Exploring Feedback Generation in Automated Skeletal Movement Assessment:
  A Comprehensive Overview
Tal Hakim
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 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. 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.