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

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arXiv:2404.09359v2 Announce Type: replace
Abstract: 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.

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