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 |