[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）
三、研究方向
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
四、代表论文
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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.
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.
