Hierarchical Biomarker Modeling for Facial Paralysis Assessment and Rehabilitation Monitoring.

Facial paralysis assessment and rehabilitation monitoring require not only accurate analysis of facial biomarkers but also a clinically deployable workflow that spans standardized data acquisition, quantitative modeling, and interpretable reporting. We present a clinically implemented facial paralysis assessment system that integrates four modules: a Data Acquisition module for standardized 4K/120 fps video recording, a Key Point Extraction and Indicator Calculation module that derives 313 fine-grained indicators from 11 standardized facial actions, an Analysis module built on the Hierarchical Dynamic Attention Patient-Adaptive Network (HiDAPA), and a Result Visualization Report Generation module for interpretable clinical reporting. At the core of the system, HiDAPA models biomarker importance in a hierarchical and personalized manner. It organizes the 313 indicators into a three-level representation to capture both structured biomarker relationships and individual heterogeneity. Using Sunnybrook scores as a clinical reference, experiments on 200 subjects show that HiDAPA achieves a Pearson correlation of 0.980 and an Acc@10 of 90.5%. The learned importance patterns are clinically interpretable, highlighting the dominance of symmetry-related indicators and spontaneous blink asymmetry. The system enables an objective and interpretable assessment of facial paralysis and supports long-term monitoring of rehabilitation progress.