Mahanth K. Gowda, Ashutosh Dhekne, Sheng Shen, Romit Roy Choudhury, Sharon Xue Yang, Lei Yang, S. Golwalkar, Alexander Essanian
{"title":"IoT Platform for Sports Analytics","authors":"Mahanth K. Gowda, Ashutosh Dhekne, Sheng Shen, Romit Roy Choudhury, Sharon Xue Yang, Lei Yang, S. Golwalkar, Alexander Essanian","doi":"10.1145/3191789.3191793","DOIUrl":null,"url":null,"abstract":"This paper is an experience report on IoT platforms for sports analytics. In our prior work [11], we proposed iBall, a system that explores the possibility of bringing IoT to sports analytics, particularly to the game of Cricket. iBall develops solutions to track a ball's 3D trajectory and spin with inexpensive sensors and radios embedded in the ball. Towards this end, iBall performs fusion of wireless and inertial sensory data and integrates them into physics-based motion models of a ball in flight. The median ball location error is at 8cm while rotational error remains below 12° even at the end of the flight. The results do not rely on training, hence we expect the core techniques to extend to other sports like baseball, with some domain-specific modifications.","PeriodicalId":213775,"journal":{"name":"GetMobile Mob. Comput. Commun.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GetMobile Mob. Comput. Commun.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3191789.3191793","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper is an experience report on IoT platforms for sports analytics. In our prior work [11], we proposed iBall, a system that explores the possibility of bringing IoT to sports analytics, particularly to the game of Cricket. iBall develops solutions to track a ball's 3D trajectory and spin with inexpensive sensors and radios embedded in the ball. Towards this end, iBall performs fusion of wireless and inertial sensory data and integrates them into physics-based motion models of a ball in flight. The median ball location error is at 8cm while rotational error remains below 12° even at the end of the flight. The results do not rely on training, hence we expect the core techniques to extend to other sports like baseball, with some domain-specific modifications.