Signal-Based Assessment of Postural Stability Using ECG, EMG, and Center of Pressure Measures

Postural stability reflects the integrated function of autonomic, neuromuscular, and postural control systems and deteriorates with aging and reduced physical activity. Although electrocardiography(ECG), electromyography(EMG), and center of pressure(COP) measures have each been used to characterize functional changes, their relative measurement sensitivity across different task conditions remains insufficiently understood when multiple physiological modalities are evaluated within the same cohort. This study compared the relative measurement sensitivity of ECG-, EMG-, and COP-derived physiological features for signal-based assessment of postural stability across static and dynamic task conditions. A total of 110 women were classified into young(YG, n=30), active older(AOG, n=57), and non-active older(NOG, n=23) groups. Resting ECG, grip-force EMG during maximal voluntary contractions, and COP signals during side-by-side stance, semitandem stance, and the five-times sit-to-stand(FTSS) task were collected. Time-and frequency-domain features were extracted from each modality. Group differences were evaluated using statistical analyses. Machine-learning models were employed as analytical tools to quantify the relative measurement sensitivity of modality-and task-specific feature sets, rather than as diagnostic classifiers. Physiological features derived from all three modalities demonstrated significant group-related differences(p < 0.001). COP-derived features exhibited pronounced task dependency, with substantially greater sensitivity during the dynamic FTSS task compared with static stance conditions. When measurement sensitivity was quantified using machinelearning-based discriminative capacity, COP features obtained during FTSS showed the highest sensitivity, followed by COP features during semi-tandem stance, ECG-derived HRV features, and EMG frequency-domain features.The measurement sensitivity of signal-based postural stability assessment is strongly influenced by both physiological signal selection and task design. COP-derived features, particularly those obtained during dynamic functional tasks such as FTSS, provide the highest sensitivity to functional variation, while ECG-and EMG-derived features offer complementary information on autonomic and neuromuscular contributions. These findings support a measurement-oriented framework for selecting appropriate physiological signals and task conditions for quantitative postural stability assessment in aging populations.