Pub Date : 2025-12-05DOI: 10.1109/THMS.2025.3640886
{"title":"2025 Index IEEE Transactions on Human-Machine Systems","authors":"","doi":"10.1109/THMS.2025.3640886","DOIUrl":"https://doi.org/10.1109/THMS.2025.3640886","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"1065-1092"},"PeriodicalIF":4.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11281498","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674784","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}
Pub Date : 2025-12-02DOI: 10.1109/THMS.2025.3630230
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2025.3630230","DOIUrl":"https://doi.org/10.1109/THMS.2025.3630230","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"C4-C4"},"PeriodicalIF":4.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652164","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}
Pub Date : 2025-12-02DOI: 10.1109/THMS.2025.3630213
{"title":"Call for Papers: IEEE Transactions on Human-Machine Systems","authors":"","doi":"10.1109/THMS.2025.3630213","DOIUrl":"https://doi.org/10.1109/THMS.2025.3630213","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"1064-1064"},"PeriodicalIF":4.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652157","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}
Pub Date : 2025-12-02DOI: 10.1109/THMS.2025.3630228
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2025.3630228","DOIUrl":"https://doi.org/10.1109/THMS.2025.3630228","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652182","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}
Pub Date : 2025-12-02DOI: 10.1109/THMS.2025.3630226
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2025.3630226","DOIUrl":"https://doi.org/10.1109/THMS.2025.3630226","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11272141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652174","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}
Pub Date : 2025-11-04DOI: 10.1109/THMS.2025.3619064
Long Chen;Jiatong He;Lei Zhang;Minpeng Xu;Zhongpeng Wang;Dong Ming
Objective: Emergency anticipation (EA) refers to the brain’s rapid perceptual, cognitive, and motor preparation in response to imminent emergencies. Timely decoding of EA can facilitate proactive responses before full behavioral execution, which is critical in real-world scenarios such as avoiding hazards or mitigating accidents. However, the cortical activation underlying the EA process has not been fully explored. This study aims to analyze the neural activity of the EA process and explore the feasibility of detecting emergency motor intention in conjunction with brain-computer interface (BCI) technology. Methods: We designed a new emergency state induction paradigm in the virtual environment, including a target task (emergency anticipation, EA) and two baseline tasks (emergency anticipation execution, EAE, visual observation, VO). A total of 31 healthy subjects were recruited for the offline experiment. The cortical responses during the EA process were quantified by analyzing event-related potential, movement-related cortical potential, and event-related spectral perturbation. Discriminative canonical pattern matching, common spatial patterns, and shrinkage linear discriminant analysis were employed to perform binary classification. Six subjects participated in the pseudo-online asynchronous experiment to valid the feasibility of identifying emergency motor intention. Results: The results showed that the cascading process associated with EA existed in both the temporal and spectral domains. Particularly, temporal domain feature demonstrated superior classification performance, with averages of 90.13% (>80% chance level). The pseudo-online evaluation showed that the system response time with an average of 257.12 ms, which was 35 ms faster than the behavioral response. Significance: Our work demonstrated the cascading process of perceptual recognition, cognitive evaluation, and motor preparation during the EA processes and provided preliminary evidence supporting the feasibility of detecting emergency motor intentions. These findings lay a theoretical foundation for extending the application of BCI technology to rapid control scenarios.
{"title":"Emergency Motor Intention Detection Based on Unpredictable Anticipatory Activity: An EEG Study","authors":"Long Chen;Jiatong He;Lei Zhang;Minpeng Xu;Zhongpeng Wang;Dong Ming","doi":"10.1109/THMS.2025.3619064","DOIUrl":"https://doi.org/10.1109/THMS.2025.3619064","url":null,"abstract":"Objective: Emergency anticipation (EA) refers to the brain’s rapid perceptual, cognitive, and motor preparation in response to imminent emergencies. Timely decoding of EA can facilitate proactive responses before full behavioral execution, which is critical in real-world scenarios such as avoiding hazards or mitigating accidents. However, the cortical activation underlying the EA process has not been fully explored. This study aims to analyze the neural activity of the EA process and explore the feasibility of detecting emergency motor intention in conjunction with brain-computer interface (BCI) technology. Methods: We designed a new emergency state induction paradigm in the virtual environment, including a target task (emergency anticipation, EA) and two baseline tasks (emergency anticipation execution, EAE, visual observation, VO). A total of 31 healthy subjects were recruited for the offline experiment. The cortical responses during the EA process were quantified by analyzing event-related potential, movement-related cortical potential, and event-related spectral perturbation. Discriminative canonical pattern matching, common spatial patterns, and shrinkage linear discriminant analysis were employed to perform binary classification. Six subjects participated in the pseudo-online asynchronous experiment to valid the feasibility of identifying emergency motor intention. Results: The results showed that the cascading process associated with EA existed in both the temporal and spectral domains. Particularly, temporal domain feature demonstrated superior classification performance, with averages of 90.13% (>80% chance level). The pseudo-online evaluation showed that the system response time with an average of 257.12 ms, which was 35 ms faster than the behavioral response. Significance: Our work demonstrated the cascading process of perceptual recognition, cognitive evaluation, and motor preparation during the EA processes and provided preliminary evidence supporting the feasibility of detecting emergency motor intentions. These findings lay a theoretical foundation for extending the application of BCI technology to rapid control scenarios.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"993-1005"},"PeriodicalIF":4.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652132","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}
Pub Date : 2025-11-04DOI: 10.1109/THMS.2025.3621275
Naveed Ahmad Khan;Prashant K. Jamwal;Fahad Hussain;Wayne Spratford;Shahid Hussain
Optimizing energy transfer during physical human–robot interactions is important for enhancing neurotherapeutic outcomes and ensuring patient safety. Energy transfer dynamics are particularly complex, involving a delicate balance between kinetic and potential energies as the robot assists or resists movement, adapting to the patient’s needs in real time. Traditional methods, which often rely on predefined robot control strategies, often struggle in dynamic environments where the interplay of forces and motions becomes unpredictable. Therefore, this work integrates the computational intelligence of quantum computing with transformer models to estimate the dynamics of energy transfer between human and gait rehabilitation robot, specifically designed based on the Stephenson III six-bar linkage mechanism. The principles of quantum computing, such as superposition and entanglement, combined with the attention mechanisms of transformer models, explore a much larger solution space. It provides accurate predictions of the complex, nonlinear interactions of energy flows between the robot and the human lower limb. The quantum transformer network was trained on the experimental data obtained from the interaction of seven male and one female healthy human subjects with the gait rehabilitation robot operated at low and high impedance control modes.
{"title":"Quantum Enhanced Transformer Network for Learning Transactive Energy During Physical Human-Robot Interaction","authors":"Naveed Ahmad Khan;Prashant K. Jamwal;Fahad Hussain;Wayne Spratford;Shahid Hussain","doi":"10.1109/THMS.2025.3621275","DOIUrl":"https://doi.org/10.1109/THMS.2025.3621275","url":null,"abstract":"Optimizing energy transfer during physical human–robot interactions is important for enhancing neurotherapeutic outcomes and ensuring patient safety. Energy transfer dynamics are particularly complex, involving a delicate balance between kinetic and potential energies as the robot assists or resists movement, adapting to the patient’s needs in real time. Traditional methods, which often rely on predefined robot control strategies, often struggle in dynamic environments where the interplay of forces and motions becomes unpredictable. Therefore, this work integrates the computational intelligence of quantum computing with transformer models to estimate the dynamics of energy transfer between human and gait rehabilitation robot, specifically designed based on the Stephenson III six-bar linkage mechanism. The principles of quantum computing, such as superposition and entanglement, combined with the attention mechanisms of transformer models, explore a much larger solution space. It provides accurate predictions of the complex, nonlinear interactions of energy flows between the robot and the human lower limb. The quantum transformer network was trained on the experimental data obtained from the interaction of seven male and one female healthy human subjects with the gait rehabilitation robot operated at low and high impedance control modes.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"930-939"},"PeriodicalIF":4.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652184","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}
Pub Date : 2025-11-04DOI: 10.1109/THMS.2025.3617576
Le Anh Dao;Marco Maccarini;Matteo Lavit Nicora;Matteo Meregalli Falerni;Marta Mondellini;Palaniappan Veerappan;Lorenzo Mantovani;Dario Piga;Simone Formentin;Matteo Malosio;Loris Roveda
Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over one or more others. Accordingly, algorithms for active preference learning have been developed to exploit this specific information in constructing a surrogate of the objective function. This surrogate is then used to define an acquisition function that suggests new decision vectors to search for the optimal solution iteratively. Based on this idea, our approach aims to extend active preference learning algorithms to leverage further information effectively, which can be obtained in reality, such as: a five-point Likert-type scale for the outcomes of the preference query (i.e., the preference can be described not only as “this is better than that” but also as “this is much better than that”), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, and, in practice, through tuning parameters for robot sealing and human–robot collaboration experiments, showing a promising improvement with respect to the state-of-the-art algorithm in the same context.
{"title":"Experience in Engineering Complex Systems: Active Preference Learning With Multiple Outcomes and Certainty Levels","authors":"Le Anh Dao;Marco Maccarini;Matteo Lavit Nicora;Matteo Meregalli Falerni;Marta Mondellini;Palaniappan Veerappan;Lorenzo Mantovani;Dario Piga;Simone Formentin;Matteo Malosio;Loris Roveda","doi":"10.1109/THMS.2025.3617576","DOIUrl":"https://doi.org/10.1109/THMS.2025.3617576","url":null,"abstract":"Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over one or more others. Accordingly, algorithms for active preference learning have been developed to exploit this specific information in constructing a surrogate of the objective function. This surrogate is then used to define an acquisition function that suggests new decision vectors to search for the optimal solution iteratively. Based on this idea, our approach aims to extend active preference learning algorithms to leverage further information effectively, which can be obtained in reality, such as: a five-point Likert-type scale for the outcomes of the preference query (i.e., the preference can be described not only as “this is better than that” but also as “this is much better than that”), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, and, in practice, through tuning parameters for robot sealing and human–robot collaboration experiments, showing a promising improvement with respect to the state-of-the-art algorithm in the same context.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"898-908"},"PeriodicalIF":4.4,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652171","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}
Pub Date : 2025-10-28DOI: 10.1109/THMS.2025.3620362
Han Zhang;Yuhan Liu;Liaoyang Zhan;Wanzhong Zhao
As heavy vehicles advance toward increased intelligence and modernization, the control of advanced driver assistance systems for ensuring driving safety faces significant challenges. To enhance the driving safety of heavy vehicles operated by drivers with varying driving styles, this article proposes a human–machine cooperative control (HMCC) strategy that combines steering and braking using deep deterministic policy gradient (DDPG) algorithm. First, a multiagent system is adopted as the framework for the driving safety assistance control system, wherein the active front steering (AFS) system and the differential braking control system (DBC) function as subsystems. These subsystems interact through control sequence information while managing yaw and roll stability. The optimal control performance of both the AFS and DBC is ensured using a distributed model predictive controller and Pareto optimality theory. Second, to analyze different drivers’ driving styles, safety characteristic parameters were collected from multiple drivers. By analyzing the effects of drivers on yaw and roll stability, drivers were classified into three types. Furthermore, an HMCC strategy based on DDPG is designed. Phase plane constraints that consider yaw and roll stability are incorporated into the design of the DDPG reward function, training the agents to allocate cooperative control weights between the driver and the AFS and DBC controllers. Finally, the proposed control strategy’s effectiveness is validated through the electro-hydraulic compound steering and braking hardware-in-the-loop test system, demonstrating its ability to improve driving safety for different driver characteristics.
{"title":"A Human–Machine Cooperative Control Strategy Based on Deep Reinforcement Learning to Enhance Heavy Vehicle Driving Safety","authors":"Han Zhang;Yuhan Liu;Liaoyang Zhan;Wanzhong Zhao","doi":"10.1109/THMS.2025.3620362","DOIUrl":"https://doi.org/10.1109/THMS.2025.3620362","url":null,"abstract":"As heavy vehicles advance toward increased intelligence and modernization, the control of advanced driver assistance systems for ensuring driving safety faces significant challenges. To enhance the driving safety of heavy vehicles operated by drivers with varying driving styles, this article proposes a human–machine cooperative control (HMCC) strategy that combines steering and braking using deep deterministic policy gradient (DDPG) algorithm. First, a multiagent system is adopted as the framework for the driving safety assistance control system, wherein the active front steering (AFS) system and the differential braking control system (DBC) function as subsystems. These subsystems interact through control sequence information while managing yaw and roll stability. The optimal control performance of both the AFS and DBC is ensured using a distributed model predictive controller and Pareto optimality theory. Second, to analyze different drivers’ driving styles, safety characteristic parameters were collected from multiple drivers. By analyzing the effects of drivers on yaw and roll stability, drivers were classified into three types. Furthermore, an HMCC strategy based on DDPG is designed. Phase plane constraints that consider yaw and roll stability are incorporated into the design of the DDPG reward function, training the agents to allocate cooperative control weights between the driver and the AFS and DBC controllers. Finally, the proposed control strategy’s effectiveness is validated through the electro-hydraulic compound steering and braking hardware-in-the-loop test system, demonstrating its ability to improve driving safety for different driver characteristics.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"1006-1015"},"PeriodicalIF":4.4,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652161","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}
Pub Date : 2025-10-23DOI: 10.1109/THMS.2025.3616947
Zhongpan Zhu;Shuaijie Zhao;Mobing Cai;Cheng Wang;Aimin Du
Intelligent driving aims to handle dynamic driving tasks in complex environments, while driver behavior onboard is less focused. In contrast, an intelligent cockpit mainly focuses on interacting with a driver, with limited connection to the driving scenarios. Since the driver onboard could affect the driving strategy significantly and thus have nonnegligible safety implications on an autonomous vehicle, a cockpit-driving integration (CDI) is generally essential to take the driver’s behavior and intention into account when shaping the driving strategy. However, no comprehensive review of current existing CDI technologies is conducted despite the significant role of CDI in safe driving. Therefore, we are motivated to summarize the state-of-the-art of CDI methods and investigate the development trends of CDI. To this end, we identify thoroughly current applications of CDI for the perception and decision-making of autonomous vehicles and highlight critical issues that urgently need to be addressed. Additionally, we propose a lifelong learning framework based on evolvable neural networks as solutions for future CDI. Finally, challenges and future work are discussed. The work provides useful insights for developers regarding designing safe and human-centric autonomous vehicles.
{"title":"Automotive Cockpit-Driving Integration for Human-Centric Autonomous Driving: A Survey","authors":"Zhongpan Zhu;Shuaijie Zhao;Mobing Cai;Cheng Wang;Aimin Du","doi":"10.1109/THMS.2025.3616947","DOIUrl":"https://doi.org/10.1109/THMS.2025.3616947","url":null,"abstract":"Intelligent driving aims to handle dynamic driving tasks in complex environments, while driver behavior onboard is less focused. In contrast, an intelligent cockpit mainly focuses on interacting with a driver, with limited connection to the driving scenarios. Since the driver onboard could affect the driving strategy significantly and thus have nonnegligible safety implications on an autonomous vehicle, a cockpit-driving integration (CDI) is generally essential to take the driver’s behavior and intention into account when shaping the driving strategy. However, no comprehensive review of current existing CDI technologies is conducted despite the significant role of CDI in safe driving. Therefore, we are motivated to summarize the state-of-the-art of CDI methods and investigate the development trends of CDI. To this end, we identify thoroughly current applications of CDI for the perception and decision-making of autonomous vehicles and highlight critical issues that urgently need to be addressed. Additionally, we propose a lifelong learning framework based on evolvable neural networks as solutions for future CDI. Finally, challenges and future work are discussed. The work provides useful insights for developers regarding designing safe and human-centric autonomous vehicles.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 6","pages":"1016-1032"},"PeriodicalIF":4.4,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652146","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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