ArticleSensors (Basel, Switzerland)2020
Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Reduced-order analysis for gait kinematics of paraplegic patients based on spatiotemporal mode decomposition.Medical & biological engineering & computing · 2026Article
- Advancing Parkinson's disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics.Frontiers in physiology · 2025Article
- Biomechanical Analysis of Golf Swing Motion Using Hilbert-Huang Transform.Sensors (Basel, Switzerland) · 2023Article
- Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert-Huang transform of electroencephalography during general anesthesia: a retrospective observational study.BMC anesthesiology · 2023Observational
- Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks.Sensors (Basel, Switzerland) · 2022Review
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Authors and funding
3 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.