Digital Twin: recent advances in digital technologies for monitoring strength training performance

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dc.contributor.author Oberhofer K.
dc.contributor.author Achermann B.
dc.contributor.author Mytko A.
dc.contributor.author Nagorna V.
dc.contributor.author Lorenzetti S.
dc.date.accessioned 2022-12-02T09:57:39Z
dc.date.available 2022-12-02T09:57:39Z
dc.date.issued 2022-09-16
dc.identifier.udk http://reposit.uni-sport.edu.ua/handle/787878787/3998
dc.description Digital Twin: recent advances in digital technologies for monitoring strength training performance / K. Oberhofer, B. Achermann, A. Mytko, V. Nagorna, S. Lorenzetti // Молодь та олімпійський рух: зб. тез доповідей 15-ї Міжнар. наук. конф. [Інтернет], 16 вересня 2022 Вер 16. - Київ: НУФВСУ, 2022. - С. 42-43. uk_UA
dc.description.abstract Indeed, we could demonstrate the feasibility of fitting a standardised digital human model to 3D body surface data for subject-specific analysis of musculoskeletal physique. In parallel, we validated the accuracy of an iOS workout analysis application for the Apple Watch Sport to capture motion data in the strength-training specific setting. Exercise recognition and repetition count were found to be feasible using the iOS app; yet, further investigations are needed to derive the one repetition maximum as the most valid indicator of dynamic strength. Due to the fact that musculo skeletal modelling during strength training is rather robust, it is clearly possible to use such computational approaches to compare and design strength training exercises. uk_UA
dc.language.iso en uk_UA
dc.publisher Молодь та олімпійський рух uk_UA
dc.subject digital technologies uk_UA
dc.subject strength training uk_UA
dc.subject monitoring uk_UA
dc.title Digital Twin: recent advances in digital technologies for monitoring strength training performance uk_UA
dc.type Article uk_UA


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