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Basketball activity classification can help document players' statistics, allow coaches, trainers and the medical team to quantitatively supervise players' physical exertion and optimize training strategy, and further help prevent potential injuries. Traditionally, sports activity classification was done by manual notational, or through multi-camera systems or motion sensing technology. These methods were often erroneous and limited by space. This study presents a basketball activity classification model based on Dynamic Time Warping (DTW) and body kinematic measures. Twenty participants, including 10 experienced players and 10 novice players, were involved in an experimental study. The experienced and novice players differed in their years of playing basketball. Four basketball movements, including shooting, passing, dribbling, and lay-up were classified by kinematic measures. The results indicate that the proposed model can successfully classify different basketball movements with high accuracy and efficiency. Specifically, with the resultant acceleration of the hand, this model can achieve classification precision, recall, and specificity up to 0.984, 0.983 and 0.994, respectively. Findings from this study supported the feasibility of using DTW in real-time sports activity classification and provided insights into the optimal sensor placement for basketball activity classification applications.
This article was published in the following journal.
Name: International journal of sports medicine
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The World Health Organization's classification categories of health and health-related domains. The International Classification of Functioning, Disability and Health (ICF) consists of two lists: a list of body functions and structure, and a list of domains of activity and participation. The ICF also includes a list of environmental factors.
The upper part of the human body, or the front or upper part of the body of an animal, typically separated from the rest of the body by a neck, and containing the brain, mouth, and sense organs.
Systematic study of the body and the use of its static and dynamic position as a means of communication.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
Developmental abnormalities in any portion of the ATRIAL SEPTUM resulting in abnormal communications between the two upper chambers of the heart. Classification of atrial septal defects is based on location of the communication and types of incomplete fusion of atrial septa with the ENDOCARDIAL CUSHIONS in the fetal heart. They include ostium primum, ostium secundum, sinus venosus, and coronary sinus defects.