Measuring the Quality of Exercises
This work addresses the need for automated exercise quality assessment in disease management for cerebral palsy patients, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automatically measuring the quality of large amplitude movement exercises for cerebral palsy treatment by classifying exercises as 'good' or 'bad' using machine learning, achieving a best accuracy of 94.68% with an AdaBoosted tree.
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.