) to mitigate overfitting. Semi-tandem stance was most discriminative, achieving 0.84 ± 0.04 accuracy and 0.86 ± 0.05 ROC-AUC—outperforming conventional kinematic features. SHAP analysis identified DTW-based features as primary drivers, correlating with clinical severity indicators, while intra-group variability in prediction probabilities indicated a compensatory reserve gradient. This study introduces a feasible bioengineering methodology based on clinical COP platform analysis, laying the groundwork for future validation and translation into routine clinical assessment tools.

Figures