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张黄河

发布日期:2023-09-15     点击量:

姓名: 张黄河908E

性别: 男

民族: 汉族

学历: 博士

职称: 副研究员

所在院系:山东大学控制科学与工程学院

研究领域:康复机器人、可穿戴技术、便携式步态分析与康复,人机交互

邮箱:zhanghuanghe@sdu.edu.cn

通信地址:济南市经十路17923号山东大学千佛山校区,控制科学与工程学院

荣誉奖励:

海外博士后引才专项入选者、山东省优海外优青、泰山学者青年专家

全国创新创业优秀博士后、美国斯蒂文斯卓越博士

国际会议SIUSAI2024,最佳汇报论文奖

第二届全国博士后创新创业大赛(2023),海外(境外)赛,优胜奖,项目负责人

2024年中国山东博士(后)创新创业大赛,铜奖,项目负责人

2023 “凤凰杯”创新创业大赛,铜奖,项目负责人

美国创新创业&企业家精神博士奖学金(前1%),博士奖学金竞赛获奖,第5名

美国研究助理博士奖学金(前1%)

“世界500强”美国强生公司2018工程展示,精选演讲者

研究概况:

可穿戴技术为健康监测、远程问诊和临床康复等领域提供了新的方案,从而能够服务于人民生命健康。但准确度的不足阻碍了它们在科学研究和临床实践中的广泛应用。本人提出了基于直推式学习的步态分析方法,提高了可穿戴设备的准确性和可靠性,可以评估异常步态,丰富了现有步态分析方法,推动了可穿戴技术的发展。提出了基于强化学习的人在回路步态训练方法,为步态障碍患者提供了新的解决方案,拓展了现有步态康复方法。研制了步态分析与康复一体化设备,助力智能养老,被央视新闻直播报道。

科研项目:

[1] 国家自然科学基金青年科学基金,62403281,基于时序序列的直推式深度学习步态分析研究,30万元,主持,2025.1-2027.12

[2] 山东省优秀青年科学基金项目(海外), 2024HWYQ-019, 多源异构融合的人机共融步态分析与康复一体化研究, 60万元,主持,2024.1-2026.12

[3] 国家重点研发计划“智能机器人”重点专项子课题, 2023YFB4706102,多体位变换照护康复一体化支撑平台创成,15万元,主持,2023.12-2026.11

学术兼职:

Elsevier期刊Healthcare and Rehabilitation, 青年编委/特邀客座编辑(Lead Guest Editor)

Elsevier期刊Wearable Technology,特邀客座编辑(Lead Guest Editor)

Elsevier期刊Biomimetic Intelligence and Robotics,特邀客座编辑

第8届生物信息与生物医学工程国际学术会议(BIBE 2025),专题主席(唯一)

2025年第四届服务机器人国际会议(ICoSR 2025),专题主席(唯一)

第三届智能无人系统与人工智能国际会议(SIUSAI 2024),专题主席(Lead)

第17届智能机器人与应用国际会议(ICIRA2024),专题主席

2025年机电一体化与传感器技术国际会议(ICMST 2025),技术程序委员

2025第四届全国电子信息材料与器件大会,学术委员

2024全国柔性电子学术会议,专题委员

中国自动化学会学生工作委员会,委员

山东生物医学工程学会医体融合专业委员会,委员

精选论文:

[1] H. Zhang, C. Wu, Y. Huang, R. Song, D. Zanotto and S. Agrawal. Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2024 Dec 02.

[2] H. Zhang, Wu C, Huang Y, Li X, Ma X, Song R, Agrawal SK. 2D Deep Convolutional Neural Networks for Estimating Stride Length and Velocity in Institutionalized Older Adults. IEEE Sensors Journal. 2024 Jun 18.

[3] H. Zhang, S. Li, Q. Zhao, A. Rao, Y. Guo and D. Zanotto. “Reinforcement Learning-Based Adaptive Biofeedback Engine for Overground Walking Speed Training”, IEEE Robotics and Automation Letters, 2022, June 30

[4] H. Zhang, T. Duong, A. Rao, P. Mazzoni, S. Agrawal, Y. Guo and D. Zanotto. “Transductive Learning Models for Accurate Ambulatory Gait Analysis in Elderly Residents of Assisted Living Facilities”, IEEE Transactions on Neural Systems and Rehabilitation Engineering 30 (2022): 124-134.

[5] H. Zhang, Y. Guo and D. Zanotto. “Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models”, IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE).2020, 28, 191-202.

[6] H. Zhang, D. Zanotto and S.K. Agrawal. “Estimating CoP Trajectories and Kinematic Gait Parameters in Walking and Running Using Instrumented Insoles”, IEEE Robotics and Automation Letters. 2017, 2, 2159-2165.

[7] H. Zhang, Z. Chen, D. Zanotto and Y. Guo. “Robot-Assisted and Wearable Sensor-Mediated Autonomous Gait Analysis”, IEEE International Conference on Robotics and Automation (ICRA), Paris, 2020.

[8] H. Zhang, M. Tay, Z. Suar, M. Kurt and D. Zanotto. “Regression Models for Estimating Kinematic Gait Parameters with Instrumented Footwear”, 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Enschede, 2018.

[9] J. Wang, Z. Guan, J. Cai, X. Li, C. Wu, X. Ma, Y. Li, R. Song and H. Zhang. “Deep Neural Networks for Gait Cycle Percentage Prediction in Frail Older Adults Using a Foot-mounted IMU,” IEEE-ROBIO 2024. Accept.

[10] Z. Feng, Z. Jiang, H. Liu, W. Wang, Y. Wang, C. Lu, X. Ma, Y. Li, R. Song and H. Zhang. “Machine Learning Models for Gait Phases Detection Using Surface Electromyography Signals”17th International Conference on Intelligent Robotics and Applications (ICIRA2024), Springer. Accept.

[11] Y. Zhang, J. Cai, X. Li, C. Wu, X. Ma, R. Song and H. Zhang. “End-to-End Deep Learning Models for Estimating Stride Length in Frail Older Adults”, CFIMA 2024: The 2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, August 9-11, Baotou, China, 2024.

[12] Y. Sun, J. Wang, Z. Wang, J. Li, Y. Li, R. Song and H. Zhang. “Gait Phase Detection and Prediction with Machine Learning Models Based on sEMG”, The International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics, Accept.

大会报告:

1. 张黄河,”End-to-End Deep Learning Models for Estimating Stride Length in Frail Older Adults”, 2024 2nd International Conference on Frontiers of Intelligent Manufacturing and Automation (CFIMA 2024), August 11, 2024, Invited Speak

2. 张黄河, ”Human Lower Limb Function Assessment and Rehabilitation System”, 2024 China Medical Equipment Conference & Medical Equipment Exhibition, March 28-31, 2024, Guest Speaker

3. 张黄河,“Reinforcement Learning-Based Adaptive BiofeedbackEngine for Overground Walking Speed Training“,IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob),Seoul, Korea, Republic of, August 21-24, 2022

4. 张黄河, “Robot-Assisted and Wearable Sensor-Mediated Autonomous Gait Analysis”, IEEE International Conference on Robotics and Automation (ICRA), Paris, May-August 2020.

5. 张黄河,“Regression Models for Estimating Kinematic Gait Parameters with Instrumented Footwear”, 7th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Enschede, August 27-29, 2018.

6. 张黄河, “Estimating CoP Trajectories and Kinematic Gait Parameters in Walking and Running Using Instrumented Insoles”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, September 24–28, 2017.

7. 张黄河, “Gait Analysis in Children and Young Adults with Autism Spectrum Disorders Using Instrumented Insoles”, INSAR 2019, Palais des Congres de Montreal, QC, Canada, May 1-4, 2019.

8. 张黄河“Evaluation of muscle strength and gait parameters in ambulatory children and adults with spinal muscular atrophy”, APTA 2019 Combined Sections Meeting, Washington DC, January 23-26 2019.

特邀演讲:

1. 基于可穿戴技术的运动损伤评估及康复研究,山东体育学院,济南,中国,2025年1月

2. 智能可穿戴评估与康复系统,2024全国柔性电子学术会议,桂林,中国,2024年11月

3. 机器学习在步态分析与康复中的应用,山东大学护理与康复学院,济南,中国,2024年6月

4. 世界这本书,我想多读几页—考研留学两不误,山东大学薪火讲堂,济南,中国,2024年4月

5. 基于可穿戴技术的步态分析与康复一体化研究,中国智能可穿戴技术创新论坛,深圳,中国,2023年11月

6. 步态分析与康复一体化机器人,华中科技大学国际青年学者东湖论坛,武汉,湖北,2023年2月

7. 基于人机交互的居家养老研究,天津大学北洋青年科学家论坛,天津,中国,2023年2月

8. 基于可穿戴技术的智能步态分析及康复,2023北京大学优秀青年人才国际论坛,北京,中国,2023年1月

9. 机器学习模型用于精确步行步态分析,2021 上海大学国际青年学者论坛,上海,中国,2021年11月

10. Learning-based Methods to Improve Accuracy of Wearable Motion Capture Systems, Stevens Institute for Artificial Intelligence (SIAI), Nov. 2018

11. Footwear-based Gait Measurement System Using Machine Learning, Northeast Robotics Colloquium (NERC), Rutgers University, Oct.2018

12. SportSole: Instrumented Footwear for Portable Gait Analysis,  Graduate Research Conference, Stevens Institute of Technology, Feb, 2018

13. Gait Analysis Using Affordable Footwear-based System, I&E Conference, Stevens Institute of Technology, Nov. 2017


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