IEEE Transactions on Human-Machine Systems最新文献_第3页

Supervision of Multiple Remote Tower Centers: Evaluating a New Air Traffic Control Interface Based on Mental Workload and Eye Tracking 多个远程塔台中心的监督：评估一种基于心理负荷和眼动追踪的新型空中交通管制接口

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-03 DOI: 10.1109/THMS.2025.3527136

Leo Julius Materne;Maik Friedrich

{"title":"Supervision of Multiple Remote Tower Centers: Evaluating a New Air Traffic Control Interface Based on Mental Workload and Eye Tracking","authors":"Leo Julius Materne;Maik Friedrich","doi":"10.1109/THMS.2025.3527136","DOIUrl":"https://doi.org/10.1109/THMS.2025.3527136","url":null,"abstract":"Remote air traffic control offers inexpensive and efficient service to multiple airports. Recent research shows that one remote air traffic control officer can safely control up to three low-traffic airports simultaneously. In a multiple remote tower center, airports can be flexibly allocated across air traffic control officers based on prospective traffic loads. The main task of the supervisor in such a center is balancing the workload of each air traffic control officer by allocating airports accordingly. This study analyzes the supervisor's visual attention during interaction with a planning tool for their daily tasks. Five use cases were identified as the main tasks of the supervisor representing a mixture of planned and unplanned events. A mixed methods within-subjects design was used to assess the workload and eye-movement patterns associated with each of these use cases. In total, 15 professional air traffic control officers participated in the study. Workload and eye movement were analyzed independently in relation to the use cases but also in combination with each other. Across all use cases, a small correlation between subjective workload ratings and fixation duration was found, supporting previous findings of fixation duration being associated with information processing. Transitions between areas of interest on the supervisor planning tool provided valuable insights into the layout design of future supervisor planning tools.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"114-123"},"PeriodicalIF":3.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10869643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy Adaptive Controller of a Wearable Assistive Upper Limb Exoskeleton Using a Disturbance Observer 基于扰动观测器的可穿戴辅助上肢外骨骼模糊自适应控制器

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-03 DOI: 10.1109/THMS.2025.3529759

Mohammad Soleimani Amiri;Rizauddin Ramli

{"title":"Fuzzy Adaptive Controller of a Wearable Assistive Upper Limb Exoskeleton Using a Disturbance Observer","authors":"Mohammad Soleimani Amiri;Rizauddin Ramli","doi":"10.1109/THMS.2025.3529759","DOIUrl":"https://doi.org/10.1109/THMS.2025.3529759","url":null,"abstract":"The motivation behind the development of a wearable assistive upper limb exoskeleton robot was to provide comprehensive multijoint therapy by assisting physiotherapists in enhancing the recovery of hemiplegic patients. However, the controlling of an upper limb exoskeleton for rehabilitation is a challenging task because of its nonlinear characteristics. This article presents a novel fuzzy adaptive controller that utilizes a high-dimensional integral-type Lyapunov function for a wearable assistive upper limb exoskeleton. A disturbance observer had been used to tackle uncertainties in the exoskeleton's dynamic model, thereby enhancing the tracking performance of the joints. The aim of this control scheme was to overcome unknown parameters in the dynamic model. The performance of the adaptive controller was validated through human interactive experiments and periodically repeated reference trajectory tests. The results demonstrated that the proposed fuzzy adaptive controller, with the inclusion of a disturbance observer, could effectively compensate for uncertain disturbances and could achieve efficient tracking of the reference trajectory. The statistical analysis revealed that the fuzzy adaptive controller performed 45%, 44%, and 31% less in average error compared to adaptive conventional controllers. The findings ascertained the potential of the proposed controller in improving the recovery of motor functions of hemiplegic patients.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"197-206"},"PeriodicalIF":3.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698219","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

Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images 全局-局部图像感知分数（GLIPS）：评估人工智能生成图像的逼真质量

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-03 DOI: 10.1109/THMS.2025.3527397

Memoona Aziz;Umair Rehman;Muhammad Umair Danish;Katarina Grolinger

{"title":"Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images","authors":"Memoona Aziz;Umair Rehman;Muhammad Umair Danish;Katarina Grolinger","doi":"10.1109/THMS.2025.3527397","DOIUrl":"https://doi.org/10.1109/THMS.2025.3527397","url":null,"abstract":"This article introduces the global-local image perceptual score (GLIPS), an image metric designed to assess the photorealistic image quality of AI-generated images with a high degree of alignment to human visual perception. Traditional metrics such as Fréchet inception distance (FID) and kernel inception distance scores do not align closely with human evaluations. The proposed metric incorporates advanced transformer-based attention mechanisms to assess local similarity and maximum mean discrepancy to evaluate global distributional similarity. To evaluate the performance of GLIPS, we conducted a human study on photorealistic image quality. Comprehensive tests across various generative models demonstrate that GLIPS consistently outperforms existing metrics like FID, structural similarity index measure, and multiscale structural similarity index measure in terms of correlation with human scores. In addition, we introduce the interpolative binning scale, a refined scaling method that enhances the interpretability of metric scores by aligning them more closely with human evaluative standards. The proposed metric and scaling approach not only provide more reliable assessments of AI-generated images but also suggest pathways for future enhancements in image generation technologies.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"223-233"},"PeriodicalIF":3.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698312","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

Deep Radiomics for Autism Diagnosis and Age Prediction 深度放射组学用于自闭症诊断和年龄预测

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-01-29 DOI: 10.1109/THMS.2025.3526957

Ahmad Chaddad

{"title":"Deep Radiomics for Autism Diagnosis and Age Prediction","authors":"Ahmad Chaddad","doi":"10.1109/THMS.2025.3526957","DOIUrl":"https://doi.org/10.1109/THMS.2025.3526957","url":null,"abstract":"Radiomics combined with deep learning is an emerging field within biomedical engineering that aims to extract important characteristics from medical images to develop a predictive model that can support clinical decision-making. This method could be used in the realm of brain disorders, particularly autism spectrum disorder (ASD), to facilitate prompt identification. We propose a novel radiomic features [deep radiomic features (DTF)], involving the use of principal component analysis to encode convolutional neural network (CNN) features, thereby capturing distinctive features related to brain regions in subjects with ASD subjects and their age. Using these features in random forest (RF) models, we explore two scenarios, such as site-specific radiomic analysis and feature extraction from unaffected brain regions to alleviate site-related variations. Our experiments involved comparing the proposed method with standard radiomics (SR) and 2-D/3-D CNNs for the classification of ASD versus healthy control (HC) individuals and different age groups (below median and above median). When using the RF model with DTF, the analysis at individual sites revealed an area under the receiver operating characteristic (ROC) curve (AUC) range of 79%–85% for features, such as the left <italic>lateral-ventricle, <italic>cerebellum-white-matter, and <italic>pallidum, as well as the right <italic>choroid-plexus and <italic>vessel. In the context of fivefold cross validation with the RF model, the combined features (DTF from 3-D CNN, ResNet50, DarketNet53, and NasNet_large with SR) achieved the highest AUC value of 76.67%. Furthermore, our method also showed notable AUC values for predicting age in subjects with ASD (80.91%) and HC (75.64%). The results indicate that DTFs consistently exhibit predictive value in classifying ASD from HC subjects and in predicting age.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"144-154"},"PeriodicalIF":3.5,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698220","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

Efficacy Assessments of Virtual Reality Systems for Immersive Consumer Testing—Two Case Studies With Tortilla Chip Evaluations 沉浸式消费者测试的虚拟现实系统效能评估——两个玉米饼片评估案例研究

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-01-22 DOI: 10.1109/THMS.2024.3524916

Kym K. W. Man;Jeremy A. Patterson;Christopher T. Simons

{"title":"Efficacy Assessments of Virtual Reality Systems for Immersive Consumer Testing—Two Case Studies With Tortilla Chip Evaluations","authors":"Kym K. W. Man;Jeremy A. Patterson;Christopher T. Simons","doi":"10.1109/THMS.2024.3524916","DOIUrl":"https://doi.org/10.1109/THMS.2024.3524916","url":null,"abstract":"In sensory science, the use of immersive technologies has gained popularity for their ability to restore relevant contextual factors during consumer testing and overcome the low ecological validity of controlled laboratory environments. Despite this, there is scant literature evaluating the effectiveness of immersive technologies in facilitating virtual product evaluation experiences; this is especially true with virtual reality (VR) headsets and the unique technical challenges associated with this technology. To fill this gap, we assessed virtual presence, system usability, engagement, and ease of task completion, in subjects using two iterations of a VR application (controllers or hand tracking) designed to address the major limitations of current systems. Results revealed that both systems exceeded the benchmark usability score of 68. System 1 (controllers) performed better for interactions with the virtual tablet interface to answer questions, whereas interactions with the food objects were easier using System 2 (hand tracking). Participants also experienced a high sense of virtual presence using both systems. When measured in System 2, a high level of subject engagement during the immersive product evaluations was observed. These studies indicate that collecting both quantitative and qualitative feedback on VR systems can provide useful insights and directions for application optimization to ensure valid investigation of context effects in future research.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"266-277"},"PeriodicalIF":3.5,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698299","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

Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition 学习手对手和人对人互动识别的相互激励

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-01-17 DOI: 10.1109/THMS.2024.3522974

Mengyuan Liu;Chen Chen;Songtao Wu;Fanyang Meng;Hong Liu

{"title":"Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition","authors":"Mengyuan Liu;Chen Chen;Songtao Wu;Fanyang Meng;Hong Liu","doi":"10.1109/THMS.2024.3522974","DOIUrl":"https://doi.org/10.1109/THMS.2024.3522974","url":null,"abstract":"Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention for various applications in the field of video analysis and human–robot interaction. Considering the success of graph convolution in modeling topology-aware features from skeleton data, recent methods commonly operate graph convolution on separate entities and use late fusion for interactive action recognition, which can barely model the mutual semantic relationships between pairwise entities. To this end, we propose a mutual excitation graph convolutional network (me-GCN) by stacking mutual excitation graph convolution (me-GC) layers. Specifically, me-GC uses a mutual topology excitation module to firstly extract adjacency matrices from individual entities and then adaptively model the mutual constraints between them. Moreover, me-GC extends the above idea and further uses a mutual feature excitation module to extract and merge deep features from pairwise entities. Compared with graph convolution, our proposed me-GC gradually learns mutual information in each layer and each stage of graph convolution operations. Extensive experiments on a challenging hand-to-hand interaction dataset, i.e., the Assembely101 dataset, and two large-scale human-to-human interaction datasets, i.e., NTU60-Interaction and NTU120-Interaction consistently verify the superiority of our proposed method, which outperforms the state-of-the-art GCN-based and Transformer-based methods.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"134-143"},"PeriodicalIF":3.5,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698216","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

Receding-Horizon Reinforcement Learning for Time-Delayed Human–Machine Shared Control of Intelligent Vehicles 智能车辆时滞人机共享控制的后退地平线强化学习

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-01-16 DOI: 10.1109/THMS.2024.3496899

Xinxin Yao;Jiahang Liu;Xinglong Zhang;Xin Xu

{"title":"Receding-Horizon Reinforcement Learning for Time-Delayed Human–Machine Shared Control of Intelligent Vehicles","authors":"Xinxin Yao;Jiahang Liu;Xinglong Zhang;Xin Xu","doi":"10.1109/THMS.2024.3496899","DOIUrl":"https://doi.org/10.1109/THMS.2024.3496899","url":null,"abstract":"Human–machine shared control has recently been regarded as a promising paradigm to improve safety and performance in complex driving scenarios. One crucial task in shared control is dynamically optimizing the driving weights between the driver and the intelligent vehicle to adapt to dynamic driving scenarios. However, designing an optimal human–machine shared controller with guaranteed performance and stability is challenging due to nonnegligible time delays caused by communication protocols and uncertainties in driver behavior. This article proposes a novel receding-horizon reinforcement learning approach for time-delayed human–machine shared control of intelligent vehicles. First, we build a multikernel-based data-driven model of vehicle dynamics and driving behavior, considering time delays and uncertainties of drivers' actions. Second, a model-based receding horizon actor–critic learning algorithm is presented to learn an explicit policy for time-delayed human–machine shared control online. Unlike classic reinforcement learning, policy learning of the proposed approach is performed according to a receding-horizon strategy to enhance learning efficiency and adaptability. In theory, the closed-loop stability under time delays is analyzed. Hardware-in-the-loop experiments on the time-delayed human–machine shared control of intelligent vehicles have been conducted in variable curvature road scenarios. The results demonstrate that our approach has significant improvements in driving performance and driver workload compared with pure manual driving and previous shared control methods.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"155-164"},"PeriodicalIF":3.5,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698298","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

Chatbot Dialog Design for Improved Human Performance in Domain Knowledge Discovery 提高人类在领域知识发现中的表现的聊天机器人对话设计

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-01-07 DOI: 10.1109/THMS.2024.3514742

Roland Oruche;Xiyao Cheng;Zian Zeng;Audrey Vazzana;MD Ashraful Goni;Bruce Wang Shibo;Sai Keerthana Goruganthu;Kerk Kee;Prasad Calyam

{"title":"Chatbot Dialog Design for Improved Human Performance in Domain Knowledge Discovery","authors":"Roland Oruche;Xiyao Cheng;Zian Zeng;Audrey Vazzana;MD Ashraful Goni;Bruce Wang Shibo;Sai Keerthana Goruganthu;Kerk Kee;Prasad Calyam","doi":"10.1109/THMS.2024.3514742","DOIUrl":"https://doi.org/10.1109/THMS.2024.3514742","url":null,"abstract":"The advent of machine learning (ML) has led to the widespread adoption of developing task-oriented dialog systems for scientific applications (e.g., science gateways) where voluminous information sources are retrieved and curated for domain users. Yet, there still exists a challenge in designing chatbot dialog systems that achieve widespread diffusion among scientific communities. In this article, we propose a novel Vidura advisor design framework (VADF) to develop dialog system designs for information retrieval (IR) and question-answering (QA) tasks, while enabling the quantification of system utility based on human performance in diverse application environments. We adopt a socio-technical approach in our framework for designing dialog systems by utilizing domain expert feedback, which features a sparse retriever for enabling accurate responses in QA settings using linear interpolation smoothing. We apply our VADF for an exemplar science gateway, viz. KnowCOVID-19, to conduct experiments that demonstrate the utility of dialog systems based on IR and QA performance, application utility, and perceived adoption. Experimental results show our VADF approach significantly improves IR performance against retriever baselines (up to 5% increase) and QA performance against large language models (LLMs) such as ChatGPT (up to 43% increase) on scientific literature datasets. In addition, through a usability survey, we observe that measuring application utility and human performance when applying VADF to KnowCOVID-19 translates to an increase in perceived community adoption.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"207-222"},"PeriodicalIF":3.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698213","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

Human-Following Control Method Based on Adaptive Recurrent PID Controller With Self-Tuning Filter 基于自适应递归PID自整定滤波器的人随动控制方法

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-12-27 DOI: 10.1109/THMS.2024.3515045

Wenfeng Li;Jinglong Zhou;Shaoyong Jiang;Chaoqun Wang;Anning Yang

{"title":"Human-Following Control Method Based on Adaptive Recurrent PID Controller With Self-Tuning Filter","authors":"Wenfeng Li;Jinglong Zhou;Shaoyong Jiang;Chaoqun Wang;Anning Yang","doi":"10.1109/THMS.2024.3515045","DOIUrl":"https://doi.org/10.1109/THMS.2024.3515045","url":null,"abstract":"The research on human-following robot is important for practical applications. It is a hot field of human–machine technology. This article proposes an adaptive recurrent proportional integral differential (PID) control algorithm with self-tuning filter based on vision to address the issue of insufficient recognition accuracy of specific following targets in the presence of occlusion, multiple people, or deformation. It also aims to further improve the control accuracy and immunity of a human-following robot. First, a depth camera-based red green blue (RGB) picture and a depth image are acquired. The person reidentification algorithm and the YOLOv8 algorithm are used to detect and track the targets. The spatial position information of the targets is calculated by the depth image. Additionally, the orientation proportional differential (PD) controller and the speed proportional integral (PI) controller are built. Its foundation is the discrepancy between the relative posture of the user and the robot. In order to minimize sensor data fluctuations and lessen the negative impacts of relative positional instability, a self-tuning filter is developed. To remember the relative postures between the robot and the user in the history window, an adaptive recurrent mechanism is suggested. The controller has the ability to output the control quantity in an adaptive manner based on the current system state. Finally, experiments are conducted to verify the reliability of the proposed method. The experimental findings demonstrate that the visual pedestrian tracking algorithm proposed in this article is highly adaptable. Compared to the traditional PID, fractional-order PID, and virtual spring model, our method demonstrates significant enhancements, reducing the average distance error by 64.29%, 57.14%, and 60.52% in steering scenarios, and by 42.86%, 40.00%, and 40.00% in straight-ahead scenarios, respectively.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"48-57"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106847","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

Cognitive Load-Based Affective Workload Allocation for Multihuman Multirobot Teams 基于认知负荷的多人多机器人团队情感工作量分配

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-12-27 DOI: 10.1109/THMS.2024.3509223

Wonse Jo;Ruiqi Wang;Baijian Yang;Daniel Foti;Mo Rastgaar;Byung-Cheol Min

{"title":"Cognitive Load-Based Affective Workload Allocation for Multihuman Multirobot Teams","authors":"Wonse Jo;Ruiqi Wang;Baijian Yang;Daniel Foti;Mo Rastgaar;Byung-Cheol Min","doi":"10.1109/THMS.2024.3509223","DOIUrl":"https://doi.org/10.1109/THMS.2024.3509223","url":null,"abstract":"The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multirobot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks, such as monitoring, exploration, and search and rescue operations. This article presents a deep reinforcement learning-based affective workload allocation controller specifically for multihuman multirobot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multirobot systems. The operators' performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multihuman multirobot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators' cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multihuman multirobot teams.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 1","pages":"23-36"},"PeriodicalIF":3.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106845","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