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使用惯性测量单元验证在跑步机上轮滑时的滑冰子技术中的时间参数。

Validation of temporal parameters within the skating sub-techniques when roller skiing on a treadmill, using inertial measurement units.

机构信息

Department of Informatics, Digital Signal Processing Group, University of Oslo, Oslo, Norway.

SINTEF Digital, Smart Sensor Systems, Oslo, Norway.

出版信息

PLoS One. 2022 Aug 18;17(8):e0270331. doi: 10.1371/journal.pone.0270331. eCollection 2022.

DOI:10.1371/journal.pone.0270331
PMID:35981009
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9387861/
Abstract

The aim of this study was to develop and validate a method using inertial measurements units (IMUs) to determine inner-cycle parameters (e.g., cycle, poles and skis contact, and swing time) and the main sub-techniques (i.e., G2, G3 and G4) in cross-country roller ski skating on a treadmill. The developed method is based on the detection of poles and skis initial and final contacts with the ground during the cyclic movements. Thirteen well-trained athletes skied at different combinations of speed (6-24 km∙h-1) and incline (2-14%) on a treadmill using the three different sub-techniques. They were equipped with IMUs attached to their wrists and skis. Their movements were tracked using reflective markers and a multiple camera infrared system. The IMU-based method was able to detect more than 99% of the temporal events. It calculated the inner-cycle temporal parameters with a precision ranging from 19 to 66 ms, corresponding to 3.0% to 7.8% of the corresponding inner-cycle duration. The obtained precision would likely allow differentiation of skiers on different performance levels and detection of technique changes due to fatigue. Overall, this laboratory validation provides interesting possibilities also for outdoor applications.

摘要

本研究旨在开发和验证一种使用惯性测量单元 (IMU) 来确定室内周期参数(例如,周期、杆和滑雪板接触以及摆动时间)和主要分项技术(即 G2、G3 和 G4)的方法,这些参数和分项技术适用于在跑步机上进行的越野滑旱冰运动。所开发的方法基于在循环运动期间检测杆和滑雪板与地面的初始和最终接触。13 名训练有素的运动员在跑步机上使用三种不同的分项技术以不同的速度(6-24 公里/小时)和坡度(2-14%)进行滑雪。他们的手腕和滑雪板上都配有 IMU。他们的运动使用反光标记和多摄像头红外系统进行跟踪。基于 IMU 的方法能够检测到超过 99%的时间事件。它计算了室内周期的时间参数,精度范围在 19 到 66 毫秒之间,对应于相应室内周期持续时间的 3.0%到 7.8%。获得的精度可能允许区分不同运动水平的运动员,并检测由于疲劳引起的技术变化。总的来说,这项实验室验证也为户外应用提供了有趣的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/5797b9be286d/pone.0270331.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/db5f6313dd11/pone.0270331.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/02fcd15b7fec/pone.0270331.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/02885c6a87ea/pone.0270331.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/f513ce06a0fa/pone.0270331.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/5797b9be286d/pone.0270331.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/db5f6313dd11/pone.0270331.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/fc1cdec8206d/pone.0270331.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/02fcd15b7fec/pone.0270331.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/02885c6a87ea/pone.0270331.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/f513ce06a0fa/pone.0270331.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ce5/9387861/5797b9be286d/pone.0270331.g006.jpg

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