> Merkliste Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
Medientyp: E-Artikel Titel: Detection and recognition of human body posture in motion based on sensor technology Beteiligte: Zhao, Lei; Chen, Wenjing Erschienen: Wiley, 2020 Erschienen in: IEEJ Transactions on Electrical and Electronic Engineering Sprache: Englisch DOI: 10.1002/tee.23113 ISSN: 1931-4973; 1931-4981 Entstehung: Anmerkungen: Beschreibung: <jats:p>The recognition of human motion posture is of great values in the field of sports. In this paper, inertial sensor technology is employed to recognize four postures including dribbling, passing, catching, and shooting in basketball. The data are collected by four nine‐axis inertial sensors worn on the arm. The time domain and frequency domain features are extracted after the process of smoothing and normalizing. Then 30 eigenvectors are obtained by dimensionality reduction of principal component analysis (PCA), and the support vector machine (SVM) method is used for posture recognition. The experimental results show that after PCA dimensionality reduction, the recognition accuracy of the features as the SVM input is significantly high. Compared with the recognition accuracy of back propagation neural network (BPNN) (85.4%), the average recognition accuracy of SVM is 96%, which verifies the reliability of the method proposed in this study. This research proves the effectiveness of sensor technology in basketball posture recognition, which can provide a reliable guidance for basketball training. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.</jats:p>