RESC: A Rubric-Guided Error–Score Consistency Framework for Action Quality Assessment in Vocational Fitter Training

Abstract

Action quality assessment in vocational fitter training is constrained by limited expert-annotated data and insufficiently interpretable predictions. To achieve accurate scoring and rubric-aligned, auditable explanations with limited data, this study proposes RESC, a rubric-guided error–score consistency framework. RESC uses human pose as its primary visual evidence and converts an instructor rubric into error semantics and scoring constraints. It therefore predicts an overall score while producing rubric-aligned error diagnoses and model-derived itemized deduction contributions. We constructed Fitter-AQA, a dataset of 134 filing videos from 25 participants, and evaluated the framework using strict leave-one-subject-out cross-validation. RESC achieved a mean absolute error of 4.450, a Pearson linear correlation coefficient of 0.902, and a Spearman rank correlation coefficient of 0.852, corresponding to the lowest pooled scoring errors and highest pooled correlations among the evaluated methods. Error diagnosis reached a Macro-F1 of 0.770 and a macro-average balanced accuracy of 0.824. These results indicate stable cross-subject scoring under limited-data conditions and transform model predictions into an auditable score decomposition aligned with rubric semantics. RESC thus provides an accurate and interpretable approach to automated assessment in vocational skills training.