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Classifying action correctness in physical rehabilitation exercises

arXiv 2108.01375 ↗First posted 3 Aug 2021cs.CV cs.LGcited by 0

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Abstract (arXiv)

The work in this paper focuses on the role of machine learning in assessing the correctness of a human motion or action. This task proves to be more challenging than the gesture and action recognition ones. We will demonstrate, through a set of experiments on a recent dataset, that machine learning algorithms can produce good results for certain actions, but can also fall into the trap of classifying an incorrect execution of an action as a correct execution of another action.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 3 Aug 2021.

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6 of 19 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysiscited by 686
  3. 2016The THUMOS Challenge on Action Recognition for Videos "in the Wild"cited by 213
  4. 2016An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Datacited by 126
  5. 2017Interpretable 3D Human Action Analysis with Temporal Convolutional Networkscited by 89
  6. 2017Qualitative Assessment of Recurrent Human Motioncited by 1

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