To address the lack of measurement devices and limited data indicators in current sit-to-stand (STS) balance assessments, a novel ergonomic measurement device was developed to capture multi-point vertical pressure signals from the hands, feet, and hips during the STS process. The system enables synchronized acquisition of time-series pressure data across key body regions. To represent a typical gradient of balance abilities from normal to various levels of decline, a total of 85 participants were recruited, including young adults, community-dwelling older adults, and elderly patients from rehabilitation hospitals. Raw multi-channel pressure signals were collected during the STS movement and preprocessed using filtering, summation, segmentation, normalization, and phase division. Fifty-five candidate features were extracted based on time, force, and center-of-pressure dimensions. After univariate testing, collinearity elimination, and forward stepwise regression, three key features were selected: rate of force development in the feet, pressure-time integral of the hands, and minimum foot pressure. An ordinal logistic regression model was constructed using these features to classify balance ability levels, and its performance was evaluated via 10-times repeated 5-fold cross-validation. The model achieved an AIC of 39.20, a Pseudo R² of 0.84, and an average classification accuracy of 91.06%, with all three variables showing statistical significance (P < 0.05). Regression analysis revealed that higher foot force development rate and higher minimum foot pressure were associated with better balance, while a higher hand pressure-time integral indicated poorer balance, reflecting compensatory reliance on upper limbs. This study demonstrates that STS time-series pressure data collected by the proposed measurement device, combined with ordinal logistic modeling, can enable accurate balance classification and offers a promising new method for evaluating balance ability in older adults.