Evolving connectionist systems for adaptive sport coaching

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dc.contributor.authorBacic, B
dc.contributor.authorKasabov, N
dc.contributor.authorMacDonell, S
dc.contributor.authorPang, SN
dc.contributor.editorIshikawa, M
dc.contributor.editorDoya, K
dc.contributor.editorMiyamoto, H
dc.contributor.editorYamakawa, T
dc.date.accessioned2011-10-31T08:07:45Z
dc.date.available2011-10-31T08:07:45Z
dc.date.copyright2008
dc.date.issued2008
dc.description.abstractContemporary computer assisted coaching software operates either on a particular sub-space of the wider problem or requires expert(s) to operate and provide explanations and recommendations. This paper introduces a novel motion data processing methodology oriented to the provision of future generation sports coaching software. The main focus of investigation is the development of techniques that facilitate processing automation, incremental learning from initially small data sets, and robustness of architecture with a degree of interpretation on individual sport performers’ motion techniques. Findings from a case study using tennis motion data verify the prospect of building similar models and architectures for other sports or entertainment areas in which the aims are to improve human motion efficacy and to prevent injury. A central feature is the decoupling of the high-level analytical architecture from the low-level processing of motion data acquisition hardware, meaning that the system will continue to work with future motion acquisition devices.
dc.identifier.citationIn M. Ishikawa, K. Doya, H. Miyamoto & T. Yamakawa (Eds.), Evolving Connectionist Systems for Adaptive Sport Coaching, Neural Information Processing, Lecture Notes in Computer Science, vol.4985, pp. 416 - 425
dc.identifier.doi10.1007/978-3-540-69162-4_43
dc.identifier.isbn978-3-540-69159-4
dc.identifier.roid3946en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/2445
dc.publisherSpringer Berlin / Heidelberg
dc.relation.urihttp://dx.doi.org/10.1007/978-3-540-69162-4_43
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation)
dc.rights.accessrightsOpenAccess
dc.subjectClassification
dc.subjectCoaching rule
dc.subjectCREM
dc.subjectCoaching scenario
dc.subjectECOS
dc.subjectEFuNN
dc.subjectiB-fold
dc.subjectFeature extraction
dc.subjectLocal personalised global knowledge integration
dc.subjectOrchestration
dc.subjectWeighted sum
dc.subjectDiscovery
dc.titleEvolving connectionist systems for adaptive sport coaching
dc.typeChapter in Book
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/School of Computing & Mathematical Science
pubs.organisational-data/AUT/PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers/DCT C & M Computing
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