Hong-Bo Xie is a Vice-Chancellor's Senior Reserach Fellow at QUT and an Associate Investigator at ACEMS, QUT. He received his Ph.D. degree in Biomedical Engineering from Shanghai Jiao Tong University, Shanghai, China. He has published over 70 research papers incluidng book chapters, peer reviewed journal and conference papers. His research interests include statistical/Bayesian signal processing, machine learning, biomedical signal/image processing, matrix and tensor analysis for siganl and image recovery, and array signal processing.
Prechtl’s Method on the Qualitative Assessment of General Movement (GMsA) of infants (Alexander et al., 1993, Darsaklis et al., 2011, Einspieler, 2004, Haywood and Getchell, 2009, Piek, 2006) is one method of early prediction of neurodevelopmental outcomes in infants. This movement assessment uses video recordings and the naked eye of the assessor and has established 2 distinct movement classifications (Writhing and Fidgeting) which occur in healthy infants aged from term to 1 month and 3 months, respectively.
Prizes, awards and special recognition
QUT Vice-Chancellor's Research Fellow Travel Fellowships was awarded to Hongbo Xie. Awarded from the QUT.
Xie, H., & Guo T.
(2017). Computational Tools and Techniques for Biomedical Signal Processing.
(Singh, B., Ed.).Computational Tools and Techniques for Biomedical Signal Processing. 100-122. doi: 10.4018/978-1-5225-0660-7.ch005
Invited talks, refereed proceedings and other conference outputs
Turner, I., Xie H.,.Pearcy M., .Grote R., &.Colditz P.
(2018). Can Three-Dimensional Mioton Analysis and Fuzzy entropy detect movement differences in General Movement Assessment Categories in the normative infant population?. World Congress of Biomechanics.
Ji, Y., & Xie H.
(2017). Stationary wavelet and two-directional 2dpca for pattern recognition of electromyographic signal. Proceedings of the 2017 International Conference on Wavelet Analysis and Pattern Recognition. doi: 10.1109/ICWAPR.2017.8076668
Xie, H., & Liu H.
(2017). Myoelectrical signal classification based on S transform and two-directional 2DPCA. 17th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, doi: 10.1177/0142331217704035
(2016). Pattern Classification of High-Dimensional Myoelectric Signals Using Wavelet Two-Directional Two-Dimensional Principal Component Analysis. 2016 8th International Conference on Machine Learning and Computing. doi: 10.18178/ijmlc.2016.6.1.574
Ji, Y., & Xie H.
(2017). Generalized Multivariate Singular Spectrum Analysis for Nonlinear Time Series De-Noising and Prediction. Chinese Physics Letters. 34(12), doi: 10.1088/0256-307X/34/12/120501
Ji, Y., & Xie H.
(2017). Stationary wavelet-based two-directional two-dimensional principal component analysis for EMG signal classification. Measurement Science Review. 17(3), doi: 10.1515/msr-2017-0015
Mendez-Rebolledo, G., Gatica-Rojas V., Martinez-Valdes E., & Xie H.
(2016). The recruitment order of scapular muscles depends on the characteristics of the postural task. Journal of Electromyography and Kinesiology. 31, 40-47. doi: 10.1016/j.jelekin.2016.09.001
Jiang, J., & Xie H.
(2016). Denoising nonlinear time series using singular spectrum analysis and fuzzy entropy. Chinese Physics Letters. 33(10), doi: 10.1088/0256-307X/33/10/100501
Xie, H., Zhou P., Guo T., Sivakumar B., Zhang X., & Dokos S.
(2016). Multiscale two-directional two-dimensional principal component analysis and its application to high-dimensional biomedical signal classification. IEEE Transactions on Biomedical Engineering. 63(7), 1416-1425. doi: 10.1109/TBME.2015.2436375