To address the lack of measurement devices and limited data indicators in current sit-to-stand (STS) balance assessments, this study developed a novel ergonomic measurement device 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 ability from normal to various levels of decline, 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. 55 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 obtained: 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 results show that the model achieves an Akaike Information Criterion (AIC) value of 39.195 and a Pseudo R² of 0.843; that three variables, namely, foot force development rate, hand pressure-time integral, and minimum foot pressure, are all statistically significant (P < 0.05); and that the average classification accuracy from repeated cross-validation is 91.06%, indicating good model fit and robust predictive performance. Moreover, regression analysis results reveal that higher foot force development rate and higher minimum foot pressure are associated with better balance, while a higher hand pressure-time integral indicates poorer balance ability. Using the sit-to-stand ergonomic measurement device designed in this study to collect time-series data and performing ordinal Logistic regression modeling can achieve relatively accurate classification of balance ability, offering a potential new method for assessing balance ability in older adults.