Beach volleyball serve type recognition

© 2016 ACM. We present results on beach volleyball serve recognition and classification from a wrist-worn gyroscope deployed with semi-professional beach volleyball players. We trained a template-based recognition system based on a Warping Longest Common Subsequence algorithm to spot serves, and pot...

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Main Authors: L. Ponce Cuspinera, Sakura Uetsuji, F. J.Ordonez Morales, Daniel Roggen
Other Authors: University of Sussex
Format: Conference or Workshop Item
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/43447
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Institution: Mahidol University
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spelling th-mahidol.434472019-03-14T15:04:30Z Beach volleyball serve type recognition L. Ponce Cuspinera Sakura Uetsuji F. J.Ordonez Morales Daniel Roggen University of Sussex Mahidol University Computer Science © 2016 ACM. We present results on beach volleyball serve recognition and classification from a wrist-worn gyroscope deployed with semi-professional beach volleyball players. We trained a template-based recognition system based on a Warping Longest Common Subsequence algorithm to spot serves, and potentially distinguish among 4 common serve types. This shows the potential of wearable technologies in beach volleyball, which could offer precise sport analytics. 2018-12-11T02:37:36Z 2019-03-14T08:04:30Z 2018-12-11T02:37:36Z 2019-03-14T08:04:30Z 2016-09-12 Conference Paper International Symposium on Wearable Computers, Digest of Papers. Vol.12-16-September-2016, (2016), 44-45 10.1145/2971763.2971781 2-s2.0-84989295869 https://repository.li.mahidol.ac.th/handle/123456789/43447 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84989295869&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Computer Science
spellingShingle Computer Science
L. Ponce Cuspinera
Sakura Uetsuji
F. J.Ordonez Morales
Daniel Roggen
Beach volleyball serve type recognition
description © 2016 ACM. We present results on beach volleyball serve recognition and classification from a wrist-worn gyroscope deployed with semi-professional beach volleyball players. We trained a template-based recognition system based on a Warping Longest Common Subsequence algorithm to spot serves, and potentially distinguish among 4 common serve types. This shows the potential of wearable technologies in beach volleyball, which could offer precise sport analytics.
author2 University of Sussex
author_facet University of Sussex
L. Ponce Cuspinera
Sakura Uetsuji
F. J.Ordonez Morales
Daniel Roggen
format Conference or Workshop Item
author L. Ponce Cuspinera
Sakura Uetsuji
F. J.Ordonez Morales
Daniel Roggen
author_sort L. Ponce Cuspinera
title Beach volleyball serve type recognition
title_short Beach volleyball serve type recognition
title_full Beach volleyball serve type recognition
title_fullStr Beach volleyball serve type recognition
title_full_unstemmed Beach volleyball serve type recognition
title_sort beach volleyball serve type recognition
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/43447
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