个人简介
方银锋(1986.12-)，博士，副教授，省级引才计划领军人才。主要从事生物信号感知与模式识别、人机交互、运动康复相关的研究工作，特别是在肌电信号领域积累了丰厚的科研成果。主持参与国家重点研发计划国合项目1项、国家自然科学基金面上项目1项、浙江省自然基金1项，及多个重点实验室开放基金项目。发表SCI论文40余篇、授权发明专利四项。指导学生获得大学生电子设计竞赛信息前沿赛国家二等奖1次，大学生电子设计竞赛省一等奖1次，二等奖3次，三等奖4次，获得新苗计划立项2项，国家创新创业计划项目1项。 2012年6月前往英国朴次茅斯大学攻读博士学位，加入了生物机器人与控制研究组，师从刘洪海教授（欧洲科学院院士），研究方向为基于肌电信号的假肢手交互控制。针对电极佩戴困难、电极位置定位不准确的问题，提出了一种含有特殊电极分布的柔性电极阵列，具有一定的抗电极移位能力，且佩戴快速、无需电极定位[1]。该成果在2012年10月举办的ICIRA2012国际会议上获得了最优报告奖，并取得了国家发明专利（授权号：CN103230271B）。2013年，主要工作集中在肌电信号采集系统的设计，通过建模人体表面生物电信号的干扰源，采用模拟与数字滤波技术，提高肌电信号的保真度[2]。2014年，将工作重点转移到基于肌电信号的模式识别算法的研究，重点是通过机器学习与用户学习（User Training）相结合的方法，在较短时间内提升用户通过肌电实时控制假肢手的能力[3]。2015年5月，完成博士毕业论文答辩，毕业论文题目为《Interacting with prosthetic hands via electromyography signals》。2015年7月至2018年10月，留校参与一个与机器人辅助康复相关的欧盟项目，正式开始博士后研究工作，主要研究方向为信息系统中的多传感器融合技术。2015年末，一篇关于假肢手控制方法的综述论文在IEEE Sens. J.上发表，重点讨论了肌电、超声、近红外、肌压、肌音等技术在假肢手控制中使用的研究现状[4]。博后期间，与上海交大生机电团队开展深度合作，在基于超声信号的肌肉形态感知传感技术、面向卒中后手功能康复的镜像电刺激技术等方面取得了丰富的研究成果，相关研究陆续在IEEE Sens. J.[5]，IEEE T Ind. Electron.[6]，IEEE T Neuro. Sys. Reh.[7]等杂志发表。另一方面，和英国医疗机构Hobbs Rehabilitation Ltd.开展合作，逐步接触卒中后运动功能康复的病人，采集卒中后患者的前臂表面肌电信号，研究患者意图识别算法。2018年10月，回国加入杭州电子科技大学。通过对实验数据进行梳理，为了实现手势力度同步识别并提高肌电识别精度，提出了一种信息粒子化法[8]。期间培养的研究生研究方向包括：前臂肌肉群建模、肌电肌压融合模型、脑电情绪识别、基于深度森林的数据融合等，相关研究成果陆续在IEEE T. HMS[9]、Neurocomputing[10]、Int. J. Mach. Learn. & Cyber.[11]等杂志发表。 Fang Y, Zhu X, Liu H. Development of a surface emg acquisition system with novel electrodes configuration and signal representation. InIntelligent Robotics and Applications: 6th International Conference, ICIRA 2013, Busan, South Korea, September 25-28, 2013, Proceedings, Part I 6 2013 (pp. 405-414). Springer Berlin Heidelberg. Fang Y, Liu H, Li G, Zhu X. A multichannel surface EMG system for hand motion recognition. International Journal of Humanoid Robotics. 2015 Jun 30;12(02):1550011. Fang Y, Zhou D, Li K, Liu H. Interface prostheses with classifier-feedback-based user training. IEEE transactions on biomedical engineering. 2016 Dec 21;64(11):2575-83. Fang Y, Hettiarachchi N, Zhou D, Liu H. Multi-modal sensing techniques for interfacing hand prostheses: A review. IEEE Sensors Journal. 2015 Jul 29;15(11):6065-76. Zhou, Yu; Fang, Yinfeng; Gui, Kai; Li, Kairu; Zhang, Dingguo; Liu, Honghai. sEMG bias-driven functional electrical stimulation system for upper-limb stroke rehabilitation. IEEE Sensors Journal: IEEE,2018,18(16): 6812-6821 Zhou, Yu; Zeng, Jia; Li, Kairu; Fang, Yinfeng; Liu, Honghai. Voluntary and fes-induced finger movement estimation using muscle deformation features. IEEE Transactions on Industrial Electronics: IEEE,2019,67(5): 4002-4012 Yang, Xingchen; Yan, Jipeng; Fang, Yinfeng; Zhou, Dalin; Liu, Honghai. Simultaneous prediction of wrist/hand motion via wearable ultrasound sensing. IEEE Transactions on Neural Systems and Rehabilitation Engineering: IEEE,2020,28(4): 970-977 Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Ju, Zhaojie; Liu, Honghai. Attribute-driven granular model for EMG-based pinch and fingertip force grand recognition. IEEE transactions on cybernetics: IEEE,2021,51(2): 789-800 Chan PP, Li Q, Fang Y, Xu L, Li K, Liu H, Yeung DS. Unsupervised domain adaptation for gesture identification against electrode shift. IEEE Transactions on Human-Machine Systems. 2022 Jun 27;52(6):1271-80. Fang, Yinfeng; Yang, Jiani; Zhou, Dalin; Ju, Zhaojie. Modelling EMG driven wrist movements using a bio-inspired neural network. Neurocomputing, 2022, 470, 89-98 Fang Y, Lu H, Liu H. Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals. International Journal of Machine Learning and Cybernetics. 2023 Apr;14(4):1119-31.
纵向科研
面向多场景应用的可解释性肌电模式解码模型研,2019-11-19,2020-01-01,方银锋,在研,通信工程学院,信息科学与系统科学,9 肌压-肌电驱动下前臂肌骨网络模型研究,2020-01-01,2020-01-01,方银锋,在研,国家科技部,通信工程学院,信息科学与系统科学,3
横向科研
基于场景理解的消隐现实系统研究,2019-04-19,2019-05-01,王宇希,在研,通信工程学院,4 基于粒子模型的抓取力与姿态的同步估计,2019-04-19,2019-05-01,方银锋,在研,通信工程学院,8
论文
| [1] | Y. Fang, H. Zhang, X. Li, and S. Y. Chen, ‘The mathematical model and control scheme of a four-legged robot based on GZ-I and note module’, in Intelligent Robotics and Applications: Third International Conference, ICIRA 2010, Shanghai, China, November 10-12, 2010. Proceedings, Part I 3, 2010, pp. 300–309. | | [2] | S. Chen, X. Li, K. Lu, Y. Fang, and W. Wang, ‘Gait, stability and movement of snake-like robots’, International Journal of Advanced Robotic Systems, vol. 9, no. 5, p. 214, 2012. | | [3] | Y. Fang, H. Liu, G. Li, and X. Zhu, ‘A multichannel surface EMG system for hand motion recognition’, International Journal of Humanoid Robotics, vol. 12, no. 02, p. 1550011, 2015. | | [4] | Y. Fang, N. Hettiarachchi, D. Zhou, and H. Liu, ‘Multi-modal sensing techniques for interfacing hand prostheses: A review’, IEEE Sensors Journal, vol. 15, no. 11, pp. 6065–6076, 2015. | | [5] | Y. Fang, X. Zhu, and H. Liu, ‘Development of a surface emg acquisition system with novel electrodes configuration and signal representation’, in Intelligent Robotics and Applications: 6th International Conference, ICIRA 2013, Busan, South Korea, September 25-28, 2013, Proceedings, Part I 6, 2013, pp. 405–414. | | [6] | Y. Fang and H. Liu, ‘Robust sEMG electrodes configuration for pattern recognition based prosthesis control’, in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014, pp. 2210–2215. | | [7] | G. Li, W. Miao, G. Jiang, Y. Fang, Z. Ju, and H. Liu, ‘Intelligent control model and its simulation of flue temperature in coke oven’, Discrete and continuous dynamical systems-series s, vol. 8, no. 6, pp. 1223–1237, 2015. | | [8] | Y. Fang, Z. Ju, X. Zhu, and H. Liu, ‘Finger pinch force estimation through muscle activations using a surface EMG sleeve on the forearm’, in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014, pp. 1449–1455. | | [9] | W. Miao, G. Li, G. Jiang, Y. Fang, Z. Ju, and H. Liu, ‘Optimal grasp planning of multi-fingered robotic hands: a review’, Applied and Computational Mathematics: An International Journal, vol. 14, no. 3, pp. 238–247, 2015. | | [10] | Y. Fang, ‘Interacting with prosthetic hands via electromyography signals’, University of Portsmouth London, 2015. | | [11] | Z. Li, G. Li, G. Jiang, Y. Fang, Z. Ju, and H. Liu, ‘Computation of grasping and manipulation for multi-fingered robotic hands’, Journal of Computational and Theoretical Nanoscience, vol. 12, no. 12, pp. 6192–6197, 2015. | | [12] | W. Ding, G. Li, G. Jiang, Y. Fang, Z. Ju, and H. Liu, ‘Intelligent computation in grasping control of dexterous robot hand’, Journal of Computational and Theoretical Nanoscience, vol. 12, no. 12, pp. 6096–6099, 2015. | | [13] | D. Chen, G. Li, G. Jiang, Y. Fang, Z. Ju, and H. Liu, ‘Intelligent computational control of multi-fingered dexterous robotic hand’, Journal of Computational and Theoretical Nanoscience, vol. 12, no. 12, pp. 6126–6132, 2015. | | [14] | H. Liu et al., ‘HSI 2016 Organizing Committees’. | | [15] | Y. Zhang, Z. Wang, Z. Zhang, Y. Fang, and H. Liu, ‘Comparison of online adaptive learning algorithms for myoelectric hand control’, in 2016 9th International conference on human system interactions (HSI), 2016, pp. 69–75. | | [16] | W. Huang, P. P. K. Chan, D. Zhou, Y. Fang, H. Liu, and D. S. Yeung, ‘Multiple classifier system with sensitivity based dynamic weighting fusion for hand gesture recognition’, in 2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 2016, pp. 31–36. | | [17] | Y. Fang, D. Zhou, K. Li, and H. Liu, ‘Interface prostheses with classifier-feedback-based user training’, IEEE transactions on biomedical engineering, vol. 64, no. 11, pp. 2575–2583, 2016. | | [18] | D. Zhou, Y. Fang, J. Botzheim, N. Kubota, and H. Liu, ‘Bacterial memetic algorithm based feature selection for surface EMG based hand motion recognition in long-term use’, in 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1–7. | | [19] | Q. X. Li, P. P. K. Chan, D. Zhou, Y. Fang, H. Liu, and D. S. Yeung, ‘Improving robustness against electrode shift of sEMG based hand gesture recognition using online semi-supervised learning’, in 2016 International Conference on Machine Learning and Cybernetics (ICMLC), 2016, vol. 1, pp. 344–349. | | [20] | K. Li, Y. Fang, Y. Zhou, and H. Liu, ‘Non-invasive stimulation-based tactile sensation for upper-extremity prosthesis: a review’, IEEE Sensors Journal, vol. 17, no. 9, pp. 2625–2635, 2017. | | [21] | P. G. Esteban et al., ‘How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder’, Paladyn, Journal of Behavioral Robotics, vol. 8, no. 1, pp. 18–38, 2017. | | [22] | D. Zhou, Y. Fang, N. Kubota, and H. Liu, ‘Surface EMG based hand motion recognition using combined growing neural gas and linear discriminant analysis’, in 10th International Conference on Human System Interaction (HSI2017), 2017. | | [23] | H. Cai, D. Lee, H. Joonkoo, Y. Fang, S. Li, and H. Liu, ‘Embedded vision based automotive interior intrusion detection system’, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 2909–2914. | | [24] | H. Chen, Y. Zhang, Z. Zhang, Y. Fang, H. Liu, and C. Yao, ‘Exploring the relation between EMG sampling frequency and hand motion recognition accuracy’, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 1139–1144. | | [25] | P. Boyd, Y. Fang, and H. Liu, ‘Preliminary results of emg-based hand gestures for long term use’, in Intelligent Robotics and Applications: 10th International Conference, ICIRA 2017, Wuhan, China, August 16--18, 2017, Proceedings, Part III 10, 2017, pp. 98–108. | | [26] | X. Yang, Y. Li, Y. Fang, and H. Liu, ‘A preliminary study on the relationship between grip force and muscle thickness’, in 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017, pp. 118–121. | | [27] | Y. Fang, D. Zhou, K. Li, Z. Ju, and H. Liu, ‘A force-driven granular model for EMG based grasp recognition’, in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 2939–2944. | | [28] | Y. Zhou, Y. Fang, K. Gui, K. Li, D. Zhang, and H. Liu, ‘sEMG bias-driven functional electrical stimulation system for upper-limb stroke rehabilitation’, IEEE Sensors Journal, vol. 18, no. 16, pp. 6812–6821, 2018. | | [29] | D. Zhou, Y. Fang, Z. Ju, and H. Liu, ‘Multi-length windowed feature selection for surface EMG based hand motion recognition’, in Intelligent Robotics and Applications: 11th International Conference, ICIRA 2018, Newcastle, NSW, Australia, August 9--11, 2018, Proceedings, Part I 11, 2018, pp. 264–274. | | [30] | J. Li, Y. Fang, Y. Huang, G. Li, Z. Ju, and H. Liu, ‘Towards active muscle pattern analysis for dynamic hand motions via sEMG’, in Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK, 2019, pp. 372–382. | | [31] | H. Cai et al., ‘Sensing-enhanced therapy system for assessing children with autism spectrum disorders: A feasibility study’, IEEE Sensors Journal, vol. 19, no. 4, pp. 1508–1518, 2018. | | [32] | K. Li, Y. Fang, Y. Zhou, Z. Ju, and H. Liu, ‘Haptics model for human fingertips based on gaussian distribution’, Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 3945–3955, 2019. | | [33] | J. Li, Y. Fang, Y. Ning, J. Jie, P. Tan, and H. Liu, ‘Relative confidence based information fusion for semg-based pattern recognition’, in 2018 International Conference on Machine Learning and Cybernetics (ICMLC), 2018, vol. 2, pp. 625–630. | | [34] | H. Chen, R. Tong, M. Chen, Y. Fang, and H. Liu, ‘A hybrid cnn-svm classifier for hand gesture recognition with surface emg signals’, in 2018 international conference on machine learning and cybernetics (ICMLC), 2018, vol. 2, pp. 619–624. | | [35] | W.-F. Xu, Y.-F. Fang, G.-Y. Zhang, Z.-J. Ju, G.-F. Li, and H.-H. Liu, ‘Surface Emg channel selection for thumb motion classificationsignal’, in 2018 International Conference on Machine Learning and Cybernetics (ICMLC), 2018, vol. 2, pp. 662–666. | | [36] | Z. Wang, Y. Fang, G. Li, and H. Liu, ‘Facilitate sEMG-based human--machine interaction through channel optimization’, International Journal of Humanoid Robotics, vol. 16, no. 04, p. 1941001, 2019. | | [37] | H. Chen, Y. Zhang, G. Li, Y. Fang, and H. Liu, ‘Surface electromyography feature extraction via convolutional neural network’, International Journal of Machine Learning and Cybernetics, vol. 11, no. 1, pp. 185–196, 2020. | | [38] | X. Zhang, D. Lin, J. Zheng, X. Tang, Y. Fang, and H. Yu, ‘Detection of salient crowd motion based on repulsive force network and direction entropy’, Entropy, vol. 21, no. 6, p. 608, 2019. | | [39] | Y. Zhou, J. Zeng, K. Li, Y. Fang, and H. Liu, ‘Voluntary and FES-induced finger movement estimation using muscle deformation features’, IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 4002–4012, 2019. | | [40] | P. Boyd, Y. Fang, and H. Liu, ‘Ultrasound feature evaluation for robustness to sensor shift in ultrasound sensor based hand motion recognition’, in Towards Autonomous Robotic Systems: 20th Annual Conference, TAROS 2019, London, UK, July 3--5, 2019, Proceedings, Part I 20, 2019, pp. 115–125. | | [41] | Y. Fang, J. Zhang, N. Kubota, and H. Zhang, ‘Bio-signal analysis for human machine interaction’, 2019. | | [42] | Y. Fang, D. Zhou, K. Li, Z. Ju, and H. Liu, ‘Attribute-driven granular model for EMG-based pinch and fingertip force grand recognition’, IEEE transactions on cybernetics, vol. 51, no. 2, pp. 789–800, 2021. | | [43] | Z. Wang, Y. Fang, D. Zhou, K. Li, C. Cointet, and H. Liu, ‘Ultrasonography and electromyography based hand motion intention recognition for a trans-radial amputee: A case study’, Medical Engineering & Physics, vol. 75, pp. 45–48, 2020. | | [44] | X. Yang, J. Yan, Y. Fang, D. Zhou, and H. Liu, ‘Simultaneous prediction of wrist/hand motion via wearable ultrasound sensing’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 4, pp. 970–977, 2020. | | [45] | D. Zhou, Y. Fang, Z. Ju, and H. Liu, ‘Subclass Discriminant Analysis based Myoelectric Hand Motion Recognition’, Poster Papers, p. 121. | | [46] | K. Li et al., ‘Electrotactile Feedback-Based Muscle Fatigue Alleviation for Hand Manipulation’, International Journal of Humanoid Robotics, vol. 18, no. 02, p. 2050024, 2021. | | [47] | Y. Fang, H. Yang, X. Zhang, H. Liu, and B. Tao, ‘Multi-feature input deep forest for EEG-based emotion recognition’, Frontiers in neurorobotics, vol. 14, p. 617531, 2021. | | [48] | Y. Fang, X. Zhang, D. Zhou, and H. Liu, ‘Improve inter-day hand gesture recognition via convolutional neural network-based feature fusion’, International Journal of Humanoid Robotics, vol. 18, no. 02, p. 2050025, 2021. | | [49] | H. Cai et al., ‘Therapy-enhanced Sensing System for Assessing Children with Autism Spectrum Disorders: A Feasibility Study’, Sensors, 2017. | | [50] | G. Lei, S. Zhang, Y. Fang, Y. Wang, and X. Zhang, ‘Investigation on the sampling frequency and channel number for force myography based hand gesture recognition’, Sensors, vol. 21, no. 11, p. 3872, 2021. | | [51] | Y. Fang, J. Yang, D. Zhou, and Z. Ju, ‘Modelling EMG driven wrist movements using a bio-inspired neural network’, Neurocomputing, vol. 470, pp. 89–98, 2022. | | [52] | P. P. K. Chan et al., ‘Unsupervised domain adaptation for gesture identification against electrode shift’, IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1271–1280, 2022. | | [53] | Z. Chen, Y. Qian, Y. Wang, and Y. Fang, ‘Deep convolutional generative adversarial network-based EMG data enhancement for hand motion classification’, Frontiers in Bioengineering and Biotechnology, vol. 10, p. 909653, 2022. | | [54] | X. Huang and Y. Fang, ‘Assistive Robot Design for Handwriting Skill Therapy of Children with Autism’, in International Conference on Intelligent Robotics and Applications, 2022, pp. 413–423. | | [55] | H. Xia, C. Pu, B. Wang, Z. Liu, and Y. Fang, ‘Desktop-Sized Lithium Battery Protection Printed Circuit Board Detection System Based on Visual Feedback Manipulator’, in International Conference on Intelligent Robotics and Applications, 2022, pp. 333–344. | | [56] | Y. Fang, H. Lu, and H. Liu, ‘Multi-modality deep forest for hand motion recognition via fusing sEMG and acceleration signals’, International Journal of Machine Learning and Cybernetics, vol. 14, no. 4, pp. 1119–1131, 2023. | | [57] | H. Yang, J. Wan, Y. Jin, X. Yu, and Y. Fang, ‘EEG-and EMG-driven poststroke rehabilitation: a review’, IEEE Sensors Journal, vol. 22, no. 24, pp. 23649–23660, 2022. | | [58] | W. Xu, Y.-F. Fang, G.-Y. Zhang, Z.-J. Ju, G.-F. Li, and H.-H. Liu, ‘SURFACE EMG CHANNEL SELECTION FOR THUMB MOTION CLASSIFICATION’. | | [59] | Y. Ma and Y. Fang, ‘Safety helmet wearing recognition based on Yolov5’, in Mobile Wireless Middleware, Operating Systems and Applications: 10th International Conference on Mobile Wireless Middleware, Operating Systems and Applications (MOBILWARE 2021), 2022, pp. 137–150. | | [60] | Y. Fang, Y. Ma, X. Zhang, and Y. Wang, ‘Enhanced YOLOv5 algorithm for helmet wearing detection via combining bi-directional feature pyramid, attention mechanism and transfer learning’, Multimedia Tools and Applications, vol. 82, no. 18, pp. 28617–28641, 2023. | | [61] | W. Xia, Y. Zhou, Y. Fang, and H. Liu, ‘ECG-Enhanced multi-sensor solution for wearable sports devices’, in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, pp. 1939–1944. | | [62] | Y. Wu, D. Gao, Y. Fang, X. Xu, H. Gao, and Z. Ju, ‘Sde-yolo: A novel method for blood cell detection’, Biomimetics, vol. 8, no. 5, p. 404, 2023. | | [63] | J. Shi, M. Liu, Y. Fang, J. Yu, H. Gao, and Z. Ju, ‘Examining the Impact of Muscle-Electrode Distance in sEMG Based Hand Motion Recognition’, in International Conference on Intelligent Robotics and Applications, 2023, pp. 55–67. | | [64] | Y. Wu, Y. Fang, D. Gao, H. Gao, and Z. Ju, ‘SW-YOLO: Improved YOLOv5s Algorithm for Blood Cell Detection’, in International Conference on Intelligent Robotics and Applications, 2023, pp. 161–172. | | [65] | S. Zhang, Y. Fang, J. Wan, G. Jiang, and G. Li, ‘Transfer Learning Enhanced Cross-Subject Hand Gesture Recognition with sEMG’, Journal of Medical and Biological Engineering, vol. 43, no. 6, pp. 672–688, 2023. | | [66] | Y. Jin et al., ‘Electroacupuncture prevents the development or establishment of chronic pain via IL-33/ST2 signaling in hyperalgesic priming model rats’, Neuroscience Letters, vol. 820, p. 137611, 2024. | | [67] | C. Zhu, Y. Peng, Y. Fang, and W. Kong, ‘Label Rectified and Graph Adaptive Semi-Supervised Regression for Electrode Shifted Gesture Recognition’, in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 1896–1900. | | [68] | Y. Zhang, Y. Fang, X. Yu, Y. Peng, and Z. Ju, ‘Cosine Similarity Graph Attention Networks for Autism Spectrum Disorder Diagnosis’, in 2024 World Rehabilitation Robot Convention (WRRC), 2024, pp. 1–5. |
科研成果
面向多场景应用的可解释性肌电模式解码模型研,2019-11-19,2020-01-01,方银锋,在研,通信工程学院,信息科学与系统科学,9 肌压-肌电驱动下前臂肌骨网络模型研究,2020-01-01,2020-01-01,方银锋,在研,国家科技部,通信工程学院,信息科学与系统科学,3
专利成果
基于排斥力网络与方向熵的人群显著性运动检测方法,发明专利,专利申请,201910222301.1,通信工程学院,张旭光 一种基于卷积神经网络的肌电信号特征提取方法,发明专利,专利申请,201811489106.7,通信工程学院,方银锋 一种用于同步识别手势和抓取力的粒子化方法及粒子化分析法,发明专利,专利申请,201910934323.0,通信工程学院,方银锋 一种电子感应触觉反馈浮漂,实用新型,专利授权,201921874961.X,CN 211430698 U,2020-09-08,通信工程学院,方银锋 一种电子感应触觉反馈浮漂,发明专利,专利申请,201911065329.5,通信工程学院,方银锋 一种基于多特征深度森林的脑电情绪识别方法,发明专利,专利申请,202110318173.8,通信工程学院,方银锋 一种用于手腕动作识别的生物启发式网络模型设计方法,发明专利,专利申请,202011305658.5,通信工程学院,方银锋 一种用于肌压信号采集的传感阵列和系统,发明专利,专利申请,202110094494.4,通信工程学院,方银锋 基于迁移学习的表面肌电信号上肢动作识别方法及系统,发明专利,专利申请,202111596704.6,通信工程学院,方银锋
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