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Title: Movement symmetry assessment by bilateral motion data fusion
Authors: Ren, Peng
Hu, Shiang
Han, Zhenfeng
Wang, Qing
Yao, Shuxia
Gao, Zhao
Jin, Jiangming
Bringas, Maria L.
Yao, Dezhong
Biswal, Bharat
Valdes-Sosa, Pedro A.
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Ren, 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.2018.2829749
Journal: IEEE Transactions on Biomedical Engineering
Abstract: Objective: 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.
ISSN: 1558-2531
DOI: 10.1109/TBME.2018.2829749
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:
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Fulltext Availability: No Fulltext
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