基本信息

王卫群  研究员、博导

中国科学院自动化研究所

中国科学院脑科学与智能技术卓越创新中心

中国科学院人工智能创新研究院"2035创新团队" 

电子邮件: weiqun.wang@ia.ac.cn

通信地址: 北京市海淀区中关村东路95

邮政编码: 100190

部门/实验室:复杂系统管理与控制国家重点实验

研究领域

面向康复医疗应用的神经科学、机器人与人工智能技术。

未来研究重点:

1、脑机接口与智能人机交互
2、强化神经协同参与的智能化康复
3、康复处方的自主学习与进化

*与国内顶级康复医疗机构有长期的合作研究,与康复机器人、计算智能领域的国际顶级研究团队有长期的合作

*获得 2020年第四届全国机器人专利创新创业大赛 特等奖,指导的博士生获得 2020世界机器人大赛-BCI脑控机器人大赛情绪脑机组一等奖(第1名)、国家奖学金、攀登奖学金等。

*招收相关专业的硕士、博士及博士后工作人员,长年招收相关方向的实习生;2021年还可招收1名普博,1-2名博士后,欢迎感兴趣的优秀学子加入

工作经历

2019.11 至今,中国科学院自动化研究所,研究员
2014.10~2019.10中国科学院自动化研究所,副研究员
2006.07~2011.08北京机械工业自动化研究所,电控工程师

学术兼职

2020.12 至今,中国自动化学会智能健康与生物信息专委会,副主任委员
2020.11 至今,中国自动化学会混合智能专委会,委员
2019.04 至今,中国生物医学工程学会康复工程分会,委员
2016.12 至今,中国医学装备协会智能装备技术分会,委员
2018.05 至今,北京人工智能学会, 理事
2016,《自动化学报》“康复机器人与智能辅助系统”特刊,编委

专利与奖励

  • 美国发明专利:Multi-Posture Lower Limb Rehabilitation Robot, 专利授权号:US10722416B2, 授权日期: 2020.07.28
  • 美国发明专利:Upper limb rehabilitation robot system,专利授权号:US10596056B2,授权日期:2020.03.24
  • 发明专利:基于脑-机接口的注意力调控系统,受理公开号:CN110522447A,公开日期:2019/12/3
  • 发明专利:一种便携式心电及表面肌电测量装置, 专利号: 201510601401.7,授权时间:2015.12.23
  • 发明专利:一种康复机器人主动训练控制方法和装置, 专利号: 201610317761.9,授权时间:2018.06.29
  • 发明专利:一种多位姿下肢康复训练机器人, 专利号: ZL201510126371.9,授权时间:2016.11.09
  • 发明专利:一种坐卧式个性化下肢康复训练机器人, 专利号: ZL201210265145.5,授权时间:2014.03.12
  • 发明专利:上肢康复机器人系统, 专利号: 201480000563.9,授权时间:2018.02.02
  • 发明专利:一种实验用兔子脊椎弯曲装置,  专利号: ZL201210268913.2,授权时间:2014.09.17
  • 发明专利:一种上肢康复机器人手指及手腕训练装置, 专利号: 201610319764.6,授权时间:2017.11.21
  • 发明专利:任务导向式主动训练控制方法,专利号:ZL201410799839.6, 授权时间:2017.02.22
  • 发明专利:基于sEMG的单关节主动训练控制方法及相应的康复机器人,专利号:ZL201410784548.X, 授权时间:2017.07.21
  • 发明专利:脊髓损伤撞击装置,专利号:ZL201210593040.2,授权时间:2015.04.01


部分学术论文

期刊论文

[1] S. Ren, W. Wang*, Z.G. Hou, X. Liang, J. Wang, and W. Shi, “Enhanced motor imagery based brain-computer interface via FES and VR for lower limbs,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020. (中科院1区,TOP期刊)

[2] J Wang, W Wang, and Z.G. Hou*, “Towards improving engagement in neural rehabilitation: attention enhancement based on brain-computer interface and audiovisual feedback,” IEEE Transactions on Cognitive and Developmental Systems, 2019.

[3] S. Ren, W. Wang*, Z.G. Hou, B. Chen, X. Liang, J. Wang, L. Peng, “Personalized gait trajectory generation based on anthropometric features Using Random Forest,” Journal of Ambient Intelligence and Humanized Computing, 2019.

[4] 任世鑫, 王卫群*, 侯增广, 陈霸东, 石伟国, 王佳星, 梁 旭, 基于改进共空间模式与视觉反馈的闭环脑机接口,《机械工程学报》, 2019.

[5] W. Wang, W. Shi, Z.G. Hou*, B. Chen, X. Liang, S. Ren, J. Wang, and L. Peng, “Prediction of human voluntary torques based on collaborative neuromusculoskeletal modeling and adaptive learning,” IEEE Transactions on Industrial Electronics,2020. (中科院1区,TOP期刊)

[6] W. Wang, Z.G. Hou*, L. Cheng, L. Tong, L. Peng, L. Peng, and M. Tan, “Towards patients’ motion intention recognition: Dynamics modeling and identification of iLeg - a lower limb rehabilitation robot under motion constraints,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 7, pp. 980-992, 2016. (中科院1区,TOP期刊)

[7] W. Wang, Z.G. Hou*, L. Tong, F. Zhang, Y. Chen and M. Tan, “A novel leg orthosis for lower limb rehabilitation robots of the sitting/lying type,” Mechanism and Machine Theory, vol. 74, pp. 337-353, 2014. 中科院1区,TOP期刊)

[8] W. Wang, W. Shi, S. Ren, Z.G. Hou*, X. Liang, J. Wang, and L. Peng,“GPR and SPSO-CG based gait pattern generation for subject-specific training, ” SCIENCE CHINA Information Sciences, 2019.

[9] J. Li, Y. Xue, W. Wang, and G. Ouyang*, "Cross-Level Parallel Network for Crowd Counting," IEEE Transactions on Industrial Informatics, vol. 16, no. 1, pp. 566-576, 2020.(中科院1区,TOP期刊)

[10] T. Sun, L. Cheng, W. Wang, and Y. Pan*, “Semiglobal exponential control of Euler-Lagrange systems using a sliding-mode disturbance observer,”Automatica, 2019.中科院1区,TOP期刊)

[11]Y. Zheng, B. Chen*, S. Wang and W. Wang, "Broad Learning System Based on Maximum Correntropy Criterion," IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2020.3009417.中科院1区,TOP期刊)

会议论文

[1] Z. Fang, W. Wang*, S. Ren, J. Wang, W. Shi, X. Liang, C.C. Fan and Z.G. Hou, “Learning regional attention convolutional neural network for motion intention recognition based on EEG data,” the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence(IJCAI-PRICAI), 2020.(录用率12.6%)

[2] X. Liang, W. Wang*, Z.G. Hou, S. Ren, J. Wang, W. Shi, L. Peng, T. Su, "Position based impedance control strategy for a lower limb rehabilitation robot," in the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019.

[3] J. Wang, W. Wang*, Z.G. Hou, X. Liang, S. Ren, L. Peng,“Towards enhancement of patients’engagement: online modification of rehabilitation training modes using facial expression and muscle fatigue”, in the 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),Honolulu, Hawaii,July 17-21, 2018, pp. 2304-2307.

[4] W. Shi, W. Wang*, Z.G. Hou, X. Liang, J. Wang, S. Ren, and L. Peng, “SEMG and KNN based human motion intention recognition for active and safe neurorehabilitation,” The 26th International Conference on Neural Information Processing (ICONIP), Sydney, Australia, December 12-15, 2019.

[5] W. Wang, Z.G. Hou*, W. Shi, X. Liang, S. Ren, J. Wang, and L. Peng, “Neuromuscular activation based sEMG-torque hybrid modeling and optimization for robot assisted neurorehabilitation,” in the 26th International Conference on Neural Information Processing (ICONIP), Sydney, Australia, December 12-15, 2019.

[6] W. Wang,  Z.G. Hou*, L. Tong, Y. Chen, L. Peng and M. Tan, “Dynamics identification of the human-robot interface based on a lower limb rehabilitation robot,” in Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA) , Hong Kong, China, Jun. 2014, pp. 6012-6017.

科研项目

作为项目负责人或者参加单位负责人承担国家重点研发计划项目、国家自然基金委重大研究计划项目、北京市自然科学基金重点项目、军委科技委项目等,研究经费充裕。