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惯性测量单元传感器在职业高山滑雪运动员表现分析中的潜力

Potential of IMU Sensors in Performance Analysis of Professional Alpine Skiers.

作者信息

Yu Gwangjae, Jang Young Jae, Kim Jinhyeok, Kim Jin Hae, Kim Hye Young, Kim Kitae, Panday Siddhartha Bikram

机构信息

Industrial Engineering & Management Research Institute, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.

Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.

出版信息

Sensors (Basel). 2016 Apr 1;16(4):463. doi: 10.3390/s16040463.

Abstract

In this paper, we present an analysis to identify a sensor location for an inertial measurement unit (IMU) on the body of a skier and propose the best location to capture turn motions for training. We also validate the manner in which the data from the IMU sensor on the proposed location can characterize ski turns and performance with a series of statistical analyses, including a comparison with data collected from foot pressure sensors. The goal of the study is to logically identify the ideal location on the skier's body to attach the IMU sensor and the best use of the data collected for the skier. The statistical analyses and the hierarchical clustering method indicate that the pelvis is the best location for attachment of an IMU, and numerical validation shows that the data collected from this location can effectively estimate the performance and characteristics of the skier. Moreover, placement of the sensor at this location does not distract the skier's motion, and the sensor can be easily attached and detached. The findings of this study can be used for the development of a wearable device for the routine training of professional skiers.

摘要

在本文中,我们进行了一项分析,以确定惯性测量单元(IMU)在滑雪者身体上的传感器位置,并提出用于捕捉转弯动作以进行训练的最佳位置。我们还通过一系列统计分析,包括与从足部压力传感器收集的数据进行比较,验证了在所提议位置上的IMU传感器数据能够表征滑雪转弯和性能的方式。该研究的目标是从逻辑上确定滑雪者身体上安装IMU传感器的理想位置以及为滑雪者收集的数据的最佳用途。统计分析和层次聚类方法表明,骨盆是安装IMU的最佳位置,数值验证表明从该位置收集的数据能够有效估计滑雪者的性能和特征。此外,将传感器放置在该位置不会干扰滑雪者的动作,并且传感器易于安装和拆卸。本研究的结果可用于开发专业滑雪者日常训练的可穿戴设备。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfa0/4850977/c537c9603e72/sensors-16-00463-g010.jpg

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