IEEE Transactions on Human-Machine Systems最新文献_第10页

Applications of Neuromorphic/Event Camera in Robotics With Human in Loop: A Systematic Review, Datasets, and Challenges 神经形态/事件相机在人环机器人中的应用：系统回顾、数据集和挑战

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-12-03 DOI: 10.1109/THMS.2025.3628064

Muhammad Hamza Zafar;Syed Kumayl Raza Moosavi;Filippo Sanfilippo

{"title":"Applications of Neuromorphic/Event Camera in Robotics With Human in Loop: A Systematic Review, Datasets, and Challenges","authors":"Muhammad Hamza Zafar;Syed Kumayl Raza Moosavi;Filippo Sanfilippo","doi":"10.1109/THMS.2025.3628064","DOIUrl":"https://doi.org/10.1109/THMS.2025.3628064","url":null,"abstract":"The evolution of industrial robotics has advanced from isolated, caged systems through basic human–robot interaction (HRI) to sophisticated human–robot collaboration (HRC). However, conventional vision systems based on red, green, blue (RGB) cameras remain a significant limiting factor in realizing the full potential of collaborative automation. This comprehensive review examines the transformative role of event cameras in advancing HRC capabilities and addressing current limitations in industrial settings. Event cameras, with their microsecond-level temporal resolution and robust performance under challenging lighting conditions, offer substantial advantages over traditional RGB cameras that are constrained by fixed frame rates and ambient lighting dependencies. We present a systematic framework for leveraging event cameras to enhance the human state understanding in collaborative robotics, encompassing real-time detection of poses, gestures, facial expressions, and emotional states. This framework addresses fundamental challenges in workplace safety and collaborative efficiency while enabling more sophisticated and responsive HRC systems. Our review synthesizes recent research developments in event camera applications specific to HRC, providing a detailed comparative analysis of their advantages over conventional vision systems. We identify emerging opportunities and potential research directions for advancing event-based vision in industrial robotics. In addition, we examine integration challenges and propose strategies for implementing event camera technology in existing industrial infrastructure. This work contributes valuable insights into the future trajectory of adaptive and intuitive HRC systems, offering a roadmap for researchers and practitioners in the field of industrial automation.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 1","pages":"32-47"},"PeriodicalIF":4.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Operational and Biomechanical Evaluation of a Wrist Exoskeleton Prototype for Assisting Meat-Cutting Tasks 腕部外骨骼原型协助切肉任务的操作和生物力学评估

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-12-03 DOI: 10.1109/THMS.2025.3632876

Aurélie Tomezzoli;Mathieu Gréau;Charles Pontonnier

{"title":"Operational and Biomechanical Evaluation of a Wrist Exoskeleton Prototype for Assisting Meat-Cutting Tasks","authors":"Aurélie Tomezzoli;Mathieu Gréau;Charles Pontonnier","doi":"10.1109/THMS.2025.3632876","DOIUrl":"https://doi.org/10.1109/THMS.2025.3632876","url":null,"abstract":"Although a growing number of exoskeletons have been developed for occupational applications, wrist exoskeletons remain relatively rare. However, in the meat processing industry, elbow and hand-wrist musculoskeletal disorders are particularly common. The aim of this article was to assess the potential effectiveness and risks of a 670 g wrist exoskeleton prototype designed to assist operators during meat-cutting tasks. Six professional butchers performed three standardized tasks reproducing meat-cutting gestures in foam, in three randomized experimental conditions: 1) without exoskeleton, 2) wearing the exoskeleton passive, with brakes off, and 3) using it with brakes activated, locked in a static position. Cutting forces were recorded using an instrumented table, joint angles using an optoelectronic motion capture system, muscle activity using surface electromyographic (EMG), and user experience was assessed using questionnaires. The cutting inaccuracy was defined as the area between the prescribed task and the actual cut on the foam surface. Joint torques were estimated by inverse dynamics with and without taking the exoskeleton’s mass into account, to isolate its effect. Linear mixed-effects statistical models were fitted. With the exoskeleton active during tasks, EMG activity was decreased of up to 18.7% (<italic>p < 0.01 to <italic>p < 0.001) in the wrist flexors and increased of up to 61.7% (not significant to <italic>p < 0.05) in the upper trapezius. Shoulder elevation joint torques were increased of up to 39.7% (<italic>p < 0.001), mainly due to the exoskeleton mass. The proposed multicriteria exoskeleton evaluation has provided guidance for following prototyping stages. Too heavy wrist exoskeletons could increase the risk of shoulder tendinitis for such tasks.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 1","pages":"58-67"},"PeriodicalIF":4.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Transactions on Human-Machine Systems Information for Authors IEEE人机系统信息汇刊

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-12-02 DOI: 10.1109/THMS.2025.3630230

引用次数: 0

Call for Papers: IEEE Transactions on Human-Machine Systems 论文征集：IEEE人机系统汇刊

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-12-02 DOI: 10.1109/THMS.2025.3630213

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-12-02 DOI: 10.1109/THMS.2025.3630228

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-12-02 DOI: 10.1109/THMS.2025.3630226

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SMHNet: Self-Supervised Multiscale Hierarchical Network for High Fidelity 3-D Face Reconstruction 基于自监督多尺度层次网络的高保真三维人脸重建

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-11-27 DOI: 10.1109/THMS.2025.3627872

Sizhuang Zhang;Ying Sun;Derui Ding;Hui Yu

引用次数: 0

A Recycling-Driven Dynamic Budget Allocation Strategy for Human–Agent Collaboration 基于循环驱动的人- agent协作动态预算分配策略

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-11-25 DOI: 10.1109/THMS.2025.3631490

Xinrui Tao;Yuping Tu;Jiadi Liu;Ying Wang;Fan Yang;Quyuan Wang

引用次数: 0

Predicting Trust Dynamics Type Using Seven Personal Characteristics 利用七个个人特征预测信任动态类型

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-11-25 DOI: 10.1109/THMS.2025.3627578

Hyesun Chung;X. Jessie Yang

{"title":"Predicting Trust Dynamics Type Using Seven Personal Characteristics","authors":"Hyesun Chung;X. Jessie Yang","doi":"10.1109/THMS.2025.3627578","DOIUrl":"https://doi.org/10.1109/THMS.2025.3627578","url":null,"abstract":"This study aims to explore the associations between individuals’ trust dynamics in automated/autonomous technologies and their personal characteristics, and to further examine whether personal characteristics can be used to predict a user’s trust dynamics type. The experimental data involved 130 participants who performed a simulated surveillance task that consisted of a compensatory tracking task and a threat detection task. An imperfect automated threat detector assisted participants in the detection task. Using a pre-experimental survey covering 12 constructs and 28 dimensions, we collected data on participants’ personal characteristics. Based on the experimental data, we performed k-means clustering and identified three trust dynamics types. Subsequently, we conducted one-way analyses of variance to evaluate differences among the three trust dynamics types in terms of personal characteristics, behaviors, performance, and postexperimental ratings. Participants were clustered into three groups, namely Bayesian decision makers, disbelievers, and oscillators. Results showed that the clusters differ significantly in seven personal characteristics: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. The disbelievers tend to have high <italic>neuroticism and low <italic>performance expectancy. The oscillators tend to have higher scores in <italic>masculinity, <italic>positive affect, <italic>extraversion, and <italic>intellect. We also found significant differences in behaviors, performance, and postexperimental ratings across the three groups. The disbelievers are the least likely to blindly follow the recommendations made by the automated threat detector. Based on the significant personal characteristics, we developed a decision tree model to predict the trust dynamics type with an accuracy of 70% . This model offers promising implications for identifying individuals whose trust dynamics may deviate from a Bayesian pattern.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 1","pages":"147-159"},"PeriodicalIF":4.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Personalized Model-Driven Adaptive Task Facilitates Visuomotor Skill Learning Mediated by Promoting Flow Experience 个性化模型驱动的自适应任务通过促进心流体验促进视觉运动技能学习

IF 4.4 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-11-19 DOI: 10.1109/THMS.2025.3627559

Bohao Tian;Dinghao Xue;Yilei Zheng;Shijun Zhang;Yuru Zhang;Dangxiao Wang

{"title":"Personalized Model-Driven Adaptive Task Facilitates Visuomotor Skill Learning Mediated by Promoting Flow Experience","authors":"Bohao Tian;Dinghao Xue;Yilei Zheng;Shijun Zhang;Yuru Zhang;Dangxiao Wang","doi":"10.1109/THMS.2025.3627559","DOIUrl":"https://doi.org/10.1109/THMS.2025.3627559","url":null,"abstract":"The ability to rapidly acquire novel visuomotor skills is essential for daily functioning tasks such as motor rehabilitation, surgical operation, and mechanical assembly. Previous research suggested that experiencing flow can enhance learning outcomes. Although dynamic difficulty adjustment (DDA) has been commonly used to induce flow and maximize engagement, most existing methods rely on model-free, stepwise adaptations that lack quantitative, model-based support. In this study, we proposed a personalized, model-driven DDA approach to facilitate visuomotor skill acquisition by enhancing flow during the learning process. We implemented an adaptive fine fingertip force control task with DDA based on optimal control principles to train the visuomotor skills. This DDA updated task difficultly using real-time multiple performance metrics, with parameters derived from an individually fitted model that captures each user's motor behavior during the task. A user study, involving two groups, compared the effects of a model-driven adaptive task with a model-free control task. Results from the flow state scale and physiological recordings demonstrated that the model-driven task elicited significantly higher levels of flow than the model-free task. Moreover, participants in the model-driven group showed a notably higher learning rate in visuomotor skills (19%) compared to the model-free group (8%). These findings underscore the potential of integrating personalized modeling and optimal control theory to optimize user experience and accelerate learning outcomes in DDA frameworks when building adaptive human–machine interaction systems.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 1","pages":"160-170"},"PeriodicalIF":4.4,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0