Measuring the Quality of Exercises

TL;DR

This paper investigates automated methods to assess the quality of large amplitude movement exercises for cerebral palsy treatment, comparing machine learning techniques to classify exercises as good or bad.

Contribution

It introduces a machine learning framework for automated exercise quality assessment, demonstrating high accuracy with AdaBoosted trees.

Findings

AdaBoosted tree achieved 94.68% accuracy

Support vector machines and neural networks were also evaluated

Automated classification is feasible for exercise quality measurement

Abstract

This work explores the problem of exercise quality measurement since it is essential for effective management of diseases like cerebral palsy (CP). This work examines the assessment of quality of large amplitude movement (LAM) exercises designed to treat CP in an automated fashion. Exercise data was collected by trained participants to generate ideal examples to use as a positive samples for machine learning. Following that, subjects were asked to deliberately make subtle errors during the exercise, such as restricting movements, as is commonly seen in cases of patients suffering from CP. The quality measurement problem was then posed as a classification to determine whether an example exercise was either "good" or "bad". Popular machine learning techniques for classification, including support vector machines (SVM), single and doublelayered neural networks (NN), boosted decision trees, and dynamic time warping (DTW), were compared. The AdaBoosted tree performed best with an accuracy of 94.68% demonstrating the feasibility of assessing exercise quality.