面向人本智造的人体运动数字孪生研究与应用进展

doi:10.3901/JME.2025.15.021

机械工程学报 ›› 2025, Vol. 61 ›› Issue (15): 21-39.doi: 10.3901/JME.2025.15.021

• 综述 • 上一篇

扫码分享

面向人本智造的人体运动数字孪生研究与应用进展

王柏村^1,2, 宋词¹, 苑艺修¹, 周慧颖^1,3, 鲍劲松⁴, 黄思翰⁵, 刘蔚然⁶, 刘庭煜⁷, 阮兵⁸, 陶飞⁶, 谢海波^1,2, 杨华勇^1,2

1. 浙江大学机械工程学院杭州 310058;
2. 浙江大学高端装备研究院杭州 311106;
3. 东方电气长三角(杭州)创新研究院有限公司杭州 310011;
4. 东华大学机械工程学院上海 201620;
5. 北京理工大学机械与车辆学院北京 100081;
6. 北京航空航天大学国际前沿交叉科学研究院北京 100191;
7. 东南大学机械工程学院南京 211189;
8. 中国汽车工业工程有限公司天津 300113

收稿日期:2025-03-03 修回日期:2025-05-23 发布日期:2025-09-28
作者简介:王柏村，男，1990年出生，博士，研究员，博士研究生导师。主要研究方向为人本智造、人-信息-物理系统、数字孪生。Email：baicunw@zju.edu.cn;宋词，女，2000年出生，博士研究生。主要研究方向为人机协作、人体运动捕捉。周慧颖(通信作者)，女，1996年出生，博士后。主要研究方向为人-信息-物理系统，人机交互，智能制造。Email：huiyingzhou@zju.edu.cn
基金资助:
国家重点研发计划(2024YFB3309802); 国家自然科学基金(52205542)资助项目。

Research and Application Progress of Human Motion Digital Twin for Human-centric Smart Manufacturing

WANG Baicun^1,2, SONG Ci¹, YUAN Yixiu¹, ZHOU Huiying^1,3, BAO Jinsong⁴, HUANG Sihan⁵, LIU Weiran⁶, LIU Tingyu⁷, RUAN Bing⁸, TAO Fei⁶, XIE Haibo^1,2, YANG Huayong^1,2

1. School of Mechanical Engineering, Zhejiang University, Hangzhou 310058;
2. Institute of Advanced Machines, Zhejiang University, Hangzhou 31106;
3. Dongfang Electric (Hangzhou) Innovation Institute Co., Ltd., Hangzhou 310011;
4. College of Mechanical Engineering, Donghua University, Shanghai 201620;
5. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081;
6. International Institute for Interdisciplinary and Frontiers, Beihang University, Beijing 100191;
7. School of Mechanical Engineering, Southeast University, Nanjing 211189;
8. Automotive Engineering Corporation Co., Ltd., Tianjin 300113

Received:2025-03-03 Revised:2025-05-23 Published:2025-09-28

摘要/Abstract

摘要： 在工业4.0迈向工业5.0的过程中，以人为本的智能制造(人本智造)是智能制造的创新发展范式，旨在以人的福祉为核心价值，重塑人在生产制造过程中的主体地位，推进未来工业迈向以人为本、可持续性、韧性。对人体运动行为进行分析，是理解人的运动意图，推动人本智造创新发展的关键。以面向人本智造的人体运动数字孪生研究为切入点，梳理人体运动建模、人体运动感知、人体运动分析等使能技术及其研究进展。重点围绕单元级、产线级、车间级三个维度，讨论人体运动数字孪生在人本智造领域的典型应用。最后，对面向人本智造的人体运动数字孪生进行展望。

关键词: 人本智造, 人体运动数字孪生, 人体运动表征, 人体运动感知, 人体运动分析, 人体运动应用

Abstract: In the transition from Industry 4.0 to Industry 5.0, human-centric smart manufacturing（HSM） represents an innovative paradigm in the development of smart manufacturing systems. In the context of HSM, human well-being is recognized as its core value which aims to redefine and reinforce the central role of humans in manufacturing and production processes. Therefore, HSM is promoting the futuristic industry which is human-centric, sustainable, and resilient. Human motion is the key to realizing human movement intentions as well as to promoting the development of HSMs. This work focuses on human motion digital twin（HMDT）, reviews its enabling technologies and research advancements, specifically focusing on human motion modeling, perception, and analysis, with an emphasis on their pivotal applications in three dimensions, i.e., unit level, production line level, and workshop level. Through case study, this work illustrates how HMDT facilitates the application of HSM. Finally, the future research directions of HMDT in HSM are outlooked.

Key words: human-centric smart manufacturing, human motion digital twin, human motion representation, human motion perception, human motion analysis, human motion application

中图分类号:

TP182

王柏村, 宋词, 苑艺修, 周慧颖, 鲍劲松, 黄思翰, 刘蔚然, 刘庭煜, 阮兵, 陶飞, 谢海波, 杨华勇. 面向人本智造的人体运动数字孪生研究与应用进展[J]. 机械工程学报, 2025, 61(15): 21-39.

WANG Baicun, SONG Ci, YUAN Yixiu, ZHOU Huiying, BAO Jinsong, HUANG Sihan, LIU Weiran, LIU Tingyu, RUAN Bing, TAO Fei, XIE Haibo, YANG Huayong. Research and Application Progress of Human Motion Digital Twin for Human-centric Smart Manufacturing[J]. Journal of Mechanical Engineering, 2025, 61(15): 21-39.

参考文献

[1] WANG B,TAO F,FANG X,et al. Smart manufacturing and intelligent manufacturing:A comparative review[J].Engineering,2021,7(6):738-757.
[2] 王柏村,薛塬,延建林,等.以人为本的智能制造:理念、技术与应用[J].中国工程科学,2020,22(4):139-146.WANG Baicun, XUE Yuan, YAN Jianlin, et al.Human-centered intelligent manufacturing:Overview and perspectives[J]. Strategic Study of CAE,2020,22(4):139-146.
[3] HUANG S,WANG B,LI X,et al. Industry 5.0 and Society 5.0-Comparison, complementation and coevolution[J]. Journal of Manufacturing Systems,2022,64:424-428.
[4] LENG J,SHA W,WANG B,et al. Industry 5.0:Prospect and retrospect[J]. Journal of Manufacturing Systems,2022,65:279-295.
[5] WANG B,ZHOU H,LI X,et al. Human digital twin in the context of Industry 5.0[J]. Robotics and ComputerIntegrated Manufacturing,2024,85:102626.
[6] WANG B,ZHENG P,YIN Y,et al. Toward humancentric smart manufacturing:A human-cyber-physical systems (HCPS) perspective[J]. Journal of Manufacturing Systems,2022,63:471-490.
[7] WANG B,LI Y,FREIHEIT T. Towards intelligent welding systems from a HCPS perspective:A technology framework and implementation roadmap[J]. Journal of Manufacturing Systems,2022,65:244-259.
[8] 王柏村,易兵,刘振宇,等. HCPS视角下智能制造的发展与研究[J].计算机集成制造系统,2021,27(10):2749-2761.WANG Baicun,YI Bing,LIU Zhenyu,et al. Evolution and State-of-the-art of smart manufacturing from HCPS perspective[J]. Computer Integrated Manufacturing Systems,2021,27(10):2749-2761.
[9] 王柏村,黄思翰,易兵,等.面向智能制造的人因工程研究与发展[J].机械工程学报,2020,56(16):240-253.WANG Baicun, HUANG Sihan, YI Bing, et al.State-of-art of human factors/ergonomics in smart manufacturing[J]. Journal of Mechanical Engineering,2020,56(16):240-253.
[10] HOMAYOUNFAR S Z, ANDREW T L. Wearable sensors for monitoring human motion:A review on mechanisms, materials, and challenges[J]. SLAS TECHNOLOGY:Translating Life Sciences Innovation,2020,25(1):9-24.
[11] BONCI A, CEN CHENG P D, INDRI M, et al.Human-robot perception in industrial environments:A survey[J]. Sensors,2021,21(5):1571.
[12] KONG Y, FU Y. Human action recognition and prediction:A survey[J]. International Journal of Computer Vision,2022,130(5):1366-1401.
[13] RUDENKO A,PALMIERI L,HERMAN M,et al.Human motion trajectory prediction:A survey[J]. The International Journal of Robotics Research,2020,39(8):895-935.
[14] ZHU W,MA X,RO D,et al. Human motion generation:A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2024,46(4):2430-2449.
[15] WANG L,LIU S,LIU H,et al. Overview of human-robot collaboration in manufacturing[C]//Proceedings of the Proceedings of 5th International Conference on the Industry 40 Model for Advanced Manufacturing,2020,15-58.
[16] 陶飞,刘蔚然,张萌,等.数字孪生五维模型及十大领域应用[J].计算机集成制造系统,2019,25(1):1-18.TAO Fei, LIU Weiran, ZHANG Meng, et al.Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems,2019,25(1):1-18.
[17] JEUNET C,GLIZE B,MCGONIGAL A,et al. Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills:Theoretical background,applications and prospects[J].Neurophysiologie Clinique,2019,49(2):125-136.
[18] K DIVYA B,P. A. KARTHICK,RAMAKRISHNAN S.Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals[J]. Computer Methods in Biomechanics&Biomedical Engineering,2022,25(3):320-332.
[19] LI T,BOLKART T,BLACK M J,et al. Learning a model of facial shape and expression from 4D scans[J]. ACM Transactions on Graphics,2017,36(6):194.
[20] PAVLAKOS G,CHOUTAS V,GHORBANI N,et al.Expressive body capture:3D hands,face,and body from a single image[C]//Proceedings of the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019,10975-10985.
[21] ROMERO J,TZIONAS D,BLACK M J. Embodied hands:Modeling and capturing hands and bodies together[J]. ArXiv preprint:220102610,2022.
[22] INKOL K A,BROWN C,MCNALLY W,et al. Muscle torque generators in multibody dynamic simulations of optimal sports performance[J]. Multibody System Dynamics,2020,50(4):435-452.
[23] SAUL K R,HU X,GOEHLER C M,et al. Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model[J].Computer Methods in Biomechanics and Biomedical Engineering,2015,18(13):1445-1458.
[24] 牛泽海.多视角三维人体姿态估计方法研究[D].北京:中国科学院大学,2024.NIU Zehai. Research on multi-view 3D human pose estimation[D]. Beijing:University of Chinese Academy of Sciences,2024.
[25] CAO Z,SIMON T,WEI S E,et al. Realtime multi-person2D pose estimation using part affinity fields[C]//Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2017,7291-7299.
[26] LIU D,HUANG Y,LIU Z,et al. A skeleton-based assembly action recognition method with feature fusion for human-robot collaborative assembly[J]. Journal of Manufacturing Systems,2024,76:553-566.
[27] ZHANG Z,JI Y,TANG D,et al. Enabling collaborative assembly between humans and robots using a digital twin system[J]. Robotics and Computer-Integrated Manufacturing,2024,86:102691.
[28] FEBRER-NAFRÍA M,NASR A,EZATI M,et al.Predictive multibody dynamic simulation of human neuromusculoskeletal systems:A review[J]. Multibody System Dynamics,2023,58(3):299-339.
[29] SITOLE S P,SUP F C. Continuous prediction of human joint mechanics using EMG signals:A review of model-based and model-free approaches[J]. IEEE Transactions on Medical Robotics and Bionics,2023,5(3):528-546.
[30] HU L,ZHAI D H,YU D,et al. A hybrid framework based on bio-signal and built-in force sensor for human-robot active co-carrying[J]. IEEE Transactions on Automation Science and Engineering,2024,22:3553-3566.
[31] SELVARAJ V,AL-AMIN M,YU X,et al. Real-time action localization of manual assembly operations using deep learning and augmented inference state machines[J].Journal of Manufacturing Systems,2024,72:504-518.
[32] BOLDO M,DE MARCHI M,MARTINI E,et al.Real-time multi-camera 3D human pose estimation at the edge for industrial applications[J]. Expert Systems with Applications,2024,252:124089.
[33] DUARTE L, NETO P. Classification of primitive manufacturing tasks from filtered event data[J]. Journal of Manufacturing Systems,2023,68:12-24.
[34] ZHOU H,YANG G,WANG B,et al. An attention-based deep learning approach for inertial motion recognition and estimation in human-robot collaboration[J]. Journal of Manufacturing Systems,2023,67:97-110.
[35] YI X,ZHOU Y,XU F. Transpose:Real-time 3D human translation and pose estimation with six inertial sensors[J].ACM Transactions on Graphics,2021,40(4):1-13.
[36] SAN-SEGUNDO R, BLUNCK H, MORENOPIMENTEL J,et al. Robust human activity recognition using smartwatches and smartphones[J]. Engineering Applications of Artificial Intelligence,2018,72:190-202.
[37] XIE C,ZHANG D,WU Z,et al. RPM:RF-based pose machines[J]. IEEE Transactions on Multimedia,2023,26:637-649.
[38] ADIB F,HSU C Y,MAO H,et al. Capturing the human figure through a wall[J]. ACM Transactions on Graphics,2015,34(6):1-13.
[39] YUK D G,SOHN J W. User independent hand motion recognition for robot arm manipulation[J]. Journal of Mechanical Science and Technology, 2022, 36(6):2739-2747.
[40] 李瀚哲,张小栋,李睿,等.利用运动准备电位的人体下肢自主运动意图预先感知方法[J].西安交通大学学报,2019,53(10):16-23.LI Hanzhe, ZHANG Xiaodong, LI Rui, et al. A preperception method for voluntary movement intention of lower limb using readiness potential[J]. Journal of Xi'an Jiaotong University,2019,53(10):16-23.
[41] WU H,JIANG D,GAO H. Tactile motion recognition with convolutional neural networks[C]//Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),2017,1572-1577.
[42] KARATSIDIS A,BELLUSCI G,SCHEPERS H M,et al.Estimation of ground reaction forces and moments during gait using only inertial motion capture[J]. Sensors,2016,17(1):75.
[43] CONFORTI I,MILETI I,PANARIELLO D,et al.Validation of a novel wearable solution for measuring L5/S1 load during manual material handling tasks[C]//Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 40&Io T,2020,501-506.
[44] YU J,LI M,ZHANG X,et al. A multi-sensor gesture interaction system for human-robot cooperation[C]//Proceedings of the 2021 IEEE International Conference on Networking,Sensing and Control (ICNSC),2021,1-6.
[45] LIU Z,CHENG Q,SONG C,et al. Cross-scale cascade transformer for multimodal human action recognition[J].Pattern Recognition Letters,2023,168:17-23.
[46] YASAR M S,ISLAM M M,IQBAL T. IMPRINT:Interactional dynamics-aware motion prediction in teams using multimodal context[J]. ACM Transactions on Human-Robot Interaction,2024,13(3):1-29.
[47] 董元发,蒋磊,彭巍,等.融合脑电-肌电信号的人机协作装配意图识别方法[J].中国机械工程,2022,33(17):2071-2078.DONG Yuanfa,JIANG Lei,PENG Wei,et al. Intention recognition method of human robot cooperation assembly based on EEG-EMG signals[J]. China Mechanical Engineering,2022,33(17):2071-2078.
[48] HUANG F,ZENG A,LIU M,et al. DeepFuse:An IMU-aware network for real-time 3D human pose estimation from multi-view image[C]//Proceedings of the Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision,2020,429-438.
[49] WANG B,SONG C,LI X,et al. A deep learning-enabled visual-inertial fusion method for human pose estimation in occluded human-robot collaborative assembly scenarios[J].Robotics and Computer-Integrated Manufacturing,2025,93:102906.
[50] WANG Z,LI P,ZHANG Q,et al. A LiDAR-depth camera information fusion method for human robot collaboration environment[J]. Information Fusion,2025,114:102717.
[51] PATIL A K,BALASUBRAMANYAM A,RYU J Y,et al. An open-source platform for human pose estimation and tracking using a heterogeneous multi-sensor system[J].Sensors,2021,21(7):2340.
[52] FABER G,KINGMA I,CHANG C,et al. Validation of a wearable system for 3D ambulatory L5/S1 moment assessment during manual lifting using instrumented shoes and an inertial sensor suit[J]. Journal of Biomechanics,2020,102:109671.
[53] YIN M-Y,LI J-G. A systematic review on digital human models in assembly process planning[J]. The International Journal of Advanced Manufacturing Technology,2023,125(3):1037-1059.
[54] PAPANAGIOTOU D,SENTERI G,MANITSARIS S.Egocentric gesture recognition using 3D convolutional neural networks for the spatiotemporal adaptation of collaborative robots[J]. Frontiers in Neurorobotics,2021,15:703545.
[55] SLAMA R,SLAMA I,TLAHIG H,et al. An overview on human-centred technologies, measurements and optimisation in assembly systems[J]. International Journal of Production Research,2024,62(14):5336-5358.
[56] PUCHERT P, ROPINSKI T. A3GC-IP:Attention-oriented adjacency adaptive recurrent graph convolutions for human pose estimation from sparse inertial measurements[J]. Computers&Graphics,2023,117:96-104.
[57] 陈彦,张锐,李亚东,等.基于无线信号的人体姿态估计综述[J].雷达学报,2024,13:1-19.CHEN Yan,ZHANG Rui,LI Yadong,et al. Human pose estimation based on wireless signals[J]. Journal of Radars,2024,13:1-19.
[58] JIANG W,XUE H,MIAO C,et al. Towards 3D human pose construction using Wi Fi[C]//Proceedings of the Proceedings of the 26th Annual International Conference on Mobile Computing and Networking,2020,1-14.
[59] 李承翰,胡明皓,李航,等.运动捕捉技术及柔性传感器用于人体连续运动监测的研究进展[J].微纳电子技术,2023,60(11):1703-1714.LI Chenghan,HU Minghao,LI Hang,et al. Research progress of motion capture technology and flexible sensors for continuous human motion monitoring[J].Micronanoelectronic Technology, 2023, 60(11):1703-1714.
[60] CHEN F,LYU H,PANG Z,et al. WristCam:A wearable sensor for hand trajectory gesture recognition and intelligent human-robot interaction[J]. IEEE Sensors Journal,2018,19(19):8441-8451.
[61] SPARROW D,KRUGER K,BASSON A. Human digital twin for integrating human workers in industry[C]//Proceedings of the Proceedings of the International Conference on Competitive Manufacturing (COMA 19) Proceedings,2019:1-6.
[62] JOUKOV V,LIN J F-S,KULIĆD. Closed-chain pose estimation from wearable sensors[C]//Proceedings of the2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids),2019:594-600.
[63] KAICHI T,MARUYAMA T,TADA M,et al. Resolving position ambiguity of imu-based human pose with a single rgb camera[J]. Sensors,2020,20(19):5453.
[64] LIN B S,LEE I J,WANG S P,et al. Residual neural network and long short-term memory-based algorithm for estimating the motion trajectory of inertial measurement units[J]. IEEE Sensors Journal,2022,22(7):6910-6919.
[65] 代磊.基于多模型计算方法的下肢行走动力学分析[D].长春:吉林大学,2024.DAI Lei. Dynamic analysis of lower limb walking based on multi-model calculation method[D]. Changchun:Jilin University,2024.
[66] LIANG W,WANG F,FAN A,et al. Extended application of inertial measurement units in biomechanics:From activity recognition to force estimation[J]. Sensors,2023,23(9):4229.
[67] ZAFAR M H,MOOSAVI S K R,SANFILIPPO F.Hierarchical recurrent-inception residual transformer (HRIRT) for multi-dimensional hand force estimation using force myography sensor[J]. IEEE Sensors Letters,2024,8(9):6011204.
[68] FAN J,ZHENG P,LI S. Vision-based holistic scene understanding towards proactive human-robot collaboration[J]. Robotics and Computer-Integrated Manufacturing,2022,75:102304.
[69] WANG Z,YAN J,YAN G,et al. Multi-scale control and action recognition based human-robot collaboration framework facing new generation intelligent manufacturing[J]. Robotics and Computer-Integrated Manufacturing,2025,91:102847.
[70] DELPRETO J,SALAZAR-GOMEZ A F,GIL S,et al.Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection[J].Autonomous Robots,2020,44:1303-1322.
[71] ZHANG J,LIU H,CHANG Q,et al. Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly[J]. CIRP Annals,2020,69(1):9-12.
[72] ZHANG J,WANG P,GAO R X. Hybrid machine learning for human action recognition and prediction in assembly[J]. Robotics and Computer-Integrated Manufacturing,2021,72:102184.
[73] 王梦思.基于肌电信号和关节角度的正常步态下肢关节力矩估计方法研究[D].成都:电子科技大学,2024.WANG Mengsi. A research on estimation method of lower limb joint torques during normal gait based on electromyographic signals and joint angles[D]. Chengdu:University of Electronic Science and Technology of China,2024.
[74] ELTOUNY K A,LIU W,TIAN S,et al. DE-TGN:Uncertainty-aware human motion forecasting using deep ensembles[J]. IEEE Robotics and Automation Letters,2024,9(3):2192-2199.
[75] MALE J,MARTINEZ-HERNANDEZ U. Deep learning based robot cognitive architecture for collaborative assembly tasks[J]. Robotics and Computer-Integrated Manufacturing,2023,83:102572.
[76] YAO B,YANG B,XU W,et al. Virtual data generation for human intention prediction based on digital modeling of human-robot collaboration[J]. Robotics and Computer-Integrated Manufacturing,2024,87:102714.
[77] VALENTIM D P, COMPER M L C, SANDY MEDEIROS RODRIGUES CIRINO L, et al.Observational methods for the analysis of biomechanical exposure in the workplace:A systematic review[J].Ergonomics,2024(29):1-22.
[78] YUNUS M N H,JAAFAR M H,MOHAMED A S A,et al. Implementation of kinetic and kinematic variables in ergonomic risk assessment using motion capture simulation:A review[J]. International Journal of Environmental Research and Public Health,2021,18(16):8342.
[79] SARDAR S K,LIM C H,YOON S H,et al. Ergonomic risk assessment of manufacturing works in virtual reality context[J]. International Journal of Human-Computer Interaction,2024,40(14):3856-3872.
[80] FERRARI E,GAMBERI M,PILATI F,et al. Motion analysis system for the digitalization and assessment of manual manufacturing and assembly processes[J].IFAC-Papers On Line,2018,51(11):411-416.
[81] RITT M,COSTA A M,MIRALLES C. The assembly line worker assignment and balancing problem with stochastic worker availability[J]. International Journal of Production Research,2016,54(3):907-922.
[82] BORTOLINI M,FACCIO M,GAMBERI M,et al.Motion Analysis System (MAS) for production and ergonomics assessment in the manufacturing processes[J].Computers&Industrial Engineering,2020,139:105485.
[83] KATSAMPIRIS-SALGADO K,DIMITROPOULOS N,GKRIZIS C,et al. Advancing human-robot collaboration:Predicting operator trajectories through AI and infrared imaging[J]. Journal of Manufacturing Systems,2024,74:980-994.
[84] 黄思翰,陈建鹏,徐哲,等.基于大语言模型和机器视觉的智能制造系统人机自主协同作业方法[J].机械工程学报,2025,61(3):130-141.HUANG Sihan,CHEN Jianpeng,XU Zhe,et al. Humanrobot autonomous collaboration method of smart manufacturing systems based on large language model and machine vision[J]. Journal of Mechanical Engineering,2025,61(3):130-141.
[85] SELVARAJ V,AL-AMIN M,TAO W,et al. Intelligent assembly operations monitoring with the ability to detect non-value-added activities as out-of-distribution (OOD) instances[J]. CIRP Annals,2023,72(1):413-416.
[86] 宋学官,何西旺,李昆鹏,等.人体骨骼数字孪生的构建方法及应用[J].机械工程学报,2022,58(18):218-228.SONG Xueguan, HE Xiwang, LI Kunpeng, et al.Construction method and application of human skeleton digital twin[J]. Journal of Mechanical Engineering,2022,58(18):218-228.
[87] YOU Y,CAI B,PHAM D T,et al. A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly[J]. Computers&Industrial Engineering,2025,200:110774.
[88] BITTENCOURT V,SAAKES D,THIEDE S. Surrogate modelling for continuous ergonomic assessment and adaptive configuration of industrial human-centered workplaces[J]. Journal of Manufacturing Systems,2025,79:383-397.
[89] LI C, ZHENG P, ZHOU P, et al. Unleashing mixed-reality capability in deep reinforcement learning-based robot motion generation towards safe human-robot collaboration[J]. Journal of Manufacturing Systems,2024,74:411-421.
[90] GUI H, LI M, YUAN Z. A behavioral conditional diffusion probabilistic model for human motion modeling in multi-action mixed human-robot collaboration[J]. Advanced Engineering Informatics,2024,62:102742.

面向人本智造的人体运动数字孪生研究与应用进展

Research and Application Progress of Human Motion Digital Twin for Human-centric Smart Manufacturing

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	黄思翰, 陈建鹏, 徐哲, 阎艳, 王国新. 基于大语言模型和机器视觉的智能制造系统人机自主协同作业方法[J]. 机械工程学报, 2025, 61(3): 130-141.
[2]	李嘉佳, 易茜, 冯毅雄, 朱鹏兴, 易树平. 人本智造单元中人-智能系统协同双智能体工作机制研究[J]. 机械工程学报, 2025, 61(3): 105-118.
[3]	张洁, 丁鹏飞, 王柏村, 张朋, 吕佑龙, 汪俊亮. 面向人本智造的人机协作：发展演变、融合应用与未来展望[J]. 机械工程学报, 2025, 61(15): 4-20.
[4]	蒋周明矩, 熊异, 王柏村. 面向工业5.0的人机协作增材制造[J]. 机械工程学报, 2024, 60(3): 238-253.
[5]	杨赓, 周慧颖, 王柏村. 数字孪生驱动的智能人机协作：理论、技术与应用[J]. 机械工程学报, 2022, 58(18): 279-291.
[6]	黄思翰, 王柏村, 张美迪, 黄金棠, 朱启章, 杨赓. 面向人本智造的新一代操作工：参考架构、使能技术与典型场景[J]. 机械工程学报, 2022, 58(18): 251-264.
[7]	宋学官, 何西旺, 李昆鹏, 来孝楠, 李忠海. 人体骨骼数字孪生的构建方法及应用[J]. 机械工程学报, 2022, 58(18): 218-228.
[8]	马南峰, 姚锡凡, 陈飞翔, 俞鸿均, 王柯赛. 面向工业5.0的人本智造[J]. 机械工程学报, 2022, 58(18): 88-102.
[9]	姚锡凡, 马南峰, 张存吉, 周佳军. 以人为本的智能制造：演进与展望[J]. 机械工程学报, 2022, 58(18): 2-15.