IEEE Transactions on Human-Machine Systems最新文献_第8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 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-23 DOI: 10.1109/THMS.2024.3401263

引用次数: 0

Share Your Preprint Research with the World! 与世界分享您的预印本研究成果

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-23 DOI: 10.1109/THMS.2024.3401261

引用次数: 0

Thresholds for Perceiving Changes in Friction When Combined With Linear System Dynamics 结合线性系统动力学感知摩擦力变化的阈值

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-22 DOI: 10.1109/THMS.2024.3368358

Robbin Veldhuis;Max Mulder;M. M. van Paassen

{"title":"Thresholds for Perceiving Changes in Friction When Combined With Linear System Dynamics","authors":"Robbin Veldhuis;Max Mulder;M. M. van Paassen","doi":"10.1109/THMS.2024.3368358","DOIUrl":"10.1109/THMS.2024.3368358","url":null,"abstract":"Understanding human perception of haptic feedback is critical when designing and regulating these interfaces. In recent years, experiments have been conducted to determine the just-noticeable difference (JND) in mass–spring–damper dynamics, using a hydraulic admittance display in the form of a side-stick. These experiments have resulted in a model of JNDs when interacting with linear second-order dynamics. In real-world applications, however, control force dynamics also commonly include nonlinearities, such as friction. This research extends the current understanding of JNDs in linear systems by including the nonlinear case, where friction is also present. Experiments were conducted to determine JNDs in friction when combined with second-order system dynamics. Results indicate that friction JND can be independent of linear system dynamics as long as its value compared to the linear system's impedance is sufficiently large. As a consequence, friction JND follows Weber's law, also when it is combined with mass–spring–damper dynamics, unless the level of friction approaches the detection threshold, which in turn can be influenced by the linear system dynamics. Based on the findings presented, it is possible to conduct targeted experiments to confirm and add to these initial results.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"260-270"},"PeriodicalIF":3.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140204010","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

Singularity-Free Finite-Time Adaptive Optimal Control for Constrained Coordinated Uncertain Robots 受限协调不确定机器人的无奇点有限时间自适应优化控制

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-21 DOI: 10.1109/THMS.2024.3397351

Shenquan Wang;Wen Yang;Yulian Jiang;Mohammed Chadli;Yanzheng Zhu

{"title":"Singularity-Free Finite-Time Adaptive Optimal Control for Constrained Coordinated Uncertain Robots","authors":"Shenquan Wang;Wen Yang;Yulian Jiang;Mohammed Chadli;Yanzheng Zhu","doi":"10.1109/THMS.2024.3397351","DOIUrl":"10.1109/THMS.2024.3397351","url":null,"abstract":"This article investigates the singularity-free finite-time adaptive optimal control problem for coordinated robots, where the position and velocity are constrained within the asymmetric yet time-varying ranges. Different from the existing results concerning constrained control, the imposed feasibility conditions are relaxed by skillfully integrating a nonlinear state-dependent function into the backstepping design procedure. Therein, the typical feature of the designed finite-time controller lies in the application of the modified smooth switching function, rendering the designed controller powerful enough to eliminate singularity problem. Notably, with the aid of the constructed optimal cost function and neural network-based critic architecture, the optimal control law is established under the backstepping design framework. It is theoretically verified that the designed controller is of satisfied optimization and finite-time tracking ability, and desired constrained objective in the meanwhile. The validity of the resulting control algorithm is eventually substantiated via two robotic manipulators.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 4","pages":"385-394"},"PeriodicalIF":3.5,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141150172","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 Review of Robot-Assisted Hand Spasticity Assessment 机器人辅助手部痉挛评估综述

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-21 DOI: 10.1109/THMS.2024.3393014

Hao Yu;Alyson Nelson;Mustafa Suphi Erden

引用次数: 0

RTSformer: A Robust Toroidal Transformer With Spatiotemporal Features for Visual Tracking RTSformer：用于视觉跟踪的具有时空特征的稳健环形变换器

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-18 DOI: 10.1109/THMS.2024.3370582

Fengwei Gu;Jun Lu;Chengtao Cai;Qidan Zhu;Zhaojie Ju

{"title":"RTSformer: A Robust Toroidal Transformer With Spatiotemporal Features for Visual Tracking","authors":"Fengwei Gu;Jun Lu;Chengtao Cai;Qidan Zhu;Zhaojie Ju","doi":"10.1109/THMS.2024.3370582","DOIUrl":"10.1109/THMS.2024.3370582","url":null,"abstract":"In complex environments, trackers are extremely susceptible to some interference factors, such as fast motions, occlusion, and scale changes, which result in poor tracking performance. The reason is that trackers cannot sufficiently utilize the target feature information in these cases. Therefore, it has become a particularly critical issue in the field of visual tracking to utilize the target feature information efficiently. In this article, a composite transformer involving spatiotemporal features is proposed to achieve robust visual tracking. Our method develops a novel toroidal transformer to fully integrate features while designing a template refresh mechanism to provide temporal features efficiently. Combined with the hybrid attention mechanism, the composite of temporal and spatial feature information is more conducive to mining feature associations between the template and search region than a single feature. To further correlate the global information, the proposed method adopts a closed-loop structure of the toroidal transformer formed by the cross-feature fusion head to integrate features. Moreover, the designed score head is used as a basis for judging whether the template is refreshed. Ultimately, the proposed tracker can achieve the tracking task only through a simple network framework, which especially simplifies the existing tracking architectures. Experiments show that the proposed tracker outperforms extensive state-of-the-art methods on seven benchmarks at a real-time speed of 56.5 fps.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 2","pages":"214-225"},"PeriodicalIF":3.6,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166154","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

Leveraging High-Density EMG to Investigate Bipolar Electrode Placement for Gait Prediction Models 利用高密度肌电图研究步态预测模型的双极电极位置

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-18 DOI: 10.1109/THMS.2024.3371099

Balint K. Hodossy;Annika S. Guez;Shibo Jing;Weiguang Huo;Ravi Vaidyanathan;Dario Farina

{"title":"Leveraging High-Density EMG to Investigate Bipolar Electrode Placement for Gait Prediction Models","authors":"Balint K. Hodossy;Annika S. Guez;Shibo Jing;Weiguang Huo;Ravi Vaidyanathan;Dario Farina","doi":"10.1109/THMS.2024.3371099","DOIUrl":"10.1109/THMS.2024.3371099","url":null,"abstract":"To control wearable robotic systems, it is critical to obtain a prediction of the user's motion intent with high accuracy. Surface electromyography (sEMG) recordings have often been used as inputs for these devices, however bipolar sEMG electrodes are highly sensitive to their location. Positional shifts of electrodes after training gait prediction models can therefore result in severe performance degradation. This study uses high-density sEMG (HD-sEMG) electrodes to simulate various bipolar electrode signals from four leg muscles during steady-state walking. The bipolar signals were ranked based on the consistency of the corresponding sEMG envelope's activity and timing across gait cycles. The locations were then compared by evaluating the performance of an offline temporal convolutional network (TCN) that mapped sEMG signals to knee angles. The results showed that electrode locations with consistent sEMG envelopes resulted in greater prediction accuracy compared to hand-aligned placements (\u0000<italic>p</i>\u0000 < 0.01). However, performance gains through this process were limited, and did not resolve the position shift issue. Instead of training a model for a single location, we showed that randomly sampling bipolar combinations across the HD-sEMG grid during training mitigated this effect. Models trained with this method generalized over all positions, and achieved 70% less prediction error than location specific models over the entire area of the grid. Therefore, the use of HD-sEMG grids to build training datasets could enable the development of models robust to spatial variations, and reduce the impact of muscle-specific electrode placement on accuracy.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 2","pages":"192-201"},"PeriodicalIF":3.6,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10474208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166273","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

Automated Classification of Cognitive Visual Objects Using Multivariate Swarm Sparse Decomposition From Multichannel EEG-MEG Signals 利用多通道脑电-MEG 信号的多变量蜂群稀疏分解对认知视觉对象进行自动分类

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-17 DOI: 10.1109/THMS.2024.3395153

Shailesh Vitthalrao Bhalerao;Ram Bilas Pachori

{"title":"Automated Classification of Cognitive Visual Objects Using Multivariate Swarm Sparse Decomposition From Multichannel EEG-MEG Signals","authors":"Shailesh Vitthalrao Bhalerao;Ram Bilas Pachori","doi":"10.1109/THMS.2024.3395153","DOIUrl":"10.1109/THMS.2024.3395153","url":null,"abstract":"In visual object decoding, magnetoencephalogram (MEG) and electroencephalogram (EEG) activation patterns demonstrate the utmost discriminative cognitive analysis due to their multivariate oscillatory nature. However, high noise in the recorded EEG-MEG signals and subject-specific variability make it extremely difficult to classify subject's cognitive responses to different visual stimuli. The proposed method is a multivariate extension of the swarm sparse decomposition method (MSSDM) for multivariate pattern analysis of EEG-MEG-based visual activation signals. In comparison, it is an advanced technique for decomposing nonstationary multicomponent signals into a finite number of channel-aligned oscillatory components that significantly enhance visual activation-related sub-bands. The MSSDM method adopts multivariate swarm filtering and sparse spectrum to automatically deliver optimal frequency bands in channel-specific sparse spectrums, resulting in improved filter banks. By combining the advantages of the multivariate SSDM and Riemann's correlation-assisted fusion feature (RCFF), the MSSDM-RCFF algorithm is investigated to improve the visual object recognition ability of EEG-MEG signals. We have also proposed time–frequency representation based on MSSDM to analyze discriminative cognitive patterns of different visual object classes from multichannel EEG-MEG signals. A proposed MSSDM is evaluated on multivariate synthetic signals and multivariate EEG-MEG signals using five classifiers. The proposed fusion feature and linear discriminant analysis classifier-based framework outperformed all existing state-of-the-art methods used for visual object detection and achieved the highest accuracy of 86.42% using tenfold cross-validation on EEG-MEG multichannel signals.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 4","pages":"455-464"},"PeriodicalIF":3.5,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063423","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

SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force From Highly Sparse Ultrasound Images SonoMyoNet：从高度稀疏的超声波图像预测等长力的卷积神经网络

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-13 DOI: 10.1109/THMS.2024.3389690

Anne Tryphosa Kamatham;Meena Alzamani;Allison Dockum;Siddhartha Sikdar;Biswarup Mukherjee

{"title":"SonoMyoNet: A Convolutional Neural Network for Predicting Isometric Force From Highly Sparse Ultrasound Images","authors":"Anne Tryphosa Kamatham;Meena Alzamani;Allison Dockum;Siddhartha Sikdar;Biswarup Mukherjee","doi":"10.1109/THMS.2024.3389690","DOIUrl":"10.1109/THMS.2024.3389690","url":null,"abstract":"Ultrasound imaging or sonomyography has been found to be a robust modality for measuring muscle activity due to its ability to image deep-seated muscles directly while providing superior spatiotemporal specificity compared with surface electromyography-based techniques. Quantifying the morphological changes during muscle activity involves computationally expensive approaches for tracking muscle anatomical structures or extracting features from brightness-mode (B-mode) images and amplitude-mode signals. This article uses an offline regression convolutional neural network called SonoMyoNet to estimate continuous isometric force from sparse ultrasound scanlines. SonoMyoNet learns features from a few equispaced scanlines selected from B-mode images and utilizes the learned features to estimate continuous isometric force accurately. The performance of SonoMyoNet was evaluated by varying the number of scanlines to simulate the placement of multiple single-element ultrasound transducers in a wearable system. Results showed that SonoMyoNet could accurately predict isometric force with just four scanlines and is immune to speckle noise and shifts in the scanline location. Thus, the proposed network reduces the computational load involved in feature tracking algorithms and estimates muscle force from the global features of sparse ultrasound images.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"317-324"},"PeriodicalIF":3.6,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939947","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

Hand Segmentation With Dense Dilated U-Net and Structurally Incoherent Nonnegative Matrix Factorization-Based Gesture Recognition 基于手势识别的密集稀疏 U-Net 和结构不连贯非负矩阵因数分解的手部分割

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-03-07 DOI: 10.1109/THMS.2024.3390415

Kankana Roy;Rajiv R. Sahay

{"title":"Hand Segmentation With Dense Dilated U-Net and Structurally Incoherent Nonnegative Matrix Factorization-Based Gesture Recognition","authors":"Kankana Roy;Rajiv R. Sahay","doi":"10.1109/THMS.2024.3390415","DOIUrl":"10.1109/THMS.2024.3390415","url":null,"abstract":"Robust segmentation of hands in a cluttered environment for hand gesture recognition has remained a challenge in computer vision. In this work, a two-stage gesture recognition framework is proposed. In the first stage, we segment hands using the proposed deep learning algorithm, and in the second stage, we use these segmented hands to classify gestures using a novel structurally incoherent nonnegative matrix factorization approach. We propose a new deep learning framework for hand segmentation called densely dilated U-Net. We exploit recently proposed dense blocks and dilated convolution layers in our work. To cope with the scarcity of labeled datasets we extend our densely dilated U-Net for semisupervised hand segmentation using hand bounding boxes as cues. We provide quantitative and qualitative evaluation of proposed hand segmentation model on several public hand segmentation datasets including EgoHands, GTEA, EYTH, EDSH, and HOF. Semisupervised segmentation results are also obtained on two hand detection datasets including VIVA and CVRR. As an extension of our work, we show semisupervised segmentation and gesture recognition results using segmented hands on NUS-II cluttered hand gesture dataset. To validate the efficiency of our semisupervised algorithm we evaluate it on OUHands dataset with real ground truth labels. For gesture classification, we propose a novel structurally incoherent nonnegative matrix factorization algorithm. We propose to use CNN features extracted from segmented images for nonnegative matrix factorization. Experimental results on NUS-II and OUHands datasets demonstrate that our two-stage approach for gesture recognition yields superior results.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"238-249"},"PeriodicalIF":3.6,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939745","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