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DC poleHodnotaJazyk
dc.contributor.authorCardenas, Kevin
dc.contributor.authorSemwal, Sudhanshu
dc.contributor.authorMaher, James
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-20T18:03:40Z-
dc.date.available2024-07-20T18:03:40Z-
dc.date.issued2024-
dc.identifier.citationJournal of WSCG. 2024, vol. 32, no. 1-2, p. 13-20.en
dc.identifier.issn1213 – 6972
dc.identifier.issn1213 – 6980 (CD-ROM)
dc.identifier.issn1213 – 6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/57340
dc.format8 s.cs_CZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectumělá inteligencecs
dc.subjectpočítačové viděnícs
dc.subjectinterakce člověka s počítačemcs
dc.subjectdetekce objektucs
dc.subjectodhad pozicecs
dc.subjectpredikce lidského pohybucs
dc.subjecthorolezectvícs
dc.titleEnd-to-End Move Prediction System for Indoor Rock Climbing: Beta Calleren
dc.typearticlecs_CZ
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.description.abstract-translatedWe developed Beta Caller, an end-to-end system supporting the sport of rock climbing for climbers with visual impairment. Beta Caller provides real-time, audible instructions containing a prediction for the climber’s next move while they are actively climbing a rock wall. This system leverages computer vision techniques to collect key information about the climber’s environment, enabling Beta Caller to make move predictions on climbing walls it has never encountered before. Neural networks are used to predict where the climber should move next, based on information provided by the computer vision models. The predicted move is translated into a verbal message guiding the climber to the next hold and then transmitted via wireless headphones using a text-to-speech model. This novel idea makes one of the fastest growing sports in the world even more appealing and approachable to climbers with visual impairment, however, this tool can be utilized by all climbers to improve their climbing skills. Beta Caller achieved 80.08% accuracy predicting which limb the climber should move next and, when predicting the location of the next hold, Beta Caller achieved a bounding box error of only 6.79%. These results pioneer a strong foundation shaping the future landscape of rock climbing prediction tools for visually impaired climbersen
dc.subject.translatedartificial intelligenceen
dc.subject.translatedcomputer visionen
dc.subject.translatedhuman computer interactionen
dc.subject.translatedobject detectionen
dc.subject.translatedpose estimationen
dc.subject.translatedhuman motion predictionen
dc.subject.translatedrock climbingen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2024.2
dc.type.statuspublishedVersionen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 32, number 1-2 (2024)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 32 (2024)
  6. Volume 32, number 1-2 (2024)