Pub Date : 2025-03-25DOI: 10.1109/THMS.2025.3553008
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2025.3553008","DOIUrl":"https://doi.org/10.1109/THMS.2025.3553008","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"C4-C4"},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698174","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 : 2025-03-25DOI: 10.1109/THMS.2025.3553004
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2025.3553004","DOIUrl":"https://doi.org/10.1109/THMS.2025.3553004","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"C2-C2"},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698169","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 : 2025-03-25DOI: 10.1109/THMS.2025.3546139
{"title":"Call for Papers: IEEE Transactions on Human-Machine Systems","authors":"","doi":"10.1109/THMS.2025.3546139","DOIUrl":"https://doi.org/10.1109/THMS.2025.3546139","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"322-322"},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698354","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 : 2025-03-25DOI: 10.1109/THMS.2025.3553006
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2025.3553006","DOIUrl":"https://doi.org/10.1109/THMS.2025.3553006","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"C3-C3"},"PeriodicalIF":3.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698250","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 : 2025-03-21DOI: 10.1109/THMS.2025.3537339
Jun Ma;Tuukka Ruotsalo
Present machine learning approaches to steer generative models rely on the availability of manual human input. We propose an alternative approach to supervising generative machine learning models by directly detecting task-relevant information from brain responses. That is, requiring humans only to perceive stimulus and react to it naturally. Brain responses of participants (N=30) were recorded via electroencephalography (EEG) while they perceived artificially generated images of faces and were instructed to look for a particular semantic feature, such as “smile” or “young”. A supervised adversarial autoencoder was trained to disentangle semantic image features by using EEG data as a supervision signal. The model was subsequently conditioned to generate images matching users' intentions without additional human input. The approach was evaluated in a validation study comparing brain-conditioned models to manually conditioned and randomly conditioned alternatives. Human assessors scored the saliency of images generated from different models according to the target visual features (e.g., which face image is more “smiling” or more “young”). The results show that brain-supervised models perform comparably to models trained with manually curated labels, without requiring any manual input from humans.
{"title":"Brain-Supervised Conditional Generative Modeling","authors":"Jun Ma;Tuukka Ruotsalo","doi":"10.1109/THMS.2025.3537339","DOIUrl":"https://doi.org/10.1109/THMS.2025.3537339","url":null,"abstract":"Present machine learning approaches to steer generative models rely on the availability of manual human input. We propose an alternative approach to supervising generative machine learning models by directly detecting task-relevant information from brain responses. That is, requiring humans only to perceive stimulus and react to it naturally. Brain responses of participants (N=30) were recorded via electroencephalography (EEG) while they perceived artificially generated images of faces and were instructed to look for a particular semantic feature, such as “smile” or “young”. A supervised adversarial autoencoder was trained to disentangle semantic image features by using EEG data as a supervision signal. The model was subsequently conditioned to generate images matching users' intentions without additional human input. The approach was evaluated in a validation study comparing brain-conditioned models to manually conditioned and randomly conditioned alternatives. Human assessors scored the saliency of images generated from different models according to the target visual features (e.g., which face image is more “smiling” or more “young”). The results show that brain-supervised models perform comparably to models trained with manually curated labels, without requiring any manual input from humans.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 3","pages":"383-393"},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492395","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 : 2025-03-07DOI: 10.1109/THMS.2025.3561858
Kasper van der El;Daan M. Pool;Marinus M. van Paassen;Max Mulder
This erratum applies to the following published paper [1].
本勘误适用于以下已发表的论文[1]。
{"title":"Erratum to “Effects of Target Trajectory Bandwidth on Manual Control Behavior in Pursuit and Preview Tracking”","authors":"Kasper van der El;Daan M. Pool;Marinus M. van Paassen;Max Mulder","doi":"10.1109/THMS.2025.3561858","DOIUrl":"https://doi.org/10.1109/THMS.2025.3561858","url":null,"abstract":"This erratum applies to the following published paper [1].","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 3","pages":"474-475"},"PeriodicalIF":3.5,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492390","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 : 2025-03-04DOI: 10.1109/THMS.2025.3540123
Shaqi Luo;Min Cheng;Ruqi Ding
Virtual fixture is a powerful tool to improve safety and efficiency for co-manipulation tasks. However, traditional virtual fixtures with constant stiffness are inadequate for scenarios where robots need to leave the constraints to perform tasks. To address this, we propose an adaptive virtual fixture based on the motion refinement tube, which dynamically adjusts the guiding force according to the distribution of trajectories. To prevent tube deformation in the Cartesian space due to the neglect of off-diagonal elements of covariance matrices, the refinement tube radii and nonlinear stiffness terms are computed in local coordinate systems based on the decomposed covariance matrix. An energy-tank-based passivity controller is designed to ensure system stability when employing the virtual fixture with state-dependent stiffness terms. In the validation tests with 18 participants, the proposed method showed improvements in task efficiency (18.69% increase) and collision avoidance (97.87% reduction) for a typical pick-and-place task with scattered materials. It also provided better subjective experiences of the users than traditional virtual fixtures. Meanwhile, compared with the method that neglects off-diagonal elements of the covariance matrix, the proposed method exhibited a 4.28% efficiency improvement and a 40.42% decrease in collision occurrences.
{"title":"Adaptive Virtual Fixture Based on Learning Trajectory Distribution for Comanipulation Tasks","authors":"Shaqi Luo;Min Cheng;Ruqi Ding","doi":"10.1109/THMS.2025.3540123","DOIUrl":"https://doi.org/10.1109/THMS.2025.3540123","url":null,"abstract":"Virtual fixture is a powerful tool to improve safety and efficiency for co-manipulation tasks. However, traditional virtual fixtures with constant stiffness are inadequate for scenarios where robots need to leave the constraints to perform tasks. To address this, we propose an adaptive virtual fixture based on the motion refinement tube, which dynamically adjusts the guiding force according to the distribution of trajectories. To prevent tube deformation in the Cartesian space due to the neglect of off-diagonal elements of covariance matrices, the refinement tube radii and nonlinear stiffness terms are computed in local coordinate systems based on the decomposed covariance matrix. An energy-tank-based passivity controller is designed to ensure system stability when employing the virtual fixture with state-dependent stiffness terms. In the validation tests with 18 participants, the proposed method showed improvements in task efficiency (18.69% increase) and collision avoidance (97.87% reduction) for a typical pick-and-place task with scattered materials. It also provided better subjective experiences of the users than traditional virtual fixtures. Meanwhile, compared with the method that neglects off-diagonal elements of the covariance matrix, the proposed method exhibited a 4.28% efficiency improvement and a 40.42% decrease in collision occurrences.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"165-175"},"PeriodicalIF":3.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698261","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 : 2025-03-04DOI: 10.1109/THMS.2025.3542452
Jongwook Jeong;Myeongseok Kwak;HyeongYeop Kang
With the increase of virtual reality (VR) applications in daily life, protecting the comfort and health of VR users has become increasingly important. The immersive nature of VR often results in decreased eye blinking and movement, putting users at risk of developing conditions such as dry eye syndrome and eye strain. In this article, we propose visual interfaces to induce temporary eye blinks or movements by drawing users' attention temporarily in order to mitigate the negative effects of VR on eye health. Our proposed interfaces can induce eye blinking and movement, which are known to mitigate eye problems in VR. The experimental results confirmed that our interfaces increase the frequency of eye blinking and movement in VR users.
{"title":"Visual Interfaces to Mitigate Eye Problems in a Virtual Environment via Triggering Eye Blinking and Movement","authors":"Jongwook Jeong;Myeongseok Kwak;HyeongYeop Kang","doi":"10.1109/THMS.2025.3542452","DOIUrl":"https://doi.org/10.1109/THMS.2025.3542452","url":null,"abstract":"With the increase of virtual reality (VR) applications in daily life, protecting the comfort and health of VR users has become increasingly important. The immersive nature of VR often results in decreased eye blinking and movement, putting users at risk of developing conditions such as dry eye syndrome and eye strain. In this article, we propose visual interfaces to induce temporary eye blinks or movements by drawing users' attention temporarily in order to mitigate the negative effects of VR on eye health. Our proposed interfaces can induce eye blinking and movement, which are known to mitigate eye problems in VR. The experimental results confirmed that our interfaces increase the frequency of eye blinking and movement in VR users.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"278-288"},"PeriodicalIF":3.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698355","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 : 2025-03-02DOI: 10.1109/THMS.2025.3553404
Samuel Adebayo;Joost C. Dessing;Seán McLoone
In this research, we present self-learn your key latent (SLYKLatent), a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets due to aleatoric uncertainties, covariant shifts, and test domain generalization. SLYKLatent utilizes self-supervised learning for initial training with facial expression datasets, followed by refinement with a patch-based tribranch network and an inverse explained variance weighted training loss function. Our evaluation on benchmark datasets achieves a 10.98% improvement on Gaze360, supersedes the top result with 3.83% improvement on MPIIFaceGaze, and leads on a subset of ETH-XGaze by 11.59%, surpassing existing methods by significant margins. In addition, adaptability tests on RAF-DB and Affectnet show 86.4% and 60.9% accuracies, respectively. Ablation studies confirm the effectiveness of SLYKLatent's novel components. This approach has strong potential in human–robot interaction.
{"title":"SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning","authors":"Samuel Adebayo;Joost C. Dessing;Seán McLoone","doi":"10.1109/THMS.2025.3553404","DOIUrl":"https://doi.org/10.1109/THMS.2025.3553404","url":null,"abstract":"In this research, we present self-learn your key latent (SLYKLatent), a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets due to aleatoric uncertainties, covariant shifts, and test domain generalization. SLYKLatent utilizes self-supervised learning for initial training with facial expression datasets, followed by refinement with a patch-based tribranch network and an inverse explained variance weighted training loss function. Our evaluation on benchmark datasets achieves a 10.98% improvement on Gaze360, supersedes the top result with 3.83% improvement on MPIIFaceGaze, and leads on a subset of ETH-XGaze by 11.59%, surpassing existing methods by significant margins. In addition, adaptability tests on RAF-DB and Affectnet show 86.4% and 60.9% accuracies, respectively. Ablation studies confirm the effectiveness of SLYKLatent's novel components. This approach has strong potential in human–robot interaction.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 3","pages":"333-346"},"PeriodicalIF":3.5,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492265","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}
In this article, we reported a novel granulation method composed of complexity information based on permutation entropy (PeEn). This method aims to recognize the electroencephalography (EEG) patterns using this proposed granulation method. First, we define the complexity information for granular computing by a technique with fast calculation, i.e., PeEn. Then, the information granule can be constructed based on the time domain information, which completes complexity information. Together with the support vector machine algorithm, the proposed granulation method outperformed the existing classification methods in accuracy. It is utilized by classifying three motor imaginary EEG signals. Two of them are binary-class datasets, i.e., one dataset includes two-hand actions, and another includes hand and foot actions. The third dataset is multiclass, including two hands and two feet actions. In addition, the proposed granulation method overcomes the difficulties in cross-individual cases when classifying the EEG signals with a higher accuracy than the existing methods. Meanwhile, this classification procedure makes it interpretable and has a high performance.
{"title":"Time Series Signal Analysis With Information Granulation Based on Permutation Entropy: An Application to Electroencephalography Signals","authors":"Youpeng Yang;Sanghyuk Lee;Haolan Zhang;Witold Pedrycz","doi":"10.1109/THMS.2025.3538098","DOIUrl":"https://doi.org/10.1109/THMS.2025.3538098","url":null,"abstract":"In this article, we reported a novel granulation method composed of complexity information based on permutation entropy (PeEn). This method aims to recognize the electroencephalography (EEG) patterns using this proposed granulation method. First, we define the complexity information for granular computing by a technique with fast calculation, i.e., PeEn. Then, the information granule can be constructed based on the time domain information, which completes complexity information. Together with the support vector machine algorithm, the proposed granulation method outperformed the existing classification methods in accuracy. It is utilized by classifying three motor imaginary EEG signals. Two of them are binary-class datasets, i.e., one dataset includes two-hand actions, and another includes hand and foot actions. The third dataset is multiclass, including two hands and two feet actions. In addition, the proposed granulation method overcomes the difficulties in cross-individual cases when classifying the EEG signals with a higher accuracy than the existing methods. Meanwhile, this classification procedure makes it interpretable and has a high performance.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"300-308"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698215","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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