Classifying action correctness in physical rehabilitation exercises

TL;DR

This paper explores machine learning methods to evaluate the correctness of physical rehabilitation exercises, highlighting challenges and potential for accurate classification with some limitations in distinguishing similar incorrect actions.

Contribution

It demonstrates the effectiveness and limitations of machine learning algorithms in classifying correct and incorrect physical rehabilitation exercises.

Findings

Machine learning can classify some actions accurately.

Algorithms may misclassify incorrect actions as correct.

Performance varies across different exercises.

Abstract

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.