个人简介

章隆彬，博士生导师、硕士生导师，入选国家海外青年人才项目。博士毕业于瑞典皇家理工学院，博士后在新加坡南洋理工大学。长期围绕基于生物力学的智能可穿戴技术展开研究，包括精确解码型运动意图预测、个性化肌肉肌腱参数的可靠估计和多场景安全型人机交互。搭建了神经肌肉骨骼和机器学习混合模型以实时预测运动意图，开创了预测模型的新方向；开发了全面的可靠的算法来量化肌肉肌腱变化，可用于制定个性化治疗计划；设计了数字孪生人机交互系统可模拟不同场景下的交互效果；研发了基于神经肌骨的自适应控制算法来提高交互安全性。在康复机器人、生物力学建模与控制领域发表高水平论文52篇，获授权专利4项，2次获机器人领域A类会议最佳论文提名，5次获国际学术奖项。

研究方向：外骨骼康复机器人，脑机接口，智慧医疗，数据安全，具身智能，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。

课题组招收博士后、博士、硕士研究生、本科生毕业设计、本科生科研助理等，与瑞典皇家理工学院、香港理工大学、新加坡南洋理工大学保持长期合作关系，可推荐优秀学生赴海外联合培养。欢迎感兴趣的同学与我联系：longbin@hnu.edu.cn

科研项目

[1] 基于生物力学的智能可穿戴技术. 国家自然科学基金优秀青年科学基金项目（海外）, 2025.01-2027.12（主持）

[2] 基于神经肌肉骨骼模型的人工神经网络实时关节扭矩预测. 瑞典Promobilia基金会,2022.06-2023.06（主持）

[3] 实时肌电驱动的膝关节可穿戴辅助设备设计与控制. 瑞典皇家工学院生物力学建模与实验中心,2020.06-2021.06（主持）

[4] 基于大数据的人体移动与平衡能力研究.新加坡康复研究院, 2021.06-2025.02（参与）

[5] 运动障碍和运动辅助的生物力学. 瑞典国家自然基金, 2019.01- 2024.01（参与）

[6] 针对运动障碍患者的多维度、多模式生物力学研究. 瑞典Promobilia基金会, 2019.01- 2023.01（参与）

[7] 神经肌骨骼建模的集成数字孪生平台. 瑞典国家自然基金, 2018.01- 2022.01（参与）

[8] 中风偏瘫患者注射肉毒毒素后的定量肌肉结构和硬度评估. 瑞典Promobilia基金会, 2018.12- 2019.12（参与）

[9] 基于弹性驱动器的可穿戴仿生腿研制.科技部, 2016.07- 2019.06（参与）

实验室情况

1. 现有研究方向：
（1）康复机器人：

     1）运动意图预测、地面反作用力预测、力矩预测、肌肉力预测、关节接触力预测

     2）膝关节智能支具、柔性外骨骼

（2）脑机接口：

     1）脑电解码算法与模型研究

     2）融合大语言模型的言语康复系统构建

     3）面向辅助交流、功能评估、康复训练与预后预测的一体化系统研究

（3）智慧医疗：

     1）图形影像：骨科CT、MRI；口腔科CBCT影像

     2）面向脑卒中康复的手部视觉评估系统设计与开发

（4）数据安全：

     1）基于联邦学习的数据隐私保护

2. 实验设备：脑电、肌电、无标记运动捕捉系统（上肢、全身）

3. 以前的一些工作：

·基于物理先验的机器学习----力矩预测

·多场景数字孪生系统

·个性化肌肉肌腱参数估计

·老年人摔倒风险评估

·运动平衡机制研究

近期论文

1.Z. Li, Z. Wan, X. Fu, S. Wang, T. Zhang, H. Zhang, L. Zhang*, X. Zhu*, and R. Wang*, “Physics-Informed Multimodal Learning for Wearable Estimation of Ground Reaction Forces and Lower-Limb Joint Moments”, IEEE Transactions on Neural Systems and Rehabilitation Engineering，在投. 

2.Z. Li, S. Wang, T. Zhang, Y. Kong, H. Zhang, L. Zhang*, A. Sidarta*, Y. C. Lim, C. Er, X. Yan, T. Wu, and W. T. Ang, “EEG Dynamics and Center of Pressure Modulation During Multi-Task Balance Challenges in Older Adults,” in 2026 41st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, 2026.

3.X. Fu, Z. Wan, Z. Li, S. Wang, T. Zhang, H. Zhang, L. Zhang*, X. Zhu*, and R. Wang*, “Predicting Ground Reaction Forces by LSTM Neural Networks with Multi-modal Data,” in 2026 41st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, 2026.

4.L. Zhang, Z. Li*, X. Fu, Y. Xie, X. Yan, S. Wang, T. Zhang, H. Zhang, K. Yang, T.L. Wu, P. Jatesiktat, A. Sidarta, and W. T. Ang, “Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models,” in 2026 11th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM), IEEE, 2026. 

5.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).

6.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. 71, no. 6, pp. 1798–1809, 2024 (SCI, IF:4.6，生物医学老牌期刊, JCR Q2).

7.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).

8.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).

9.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).

10.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).

11.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).

12.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).

13.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).

14.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).

15.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).

16.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.

17.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.