The objective of this study was to establish a quantitative balance assessment model for older adults by integrating high-frequency force platform sensor data with grey relational analysis to evaluate morphological similarity. Utilizing a 1000 Hz sampling rate, we extracted seven temporal and five weight-related balance factors during sit-to-stand tasks. Data were collected from 60 participants, including 22 healthy young adults—serving as a normative baseline—and 38 older adults. On the basis of these seven time factors, five weight factors, and clinical indicators derived from the Berg Balance Scale and body mass index, three 2D balance assessment standards were formulated, subsequently leading to the derivation of a comprehensive 3D assessment norm. This 3D assessment norm visualizes individual balance performance through specific functional quadrants and may help identify possible compensatory adaptations and functional differences that traditional scales may overlook. The results suggest that the proposed 3D assessment norm provides a standardized visual platform for characterizing individual balance performance and may support future validation-oriented studies and AI-assisted support systems.