TY - JOUR
T1 - Movement Symmetry Assessment by Bilateral Motion Data Fusion
AU - Ren, Peng
AU - Hu, Shiang
AU - Han, Zhenfeng
AU - Wang, Qing
AU - Yao, Shuxia
AU - Gao, Zhao
AU - Jin, Jiangming
AU - Bringas, Maria L.
AU - Yao, Dezhong
AU - Biswal, Bharat
AU - Valdes-Sosa, Pedro A.
N1 - Funding Information:
Manuscript received February 5, 2018; revised April 12, 2018; accepted April 15, 2018. Date of publication April 24, 2018; date of current version December 19, 2018. This work was supported in part by the National Natural Science Foundation of China under Grant 81601585, Grant 81330032, and Grant 61673090 and in part by the Fundamental Research Funds for the Central Universities China. (Corresponding authors: Peng Ren, Bharat Biswal, and Pedro A. Valdes-Sosa.) P. Ren is with the Clinical Hospital, Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China (e-mail: pren28@uestc.edu.cn).
Publisher Copyright:
© 1964-2012 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/1
Y1 - 2019/1
N2 - 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.
AB - 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.
KW - Canonical correlation analysis (CCA)
KW - Poincaré plot, RReliefF
KW - data fusion
KW - discrete wavelet transforms (DWT)
KW - joint independent component analysis (jICA)
KW - latent variable
KW - movement symmetry
KW - multiresolution analysis
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U2 - 10.1109/TBME.2018.2829749
DO - 10.1109/TBME.2018.2829749
M3 - Article
C2 - 29993408
AN - SCOPUS:85045992394
SN - 0018-9294
VL - 66
SP - 225
EP - 236
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 1
M1 - 8345582
ER -