{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T07:10:59Z","timestamp":1773213059983,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T00:00:00Z","timestamp":1605484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["20K23352"],"award-info":[{"award-number":["20K23352"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s20226534","type":"journal-article","created":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T21:48:52Z","timestamp":1605563332000},"page":"6534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6764-4861","authenticated-orcid":false,"given":"Ran","family":"Dong","sequence":"first","affiliation":[{"name":"School of Computer Science, Tokyo University of Technology, Tokyo 192-0982, Japan"}]},{"given":"Dongsheng","family":"Cai","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Information and Systems, University of Tsukuba, Ibaraki 305-8577, Japan"}]},{"given":"Soichiro","family":"Ikuno","sequence":"additional","affiliation":[{"name":"School of Computer Science, Tokyo University of Technology, Tokyo 192-0982, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,16]]},"reference":[{"key":"ref_1","first-page":"142","article-title":"A survey of advances in vision-based human motion capture and analysis","volume":"10","author":"Moeslund","year":"2008","journal-title":"Comput. 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