  Human action recognition and performance assessment have been hot research
topics in recent years. Recognition problems have mature solutions in the field
of sign language, but past research in performance analysis has focused on
competitive sports and medical training, overlooking the scoring assessment
,which is an important part of sign language teaching digitalization. In this
paper, we analyze the existing technologies for performance assessment and
adopt methods that perform well in human pose reconstruction tasks combined
with motion rotation embedded expressions, proposing a two-stage sign language
performance evaluation pipeline. Our analysis shows that choosing
reconstruction tasks in the first stage can provide more expressive features,
and using smoothing methods can provide an effective reference for assessment.
Experiments show that our method provides good score feedback mechanisms and
high consistency with professional assessments compared to end-to-end
evaluations.
