Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.

Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.
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基于希尔伯特-黄变换的运动捕捉数据瞬时频域分析

DOI:
10.3390/s20226534
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发表时间:
2020-11-16
期刊:
Sensors (Basel, Switzerland)
影响因子:
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通讯作者:
Ikuno S
Ikuno S
中科院分区:
其他
文献类型:
--
作者:
Dong R;Cai D;Ikuno S

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运动捕捉数据被广泛应用于医疗、娱乐、工业等不同研究领域。然而,大多数使用运动捕捉数据的运动研究都是在时间域进行的。为了理解人体运动的复杂性,需要对运动数据进行频域分析。为了分析人体运动,提出了一种利用希尔伯特-黄变换(HHT)将运动变换到瞬时频域的方法。经验模式分解(EMD)是HHT的一部分,它将从实际实验中获取的非平稳和非线性信号分解成伪单色信号,即所谓的固有模式函数(IMF)。我们的研究表明,多变量EMD可以将复杂的人体运动分解成与不同运动基元相对应的有限个非线性模式(IMF)。对这些分解后的运动进行希尔伯特谱分析,可以在瞬时频域中提取运动特征并进行可视化。例如,我们将我们的框架应用于(1)跳跃动作、(2)脚部受伤的步态和(3)高尔夫挥杆动作。
Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.
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