Fine-Grained Action Analysis for Automated Skill Assessment and Feedback in Instructional Videos

Fine-grained action analysis in instructional videos presents significant challenges due to subtle motion variations and complex temporal dependencies. This paper introduces a comprehensive framework for automated skill assessment and feedback generation based on granularity-aware feature extraction and multi-modal fusion techniques. The proposed approach incorporates a temporal self-similarity module that captures periodic patterns critical for skill quality assessment, a part-level feature extraction network that analyzes body part movements, and a cross-attention transformer architecture that integrates skeleton and RGB modalities. Experiments conducted on our newly collected Skill Video dataset, comprising 8450 instructional videos across sports, crafts, medical procedures, and musical performances, demonstrate substantial improvements over state-of-the-art methods. The framework achieves 89.5% accuracy in skill level classification, a 20.1% reduction in dimensional assessment error, and a 5.8% improvement in temporal action quality estimation compared to existing approaches. User studies with 45 participants reveal that feedback generated by our system produces learning outcomes comparable to human expert guidance, with only a 3.6% gap in skill improvement and 2.6% difference in retention, as supported by rigorous experimental design and statistical analysis. The proposed technology enables personalized learning experiences through continuous assessment and feedback, with applications spanning formal education, professional training, and self-directed learning environments.