Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145337
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dc.contributor.authorRen, Pengen_US
dc.contributor.authorHu, Shiangen_US
dc.contributor.authorHan, Zhenfengen_US
dc.contributor.authorWang, Qingen_US
dc.contributor.authorYao, Shuxiaen_US
dc.contributor.authorGao, Zhaoen_US
dc.contributor.authorJin, Jiangmingen_US
dc.contributor.authorBringas, Maria L.en_US
dc.contributor.authorYao, Dezhongen_US
dc.contributor.authorBiswal, Bharaten_US
dc.contributor.authorValdes-Sosa, Pedro A.en_US
dc.date.accessioned2020-12-17T07:22:35Z-
dc.date.available2020-12-17T07:22:35Z-
dc.date.issued2019-
dc.identifier.citationRen, P., Hu, S., Han, Z., Wang, Q., Yao, S., Gao, Z., ... Valdes-Sosa, P. A. (2019). Movement symmetry assessment by bilateral motion data fusion. IEEE Transactions on Biomedical Engineering, 66(1), 225-236. doi:10.1109/TBME.2018.2829749en_US
dc.identifier.issn1558-2531en_US
dc.identifier.urihttps://hdl.handle.net/10356/145337-
dc.description.abstractObjective: A new approach, named bilateral motion data fusion, was proposed for the analysis of movement symmetry, which takes advantage of cross-information between both sides of the body and processes the unilateral motion data at the same time. Methods: This was accomplished using canonical correlation analysis and joint independent component analysis. It should be noted that human movements include many categories, which cannot be enumerated one by one. Therefore, the gait rhythm fluctuations of the healthy subjects and patients with neurodegenerative diseases were employed as an example for method illustration. In addition, our model explains the movement data by latent parameters in the time and frequency domains, respectively, which were both based on bilateral motion data fusion. Results: They show that our method not only reflects the physiological correlates of movement but also obtains the differential signatures of movement asymmetry in diverse neurodegenerative diseases. Furthermore, the latent variables also exhibit the potentials for sharper disease distinctions. Conclusion: We have provided a new perspective on movement analysis, which may prove to be a promising approach. Significance: This method exhibits the potentials for effective movement feature extractions, which might contribute to many research fields such as rehabilitation, neuroscience, biomechanics, and kinesiology.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Biomedical Engineeringen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TBME.2018.2829749en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleMovement symmetry assessment by bilateral motion data fusionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1109/TBME.2018.2829749-
dc.identifier.pmid29993408-
dc.identifier.issue1en_US
dc.identifier.volume66en_US
dc.identifier.spage225en_US
dc.identifier.epage236en_US
dc.subject.keywordsData Integrationen_US
dc.subject.keywordsStandardsen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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