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Staes, \u201cEfficacy measures associated to a plantar pressure based classification system in diabetic foot medicine,\u201d Gait & posture, vol.49, pp.168-175, 2016. 10.1016\/j.gaitpost.2016.07.009","DOI":"10.1016\/j.gaitpost.2016.07.009"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] C.J. Bennetts, T.M. Owings, A. Erdemir, G. Botek, and P.R.Cavanagh, \u201cClustering and classification of regional peak plantar pressures of diabetic feet,\u201d Journal of biomechanics, vol.46, pp.19-25, 2013. 10.1016\/j.jbiomech.2012.09.007","DOI":"10.1016\/j.jbiomech.2012.09.007"},{"key":"5","unstructured":"[5] L. Cao, N. Dey, A.S. Ashour, et al., \u201cDiabetic plantar pressure analysis using image fusion,\u201d Multimedia Tools and Applications, pp.1-24, 2018."},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] Y. Xia, Y. Li, L. Xun, Q. Yan, and D. Zhang, \u201cA convolutional neural network Cascade for plantar pressure images registration,\u201d Gait & posture, vol.68, pp.403-408, 2019. 10.1016\/j.gaitpost.2018.12.021","DOI":"10.1016\/j.gaitpost.2018.12.021"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] S. Ostadabbas, M. Nourani, A. Saeed, R. Yousefi, and M. Pompeo, \u201cA knowledge-based modeling for plantar pressure image reconstruction,\u201d IEEE Trans. Biomed. Eng., vol.61, no.10, pp.2538-2549, 2014. 10.1109\/tbme.2014.2322993","DOI":"10.1109\/TBME.2014.2322993"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] S.A. Bus, \u201cInnovations in plantar pressure and foot temperature measurements in diabetes,\u201d Diabetes\/metabolism research and reviews, vol.32, pp.221-226, 2016. 10.1002\/dmrr.2760","DOI":"10.1002\/dmrr.2760"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] K. Cheng, Y. Zhang, X. He, W. Chen, and J. Cheng, \u201cSkeleton-based action recognition with shift graph convolutional network,\u201d Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.183-192, 2020. 10.1109\/cvpr42600.2020.00026","DOI":"10.1109\/CVPR42600.2020.00026"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] P. Jia, Q. Zhao, B. Li, and J. Zhang, \u201cCJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition,\u201d IEICE Trans. Inf. & Syst., vol.E104-D, no.8, pp.1239-1249, Aug. 2021. 10.1587\/transinf.2020bdp0010","DOI":"10.1587\/transinf.2020BDP0010"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] N. Mukojima, M. Yasugi, Y. Mizutani, et al., \u201cDeep-learning-assisted single-pixel imaging for gesture recognition in consideration of privacy,\u201d IEICE Trans. Electron., vol.E105-C, pp.79-85, 2022.","DOI":"10.1587\/transele.2021DII0002"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] D. Wang, Z. Li, N. Dey, A.S. Ashour, L. Moraru, R.S. Sherratt, and F. Shi, \u201cDeep-segmentation of plantar pressure images incorporating fully convolutional neural networks,\u201d Biocybernetics and Biomedical Engineering, vol.40, pp.546-558, 2020. 10.1016\/j.bbe.2020.01.004","DOI":"10.1016\/j.bbe.2020.01.004"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] D. Wang, Z. Li, N. Dey, A.S. Ashour, L. Moraru, A. Biswas, and F. Shi, \u201cOptical pressure sensors based plantar image segmenting using an improved fully convolutional network,\u201d Optik, vol.179, pp.99-114, 2019. 10.1016\/j.ijleo.2018.10.155","DOI":"10.1016\/j.ijleo.2018.10.155"},{"key":"14","unstructured":"[14] S. Sabour, N. Frosst, and G.E. Hinton, \u201cDynamic routing between capsules,\u201d Advances in neural information processing systems, pp.3856-3866, 2017."},{"key":"15","unstructured":"[15] E. Xi, S. Bing, and Y. Jin, \u201cCapsule network performance on complex data,\u201d arXiv preprint arXiv:1712.03480, 2017."},{"key":"16","unstructured":"[16] T. Cohen and M. Welling, \u201cGroup equivariant convolutional networks,\u201d International conference on machine learning, pp.2990-2999, 2016."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] D.E. Worrall, S.J. Garbin, D. Turmukhambetov, and G.J. Brostow, \u201cHarmonic networks: Deep translation and rotation equivariance,\u201d Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.5028-5037, 2017. 10.1109\/cvpr.2017.758","DOI":"10.1109\/CVPR.2017.758"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] J.A. Ramirez-Bautista, A. Hern\u00e1ndez-Zavala, S.L. Chaparro-C\u00e1rdenas, and J.A. 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