IEEE Transactions on Human-Machine Systems最新文献

{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2026.3718367","DOIUrl":"https://doi.org/10.1109/THMS.2026.3718367","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695034","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}

{"title":"Quantitative Trust and Behavioral Measurement Tools for Optimizing Human–Robot Interaction","authors":"Darya Zanjanpour;Harry Chen;Hugh H.T. Liu;Jason E. Plaks","doi":"10.1109/THMS.2026.3691244","DOIUrl":"https://doi.org/10.1109/THMS.2026.3691244","url":null,"abstract":"Quantifying trust in human–robot interaction is a significant challenge due to the lack of a universally accepted definition of “trust” in this domain. Existing approaches often rely on static self-report measures, which are limited in capturing dynamic trust behaviors in safety critical contexts such as advanced air mobility. To address this gap, we introduce a novel empirical framework for quantifying trust in this domain. The proposed framework integrates performance, behavioral, and data-driven metrics to assess how users calibrate their trust during simulated flight missions. Four distinct trust-measurement tools are presented: efficiency factor, obstacle clearance factor, manual/automatic mode usage, and geometric difference. In a user study with 40 participants across 30 simulation runs, the results show that 1) trust in the automation increases with operator experience, as evidenced by improved efficiency (<inline-formula><tex-math>$r = -0.23$</tex-math></inline-formula>, <inline-formula><tex-math>$p &lt; 10^{-12}$</tex-math></inline-formula>) and fewer manual interventions (<inline-formula><tex-math>$r = -0.22$</tex-math></inline-formula>, <inline-formula><tex-math>$p = 7.6times 10^{-15}$</tex-math></inline-formula>); 2) stable individual differences (e.g., risk tolerance) significantly influence trust-related metrics, with obstacle clearance distances remaining consistent for most participants across runs (<inline-formula><tex-math>$r = -0.13$</tex-math></inline-formula>, <inline-formula><tex-math>$p = 1.9times 10^{-5}$</tex-math></inline-formula>); and 3) the geometric difference metric captures nuanced behavioral variations, revealing distinct trust-based operational strategies among users. By integrating performance metrics and behavioral patterns, this multidimensional framework offers practical tools for enhancing the trustworthiness of autonomous systems. It fills a key gap in current trust assessment approaches, supports the development of safer and more effective advanced air mobility systems, and contributes to the broader advancement of human–robot interaction research.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"704-713"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695059","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}

{"title":"Reduced Taxi Delays With Intelligent Departure Metering Advisory Tool","authors":"Hasnain Ali;Duc-Thinh Pham;Sameer Alam;Brian Hilburn","doi":"10.1109/THMS.2026.3678568","DOIUrl":"https://doi.org/10.1109/THMS.2026.3678568","url":null,"abstract":"Airport taxi delays pose significant challenges to airlines, passengers, and environmental sustainability. Addressing these delays requires synchronizing uncertain airside operations, including pushback sequencing, taxiway movements, and runway take-offs. This article presents the intelligent departure metering advisory tool (I-MATE), an artificial intelligence (AI)-based decision support system that recommends pushback timings to air traffic controllers (ATCOs) to reduce taxi delays while balancing downstream runway throughput. I-MATE's recommendation outcomes unfold over a longer time horizon compared to other AI-driven decision support systems in air traffic control, such as conflict resolution tools, posing a greater cognitive challenge for ATCOs. We conducted validation experiments to assess the efficacy and acceptability of I-MATE in assisting ATCOs to manage airside traffic. The study revealed a spectrum of compliance with I-MATE recommendations among ATCOs, highlighting the complex interplay between human behavior and AI-driven decision support systems. While ATCOs rated I-MATE highly for usefulness and reliability, concerns regarding transparency and explainability emerged. ATCO feedback also emphasized the importance of considering individual differences and human factors in the design of such systems. This research underscores the value of AI-based decision support systems in complex, dynamic environments, particularly for addressing the cognitive challenge of balancing long-term outcomes with immediate actions.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"714-722"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694809","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}

{"title":"Exploring the Effect of Gaze and Distance Guidance in Room-Scale Virtual Reality","authors":"Haopeng Lu;Huiwen Ren;Li Song;Xinfeng Zhang;Shanshe Wang;Siwei Ma;Wen Gao","doi":"10.1109/THMS.2026.3700730","DOIUrl":"https://doi.org/10.1109/THMS.2026.3700730","url":null,"abstract":"The growing popularity of virtual reality (VR) provides new opportunities for cultural heritage protection and online education. Many studies have investigated how to guide users to move to a specific view/position. However, the coguided interaction of position and gaze is still under researched. In this article, a novel interaction model named GazeTance Guidance (GTG) is proposed. This model utilizes head rotation and viewing distance to improve the effect of room-scale user guidance. To explore the efficiency of GTG, we conducted two within-subject studies with 25 and 31 participants navigating through virtual museums: first, we evaluated user acceptance of audio commentary and virtual guidance markers in terms of presence and user experience, and assessed the feasibility of memory tasks. second, we separately investigated the effects of gaze and distance guidance on motion sickness, cognitive load, memory efficiency, and user behavior. The experimental results prove that the guidance markers do not affect user experience and sense of presence. Compared with independent effects, the combination of gaze and distance guidance can reduce the user's motion sickness and significantly improve users' memory effect. These findings provide new inspiration for the interaction design of complex VR tours.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"694-703"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694837","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}

{"title":"Advancing Automated Reporting in Manufacturing With Dual-Channel GCN and CNN Architectures","authors":"Meng Han;Liyuan Liu;Xuna Wang;Hong Lyu;Jiahui Yu","doi":"10.1109/THMS.2026.3696276","DOIUrl":"https://doi.org/10.1109/THMS.2026.3696276","url":null,"abstract":"The automation of reporting in the manufacturing sector, enabled by advanced sensing technologies and sophisticated software, offers considerable promise for enhancing operational efficiency and profitability. Despite substantial economic benefits, fully realizing automated reporting remains hindered by critical challenges in human action recognition. These obstacles include visual occlusions in dynamic environments, heavy reliance on large-scale labeled video datasets, requirements for real-time activity identification, and the complexity of recognizing sequential and interdependent tasks typical in manufacturing settings. This article introduces a novel CNN-based framework specifically designed to overcome these challenges, thereby enhancing automated reporting capabilities in manufacturing. Our proposed method incorporates innovative geometric convolution layers, significantly improving the accuracy of motion identification and posture estimation under challenging conditions. To reduce dependence on extensive labeled datasets, we integrate a self-supervised learning strategy, leveraging unlabeled video data effectively. Moreover, we propose a robust dual-channel graph convolutional network that concurrently addresses multitask action recognition and prediction, effectively capturing complex sequential activities. Collectively, these contributions equip manufacturers with the tools necessary for precise, real-time monitoring of worker actions, enabling optimized operations, enhanced worker safety, and ultimately, improved profitability.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"907-916"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694932","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}

{"title":"Adaptive Lévy Flight-Based Optimization for Fetal ECG Recovery and Arrhythmia Detection","authors":"Shivangi Diwan;Mridu Sahu;Varun Bajaj","doi":"10.1109/THMS.2026.3687259","DOIUrl":"https://doi.org/10.1109/THMS.2026.3687259","url":null,"abstract":"Early detection of fetal cardiac abnormality facilitates timely intervention and management. Existing noninvasive fetal electrocardiogram (fECG) extraction techniques often rely on fixed or heuristic optimization parameters and segment-level evaluation strategies, limiting robustness under nonstationary maternal–fetal signal overlap and introducing potential information leakage. This work presents an adaptive optimization framework utilizing the adaptive Lévy flight-based cuckoo search (ALCS) method for improved fECG extraction and detection of fetal arrhythmias, contributing to intelligent human–machine systems in obstetric monitoring. The framework employs ALCS as an outer loop optimizer to determine an optimal initialization of the least mean square (LMS) filter coefficients for adaptive noise cancellation (ANC), where a convergence-aware Lévy flight parameter governs the search process. The Lévy parameter <inline-formula><tex-math>$lambda$</tex-math></inline-formula> evolves adaptively within <inline-formula><tex-math>$[1.5, 2.0]$</tex-math></inline-formula> enabling an effective transition from exploration to exploitation. The proposed optimizer achieves approximately 1.8–<inline-formula><tex-math>$2.0times$</tex-math></inline-formula> faster convergence than standard LMS, reducing the mean-squared error by <inline-formula><tex-math>$sim$</tex-math></inline-formula>74% to 0.10 within 50 iterations. A comprehensive feature set exceeding 100 descriptors is extracted from fECG signals and evaluated using ensemble classifiers. Ensuring a leakage-safe evaluation protocol, the experiments were conducted using subjectwise grouped cross-validation with foldwise feature selection on the noninvasive fetal ECG arrhythmia database available from PhysioNet. The proposed framework achieves robust arrhythmia detection, attaining 91.2% accuracy and 95.0% area under the curve across unseen subjects. To quantify systematic bias and agreement across evaluation stages, Bland–Altman analysis is employed to provide clinical interpretability beyond aggregate scores. Overall, the results demonstrate that convergence-aware Lévy-based optimization improves ANC performance for fECG analysis, while acknowledging dataset scale and offline evaluation limitations.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"848-857"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694977","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}

{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2026.3718365","DOIUrl":"https://doi.org/10.1109/THMS.2026.3718365","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695029","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}

{"title":"Identifying Critical Breakpoints and Latency Effects During Remote Backup Driving","authors":"Xingjian Ma;Anthony D. McDonald","doi":"10.1109/THMS.2026.3691492","DOIUrl":"https://doi.org/10.1109/THMS.2026.3691492","url":null,"abstract":"As Level 4 driving automation progresses, remote driving remains a critical fallback for commercial trucks operating at the edge of their operational design domains. However, network latency presents a major challenge, impairing driving performance, increasing cognitive workload, and reducing trust in the remote driving system. This study investigates how latency affects human factors and driving performance in remote driving scenarios through a within-subject driving simulator experiment. Results showed that latency significantly increased perceived workload and reduced trust, with no direct effect on fatigue or situation awareness. Driving performance was also affected: median speed decreased, steering corrections intensified, and steering entropy increased under higher latency. Segment analysis revealed that lane-keeping tasks—particularly in curved road segments—were most sensitive to latency, exhibiting the strongest degradation in control stability. In contrast, lane-changing maneuvers were less affected, likely due to their discrete and anticipatory nature. Segmented regression identified critical latency breakpoints (347–480 ms), beyond which trust in the remote driving system and driving performance exhibited substantial changes. These findings highlight the need for latency-aware, context-sensitive design strategies, including predictive assistance, and adaptive feedback interfaces, to support safe and efficient remote truck operation under fluctuating network conditions.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"804-814"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694784","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}

{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2026.3718363","DOIUrl":"https://doi.org/10.1109/THMS.2026.3718363","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11644440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694792","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}

{"title":"Visualizing Turnability Affordance Enhances Drivers’ Maneuvers","authors":"Emilie Dubuisson;Antoine H. P. Morice","doi":"10.1109/THMS.2026.3689175","DOIUrl":"https://doi.org/10.1109/THMS.2026.3689175","url":null,"abstract":"While the design of advanced driver assistance systems is of concern to all car manufacturers today, nothing has yet been devised to facilitate forward tight-turn maneuvers. The handling capabilities of cars in forward gear are limited. This suggests that turns are controlled by an affordance-based mechanism and that it would be possible to assist drivers by allowing them to visualize these affordances in a new interface using the ecological interface design approach. We formalized the possibilities of making a tight turn to enter a narrow orthogonal alleyway as an affordance and designed a visual dashboard interface that makes it visible. In a fixed-base driving simulator, we investigated whether drivers could benefit from this new interface to improve their decision-making and control of the turn. Three groups of drivers controlled the steering of low-, standard-, and high-handling cars at a constant speed onto a narrow orthogonal alleyway. We manipulated the required path curvature of the turn. In half of the trials, drivers were assisted by our interface. Without the interface, drivers did not fully perceive the possibility of turning toward the alleyway. With the interface, drivers reduced the number of collisions and failed turn attempts when turns were not possible, and otherwise maximized successful turn maneuvers. We discuss the benefits of the ecological interface for improving decision-making and control of turning maneuvers and propose perspectives on implementing such interface in real-life situations.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"56 4","pages":"794-803"},"PeriodicalIF":4.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694915","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}