一、个人简介

章隆彬，博士、教授、博士生导师、硕士生导师、国家优秀青年基金（海外）获得者。2021年博士毕业于瑞典皇家理工。

研究方向：下肢骨肌系统生物力学研究，可穿戴辅助设备设计与控制，肌电控制和深度信息融合控制，人体运动仿真，数字孪生，物理信息与机器学习协同建模，康复医学，老年人摔倒风险评估，运动平衡机制研究

招收博士、硕士生，欢迎感兴趣的同学与我联系：longbin@hnu.edu.cn

二、代表论文

1. L. Zhang, A. Sidarta, T.-L. Wu, P. Jatesiktat, H. Wang, L. Li, P. W.-H. Kwong, A. Long, X. Long, and W. T. Ang, “Towards clinical application of enhanced timed up and go with markerless motion capture and machine learning for balance and gait assessment,”IEEE Journal of Biomedical and Health Informatics, vol. X, no. X, pp. 1–9, 2025 (SCI, IF:7.7, 工程技术顶刊, JCR Q1).

2. L. Zhang, T. V. Wouwe, S. Yan, and R. Wang, “Emg-constrained and ultrasound-informed muscle-tendon parameter estimation in post-stroke hemiparesis,”IEEE Transactions on Biomedical Engineering, vol. XXX, DOI:10.1109/TBME.2024.3352556, pp. 1–12, 2024 (SCI, IF:4.6，生物医学老牌期刊, JCR Q2).

3. L. Zhang, D. Soselia, R. Wang, and E. M. Gutierrez-Farewik, “Estimation of joint torque by emg-driven neuromusculoskeletal models and lstm networks,”IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 3722–3731, 2023 (SCI, IF:4.9，康复医学顶刊, JCR Q1).

4. L. Zhang, X. Zhu, E. M. Gutierrez-Farewik, and R. Wang, “Ankle joint torque prediction using an nms solver informed-ann model and transfer learning,”IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 12, pp. 5895–5906, 2022 (Featured Article, SCI, IF:7.7, 工程技术顶刊, JCR Q1).

5. L. Zhang, D. Soselia, R. Wang, and E. M. Gutierrez-Farewik, “Lower-limb joint torque prediction using lstm neural networks and transfer learning,”IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 600–609, 2022 (SCI, IF:4.9，康复医学顶刊, JCR Q1).

6. L. Zhang, Z. Li, Y. Hu, C. Smith, E. M. Gutierrez-Farewik, and R. Wang, “Ankle joint torque estimation using an emg-driven neuromusculoskeletal model and an artificial neural network model,”IEEE Transactions on Automation Science and Engineering, vol. 18, no. 2, pp. 564–573, 2021 (SCI, IF:5.6, 自动化与控制系统顶刊, JCR Q1).

7. L. Zhang, Z. Li, and C. Yang, “Adaptive neural network based variable stiffness control of uncertain robotic systems using disturbance observer,”IEEE Transactions on Industrial Electronics, vol. 64, no. 3, pp. 2236–2245, 2017 (SCI, IF:7.7, 工程技术顶刊, JCR Q1).

8. L. Zhang, X. Zhang, X. Zhu, R. Wang, and E. M. Gutierrez-Farewik, “Neuromusculoskeletal model- informed machine learning-based control of a knee exoskeleton with uncertainties quantification,” Frontiers in Neuroscience, vol. 17, pp. 1–12, 2023 (SCI, IF:4.3, 神经科学,JCR Q2).

9. L. Zhang, Y. Liu, R. Wang, C. Smith, and E. M. Gutierrez-Farewik, “Modeling and simulation of a hu- man knee exoskeleton’s assistive strategies and interaction,” Frontiers in Neurorobotics, pp. 1–12, 2021 (SCI,IF:3.1, 机器人学, JCR Q3).

10. Z. Li, K. Zhao,L. Zhang, X. Wu, T. Zhang, Q. Li, X. Li, and C.-Y. Su, “Human-in-the-loop control of a wearable lower limb exoskeleton for stable dynamic walking,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 5, pp. 2700–2711, 2021 (SCI, IF:6.4, 工程技术顶刊, JCR Q1).

11. S. Qiu, Z. Li, W. He,L. Zhang, C. Yang, and C.-Y. Su, “Brain–machine interface and visual compressive sensing-based teleoperation control of an exoskeleton robot,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 58–69, 2016 (SCI, IF:11.9, 工程技术顶刊，JCR Q1).

12. L. Zhang, X. Zhang, X. Zhu, R. Wang, and E. M. Gutierrez-Farewik, “Knee joint torque prediction with uncertainties by a neuromusculoskeletal solver-informed gaussian process model,” in 2023 International Conference on Advanced Robotics and Mechatronics (ICARM), IEEE, 2023, pp. 1035–1040.

13. L. Zhang, X. Zhu, E. M. G. Farewik, and R. Wang, “Estimation of ankle dynamic joint torque by a neu- romusculoskeletal solver-informed nn model,” in 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), IEEE, 2021, 75–80.