Classifying action correctness in physical rehabilitation exercises
This addresses the challenge of automated quality control in rehabilitation for patients, but it is incremental as it highlights limitations without introducing a new solution.
The paper tackled the problem of assessing correctness in physical rehabilitation exercises using machine learning, finding that while algorithms can produce good results for certain actions, they often misclassify incorrect executions as correct ones.
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.