Publication: Beach volleyball serve type recognition
| dc.contributor.author | L. Ponce Cuspinera | en_US |
| dc.contributor.author | Sakura Uetsuji | en_US |
| dc.contributor.author | F. J.Ordonez Morales | en_US |
| dc.contributor.author | Daniel Roggen | en_US |
| dc.contributor.other | University of Sussex | en_US |
| dc.contributor.other | Mahidol University | en_US |
| dc.date.accessioned | 2018-12-11T02:37:36Z | |
| dc.date.accessioned | 2019-03-14T08:04:30Z | |
| dc.date.available | 2018-12-11T02:37:36Z | |
| dc.date.available | 2019-03-14T08:04:30Z | |
| dc.date.issued | 2016-09-12 | en_US |
| dc.description.abstract | © 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. | en_US |
| dc.identifier.citation | International Symposium on Wearable Computers, Digest of Papers. Vol.12-16-September-2016, (2016), 44-45 | en_US |
| dc.identifier.doi | 10.1145/2971763.2971781 | en_US |
| dc.identifier.other | 2-s2.0-84989295869 | en_US |
| dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/20.500.14594/43447 | |
| dc.rights | Mahidol University | en_US |
| dc.rights.holder | SCOPUS | en_US |
| dc.source.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84989295869&origin=inward | en_US |
| dc.subject | Computer Science | en_US |
| dc.title | Beach volleyball serve type recognition | en_US |
| dc.type | Conference Paper | en_US |
| dspace.entity.type | Publication | |
| mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84989295869&origin=inward | en_US |