  The application of machine-learning solutions to movement assessment from
skeleton videos has attracted significant research attention in recent years.
This advancement has made rehabilitation at home more accessible, utilizing
movement assessment algorithms that can operate on affordable equipment for
human pose detection and analysis from 2D or 3D videos. While the primary
objective of automatic assessment tasks is to score movements, the automatic
generation of feedback highlighting key movement issues has the potential to
significantly enhance and accelerate the rehabilitation process. While numerous
research works exist in the field of automatic movement assessment, only a
handful address feedback generation. In this study, we explain the types of
feedback that can be generated, review existing solutions for automatic
feedback generation, and discuss future research directions. To our knowledge,
this is the first comprehensive review of feedback generation in skeletal
movement assessment.
