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使用惯性传感器估计楼梯跑步表现。

Estimating Stair Running Performance Using Inertial Sensors.

机构信息

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA.

出版信息

Sensors (Basel). 2017 Nov 17;17(11):2647. doi: 10.3390/s17112647.

Abstract

Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time.

摘要

楼梯跑,无论是上升还是下降,都是一种具有挑战性的有氧运动,许多运动员、休闲跑步者和士兵在训练中都会进行。由于使用基于光学的运动跟踪系统带来的实际挑战,对多级楼梯跑的生物力学研究一直受到限制。我们建议使用脚部安装的惯性测量单元(IMU)作为解决方案,因为它们可以在任何环境中不受限制地进行运动捕捉,并且不需要外部参考。特别是,本文提出了使用脚部安装的 IMU 在楼梯跑中估算脚部速度和轨迹的方法。计算方法利用站立阶段期间发生的静止期和已知的楼梯几何形状来估算脚部方向和轨迹,最终用于计算步幅指标。这些计算应用于人体参与者的楼梯跑数据,通过计时、轨迹、能量和力步幅指标揭示性能趋势。我们展示了对十一名受试者收集的实验数据进行分析的结果。总的来说,我们确定无论是上升还是下降,站立时间都是速度的最强预测指标,这一点从其与步幅时间的高度相关性就可以看出。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2f1/5713493/3d89cd51efb9/sensors-17-02647-g001.jpg

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