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.
The FINA09 diving dataset was introduced in @cite_4 . They carry out an initial investigation and conclude that temporal information, implicitly present in videos, is very important and that HOG features can be used for pose estimation. , addressed the issue of quality assessment @cite_1 . They developed an approach in which the system is trained on an action dataset which has scores, for quality, marked by humans. Once trained, the system can assess sample and output a score for quality between 0 and 100. They tested their system on Olympic diving dataset in which the scores were given by judges.
@cite_1 @cite_4
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Estimating action quality, the process of assigning a "score" to the execution of an action, is crucial in areas such as sports and health care. Unlike action recognition, which has millions of examples to learn from, the action quality datasets that are currently available are small -- typically comprised of only a few hundred samples. This work presents three frameworks for evaluating Olympic sports which utilize spatiotemporal features learned using 3D convolutional neural networks (C3D) and perform score regression with i) SVR, ii) LSTM, and iii) LSTM followed by SVR. An efficient training mechanism for the limited data scenarios is presented for clip-based training with LSTM. The proposed systems show significant improvement over existing quality assessment approaches on the task of predicting scores of Olympic events diving, vault, figure skating . While the SVR-based frameworks yield better results, LSTM-based frameworks are more natural for describing an action and can be used for improvement feedback. This work presents a unique new dataset and objectives for action analysis. The data presents 3 key challenges: tracking, classification, and judging action quality. The last of these, to our knowledge, has not yet been attempted in the vision literature as applied to sports where technique is scored. This work performs an initial analysis of the dataset with classification experiments, confirming that temporal information is more useful than holistic bag-of-features style analysis in distinguishing dives. Our investigation lays a groundwork of effective tools for working with this type of sports data for future investigations into judging the quality of actions.