Mingming Zhang, Research Associate Scientist, Department of Biomedical Engineering, Southern University of Science and Technology, IEEE/ASME Member. His main research interests include intelligent control and human-computer interaction, wearable exoskeleton rehabilitation robots, tactile sensing and feedback technology, and EMG/EEG based pattern recognition algorithms. He received the Ph.D. degree in Mechanical Engineering from the University of Auckland, New Zealand, in 2015. From 2015 to 2016, he worked as a postdoc research fellow in the Department of Mechanical Engineering at the University of Auckland. From early 2017 to 2018, he worked at CASTIOT LIMITED (Auckland) as a vice president in developing lower limb rehabilitation robotics. He was also employed as a visiting research fellow at the University of Auckland. He has authored over 30 academic papers published on peer-reviewed international journals in the fields of robotics, intelligent control, neural rehabilitation and biomechanics, including IEEE Transactions on Industrial Electronics, IEEE Transactions on Biomedical Engineering, Journal of NeuroEngineering and Rehabilitation, Journal of Biomechanics, etc. His 23 patents have been applied or authorized as well. He also authored an academic book published by Institution of Engineering and Technology (UK).
Lab website: https://zhangmmlab.com/
2012-2016, Ph.D in Mechanical Engineering, The University of Auckland, New Zealand
2009-2011, M. S. in Mechatronics, Chongqing University
2005-2009, B. S. in Mechanical Engineering, Henan University of Science and Technology
August of 2018-Present, Research Associate Scientist, Department of Biomedical Engineering, Southern University of Science and Technology
2017-2018, Visiting Research Fellow, The University of Auckland, New Zealand
2017-2018, Vice President, CASTIOT LIMITED (Auckland), New Zealand
2015-2016, Postdoc Fellow, Department of Mechanical Engineering, University of Auckland, New Zealand
Honors and Rewards
1. IEEE/ASME Member
2. Lead Guest Editor, Advances in Mechanical Engineering (SCI Journal)
3. Associate Editor，2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Auckland), 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Hong Kong)
4. Invited Reviewer for IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Electronics, IEEE Transactions on Neural System and Rehabilitation Engineering, Journal of NeuroEngineering and Rehabilitation, IEEE Transactions on Biomedical Engineering, etc
1. Bin Zhong†, Wenxin Niu†, Elizabeth Broadbent, Andrew McDaid, Tatia M.C. Lee and M. Zhang*, “Bringing psychological strategies to robot-assisted physiotherapy for enhanced treatment efficacy”, Frontiers in Neuroscience, 2019.
2.Q. Miao, M. Zhang* (Corresponding Author), Andrew McDaid, Yuxin Peng, Sheng Q. Xie, "A robot-assisted bilateral upper limb training strategy with subject-specific workspace: A pilot study", Robotics and Autonomous Systems. (In Press)
3.L. Zhong, F. Li, Y. Peng*, Q. Yang, M. Zhang, J. Wang. “Design and characterization of a T-shaped two-axis force sensor”. Sensor review, 2019.
4. M. Zhang* (Corresponding Author), A. McDaid, S. Zhang, Y. Zhang, Sheng Q. Xie, “Automated objective robot-assisted assessment of wrist active ranges of motion”, Medical Engineering & Physics, 2019. (In Press)
5.D. Xu, M. Zhang* ,H. Xu, J. Fu, X. Li, 1 and S. Q. Xie, “Interactive Compliance Control of a Wrist Rehabilitation Device (WReD) with Enhanced Training Safety”, Journal of Healthcare Engineering, 2019.
6.M. Zhang* , A. McDaid, C. Davies, Sheng Q. Xie, “Adaptive Trajectory Tracking Control of a Parallel Ankle Rehabilitation Robot with Joint-Space Force Distribution”, IEEE Access, 2019. 7: p. 85812 – 85820.
7.Q. Miao, M. Zhang*, J. Cao, S. Q. Xie, "Reviewing High-Level Control Techniques on Robot-Assisted Upper-Limb Rehabilitation", Advanced Robotics, 2018. 32 (24): 1253-1268.
8.Q. Miao, A. McDaid, M. Zhang*, P. Kebria, H. Li, “A Three-Stage Trajectory Determination Method of Bilateral Upper Limb Training Using Interference Analysis”, Robotics and Autonomous Systems, 2018. 105: p. 38-46.
9.Zhang*, S. Zhang, A. McDaid, C. Davies, Sheng Q. Xie, “Automated Objective Robot-Assisted Assessment of Wrist Passive Ranges of Motion”, Journal of Biomechanics, 2018. 73: p. 223–226.
10.Q. Miao, M. Zhang*, C. Wang, H. Li, “Towards Optimal Robot Design for Ankle Rehabilitation: The State of Art and Future Prospects”, Journal of Healthcare Engineering, 2018.
11.Zhang, S. Xie, X. Li, G. Zhu, W. Meng, X. Huang, A. Veale, “Adaptive Patient-Cooperative Control of a Compliant Ankle Rehabilitation Robot (CARR) with Enhanced Training Safety”, IEEE Transactions on Industrial Electronics, 2017. 65(2): p. 1398 – 1407.
12.Zhang, J. Cao, G. Zhu, Q. Miao, X. Zeng, S. Xie, “Reconfigurable Workspace and Torque Capacity of a Compliant Ankle Rehabilitation Robot (CARR)”, Robotics and Autonomous Systems, 2017. 98: p. 213-221.
13.Zhang, J. Cao, S. Xie, G. Zhu, X. Zeng, X. Huang, Q. Xu, “A Preliminary Study on Robot-Assisted Ankle Rehabilitation for the Treatment of Drop Foot”, Journal of Intelligent & Robotic Systems, 2017.
14.Q. Miao, M. Zhang*, S. Xie, “Design and Interaction Control of a New Bilateral Upper Limb Rehabilitation Device (BULReD)”, Journal of Healthcare Engineering, 2017.
15.Zhang, T.C. Davies, Y Zhang, and S. Xie, “A Robot-Driven Computational Model Based Ankle Assessment Technique with Subject-Specific Adaptation”, IEEE Transactions on Biomedical Engineering, 2016. 63(4): p. 814-21.
16.Zhang, T.C. Davies, Y. Zhang, and S. Xie, “A Real-Time Computational Model for Estimating Kinematics of Ankle Ligaments”, Computer Methods in Biomechanics and Biomedical Engineering, 2016. 19(8): p. 835-44.
17.Zhang, T.C. Davies, A. Nandakumar, and S. Xie, “A Novel Assessment Technique for Measuring Ankle Orientation and Stiffness”, Journal of Biomechanics, 2015, 48(12): p. 3527-9.
18.Zhang, T.C. Davies, Y. Zhang, and S. Xie, Zhang, M., et al., Reviewing effectiveness of ankle assessment techniques for use in robot-assisted therapy. Journal of Rehabilitation Research & Development, 2014. 51(4), p. 517-34.
19.Zhang, et al., “An Assistance-as-Needed Control Paradigm for Robot-Assisted Ankle Rehabilitation”, Rehabilitation Process and Outcome, 2014: p. 15-17.
20.Zhang*, T.C. Davies, and S. Xie, Effectiveness of Robot-Assisted Therapy on Ankle Rehabilitation — A Systematic Review. Journal of NeuroEngineering and Rehabilitation, 2013. 10(1): p. 30.