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

• Main Authors: Ran Dong, Dongsheng Cai, Soichiro Ikuno
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Sensors
• Subjects: motion capture data; motion analysis; motion primitive; feature extraction; Hilbert-Huang transform; empirical mode decomposition
• Online Access: https://www.mdpi.com/1424-8220/20/22/6534

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.