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

IEEE Systems, Man, and Cybernetics Society Information 电气和电子工程师学会系统、人和控制论学会信息

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-03-25 DOI: 10.1109/THMS.2025.3553006

引用次数: 0

Brain-Supervised Conditional Generative Modeling 脑监督条件生成建模

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-03-21 DOI: 10.1109/THMS.2025.3537339

Jun Ma;Tuukka Ruotsalo

{"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}

引用次数: 0

Erratum to “Effects of Target Trajectory Bandwidth on Manual Control Behavior in Pursuit and Preview Tracking” “目标轨迹带宽对跟踪和预瞄跟踪中手动控制行为的影响”的勘误

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-03-07 DOI: 10.1109/THMS.2025.3561858

Kasper van der El;Daan M. Pool;Marinus M. van Paassen;Max Mulder

引用次数: 0

Adaptive Virtual Fixture Based on Learning Trajectory Distribution for Comanipulation Tasks 基于学习轨迹分布的协同操作任务自适应虚拟夹具

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-03-04 DOI: 10.1109/THMS.2025.3540123

Shaqi Luo;Min Cheng;Ruqi Ding

{"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}

引用次数: 0

Visual Interfaces to Mitigate Eye Problems in a Virtual Environment via Triggering Eye Blinking and Movement 通过触发眼睛眨眼和运动来减轻虚拟环境中眼睛问题的视觉界面

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-03-04 DOI: 10.1109/THMS.2025.3542452

Jongwook Jeong;Myeongseok Kwak;HyeongYeop Kang

引用次数: 0

SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning 基于深度面部特征学习的注视估计学习框架

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-03-02 DOI: 10.1109/THMS.2025.3553404

Samuel Adebayo;Joost C. Dessing;Seán McLoone

引用次数: 0

Time Series Signal Analysis With Information Granulation Based on Permutation Entropy: An Application to Electroencephalography Signals 基于排列熵的信息粒化时间序列信号分析：在脑电图信号中的应用

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-25 DOI: 10.1109/THMS.2025.3538098

Youpeng Yang;Sanghyuk Lee;Haolan Zhang;Witold Pedrycz

{"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}

引用次数: 0

A Single-Camera Method for Estimating Lift Asymmetry Angles Using Deep Learning Computer Vision Algorithms 基于深度学习计算机视觉算法的单摄像机升力不对称角估计方法

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-25 DOI: 10.1109/THMS.2025.3539187

Zhengyang Lou;Zitong Zhan;Huan Xu;Yin Li;Yu Hen Hu;Ming-Lun Lu;Dwight M. Werren;Robert G. Radwin

{"title":"A Single-Camera Method for Estimating Lift Asymmetry Angles Using Deep Learning Computer Vision Algorithms","authors":"Zhengyang Lou;Zitong Zhan;Huan Xu;Yin Li;Yu Hen Hu;Ming-Lun Lu;Dwight M. Werren;Robert G. Radwin","doi":"10.1109/THMS.2025.3539187","DOIUrl":"https://doi.org/10.1109/THMS.2025.3539187","url":null,"abstract":"A computer vision (CV) method to automatically measure the revised NIOSH lifting equation asymmetry angle (<italic>A</i>) from a single camera is described and tested. A laboratory study involving ten participants performing various lifts was used to estimate <italic>A</i> in comparison to ground truth joint coordinates obtained using 3-D motion capture (MoCap). To address challenges, such as obstructed views and limitations in camera placement in real-world scenarios, the CV method utilized video-derived coordinates from a selected set of landmarks. A 2-D pose estimator (HR-Net) detected landmark coordinates in each video frame, and a 3-D algorithm (VideoPose3D) estimated the depth of each 2-D landmark by analyzing its trajectories. The mean absolute precision error for the CV method, compared to MoCap measurements using the same subset of landmarks for estimating <italic>A</i>, was 6.25° (SD = 10.19°, N = 360). The mean absolute accuracy error of the CV method, compared against conventional MoCap landmark markers was 9.45° (SD = 14.01°, <italic>N</i> = 360).","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"309-314"},"PeriodicalIF":3.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698218","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

A Miner Mental State Evaluation Scheme With Decision Level Fusion Based on Multidomain EEG Information 基于多域脑电信息的决策级融合矿工心理状态评价方案

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-24 DOI: 10.1109/THMS.2025.3538162

Hongguang Pan;Shiyu Tong;Haoqian Song;Xin Chu

{"title":"A Miner Mental State Evaluation Scheme With Decision Level Fusion Based on Multidomain EEG Information","authors":"Hongguang Pan;Shiyu Tong;Haoqian Song;Xin Chu","doi":"10.1109/THMS.2025.3538162","DOIUrl":"https://doi.org/10.1109/THMS.2025.3538162","url":null,"abstract":"It has been proven that electroencephalography (EEG) is an effective method for evaluating an individual's mental state. However, when it comes to the evaluation of miners' mental state, there are still some issues with missing EEG dataset and unsatisfactory evaluation accuracy. Therefore, this article proposes a miner mental state evaluation scheme with decision-level fusion based on multidomain EEG information. First, in the comprehensive lab for coal-related programs of Xi'an University of Science and Technology, the coal mine environment is simulated, and a realistic EEG dataset is constructed. Second, the multidomain features are extracted to represent abundant information in time, frequency, time-frequency, and space domain. These features with low dimension are classified adopting support vector machine (SVM), k-nearest neighbor (kNN), and back propagation (BP) network to obtain the optimal evaluation submodel (four domains corresponding to four submodels). Finally, based on the state probabilities provided by the optimal evaluation submodel, we adopt stack fusion and an improved Yager rule to fuse four submodels in order to find the most suitable fusion algorithm. The experimental results demonstrate that the average accuracy can reach 93.19% on the self-built dataset when utilizing the improved Yager rule with weight, and it realizes a better evaluation accuracy.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"289-299"},"PeriodicalIF":3.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698353","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

WiOpen: A Robust Wi-Fi-Based Open-Set Gesture Recognition Framework WiOpen：基于 Wi-Fi 的鲁棒开放集手势识别框架

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2025-02-21 DOI: 10.1109/THMS.2025.3532910

Xiang Zhang;Jinyang Huang;Huan Yan;Yuanhao Feng;Peng Zhao;Guohang Zhuang;Zhi Liu;Bin Liu

{"title":"WiOpen: A Robust Wi-Fi-Based Open-Set Gesture Recognition Framework","authors":"Xiang Zhang;Jinyang Huang;Huan Yan;Yuanhao Feng;Peng Zhao;Guohang Zhuang;Zhi Liu;Bin Liu","doi":"10.1109/THMS.2025.3532910","DOIUrl":"https://doi.org/10.1109/THMS.2025.3532910","url":null,"abstract":"Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known class during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based open-set gesture recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a twofold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the channel state information (CSI) ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples' neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen's effectiveness.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"55 2","pages":"234-245"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698290","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