IEEE Transactions on Human-Machine Systems最新文献_第10页

A Physics-Based Virtual Reality Haptic System Design and Evaluation by Simulating Human-Robot Collaboration 通过模拟人机协作设计和评估基于物理的虚拟现实触觉系统

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-06-06 DOI: 10.1109/THMS.2024.3407109

Syed T. Mubarrat;Antonio Fernandes;Suman K. Chowdhury

{"title":"A Physics-Based Virtual Reality Haptic System Design and Evaluation by Simulating Human-Robot Collaboration","authors":"Syed T. Mubarrat;Antonio Fernandes;Suman K. Chowdhury","doi":"10.1109/THMS.2024.3407109","DOIUrl":"https://doi.org/10.1109/THMS.2024.3407109","url":null,"abstract":"Recent advancements in virtual reality (VR) technology facilitate tracking real-world objects and users' movements in the virtual environment (VE) and inspire researchers to develop a physics-based haptic system (i.e., real object haptics) instead of computer-generated haptic feedback. However, there is limited research on the efficacy of such VR systems in enhancing operators’ sensorimotor learning for tasks that require high motor and physical demands. Therefore, this study aimed to design and evaluate the efficacy of a physics-based VR system that provides users with realistic cutaneous and kinesthetic haptic feedback. We designed a physics-based VR system, named PhyVirtual, and simulated human–robot collaborative (HRC) sequential pick-and-place lifting tasks in the VE. Participants performed the same tasks in the real environment (RE) with human–human collaboration instead of human–robot collaboration. We used a custom-designed questionnaire, the NASA-TLX, and electromyography activities from biceps, middle, and anterior deltoid muscles to determine user experience, workload, and neuromuscular dynamics, respectively. Overall, the majority of responses (>65%) demonstrated that the system is easy-to-use, easy-to-learn, and effective in improving motor skill performance. While compared to tasks performed in the RE, no significant difference was observed in the overall workload for the PhyVirtual system. The electromyography data exhibited similar trends (\u0000<italic>p</i>\u0000 > 0.05; \u0000<italic>r</i>\u0000 > 0.89) for both environments. These results show that the PhyVirtual system is an effective tool to simulate safe human–robot collaboration commonly seen in many modern warehousing settings. Moreover, it can be used as a viable replacement for live sensorimotor training in a wide range of fields.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 4","pages":"375-384"},"PeriodicalIF":3.5,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725598","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 Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVD 利用基于 QR 分解的近似 SVD 从损坏的三维运动捕捉数据中快速高效地恢复人体动作

IF 3.5 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-06-05 DOI: 10.1109/THMS.2024.3400290

M. S. Subodh Raj;Sudhish N. George

{"title":"A Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVD","authors":"M. S. Subodh Raj;Sudhish N. George","doi":"10.1109/THMS.2024.3400290","DOIUrl":"https://doi.org/10.1109/THMS.2024.3400290","url":null,"abstract":"In this article, we propose a robust algorithm for the fast recovery of human actions from corrupted 3-D motion capture (mocap) sequences. The proposed algorithm can deal with misrepresentations and incomplete representations in mocap data simultaneously. Fast convergence of the proposed algorithm is ensured by minimizing the overhead associated with time and resource utilization. To this end, we have used an approximate singular value decomposition (SVD) based on QR decomposition and \u0000<inline-formula><tex-math>$ell _{2,1}$</tex-math></inline-formula>\u0000 norm minimization as a replacement for the conventional nuclear norm-based SVD. In addition, the proposed method is braced by incorporating the spatio-temporal properties of human action in the optimization problem. For this, we have introduced pair-wise hierarchical constraint and the trajectory movement constraint in the problem formulation. Finally, the proposed method is void of the requirement of a sizeable database for training the model. The algorithm can easily be adapted to work on any form of corrupted mocap sequences. The proposed algorithm is faster by 30% on average compared with the counterparts employing similar kinds of constraints with improved performance in recovery.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 4","pages":"395-405"},"PeriodicalIF":3.5,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725575","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

Gesture-mmWAVE: Compact and Accurate Millimeter-Wave Radar-Based Dynamic Gesture Recognition for Embedded Devices Gesture-mmWAVE：基于毫米波雷达的嵌入式设备紧凑而精确的动态手势识别技术

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-29 DOI: 10.1109/THMS.2024.3385124

Biao Jin;Xiao Ma;Bojun Hu;Zhenkai Zhang;Zhuxian Lian;Biao Wang

{"title":"Gesture-mmWAVE: Compact and Accurate Millimeter-Wave Radar-Based Dynamic Gesture Recognition for Embedded Devices","authors":"Biao Jin;Xiao Ma;Bojun Hu;Zhenkai Zhang;Zhuxian Lian;Biao Wang","doi":"10.1109/THMS.2024.3385124","DOIUrl":"10.1109/THMS.2024.3385124","url":null,"abstract":"Dynamic gesture recognition using millimeter-wave radar is a promising contactless mode of human–computer interaction with wide-ranging applications in various fields, such as intelligent homes, automatic driving, and sign language translation. However, the existing models have too many parameters and are unsuitable for embedded devices. To address this issue, we propose a dynamic gesture recognition method (named “Gesture-mmWAVE”) using millimeter-wave radar based on the multilevel feature fusion (MLFF) and transformer model. We first arrange each frame of the original echo collected by the frequency-modulated continuously modulated millimeter-wave radar in the Chirps × Samples format. Then, we use a 2-D fast Fourier transform to obtain the range-time map and Doppler-time map of gestures while improving the echo signal-to-noise ratio by coherent accumulation. Furthermore, we build an MLFF-transformer network for dynamic gesture recognition. The MLFF-transformer network comprises an MLFF module and a transformer module. The MLFF module employs the residual strategies to fuse the shallow, middle, and deep features and reduce the parameter size of the model using depthwise-separable convolution. The transformer module captures the global features of dynamic gestures and focuses on essential features using the multihead attention mechanism. The experimental results demonstrate that our proposed model achieves an average recognition accuracy of 99.11% on a dataset with 10% random interference. The scale of the proposed model is only 0.42M, which is 25% of that of the MobileNet V3-samll model. Thus, this method has excellent potential for application in embedded devices due to its small parameter size and high recognition accuracy.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"337-347"},"PeriodicalIF":3.6,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140837281","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 Synergistic Formal-Statistical Model for Recognizing Complex Human Activities 识别复杂人类活动的形式-统计协同模型

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-25 DOI: 10.1109/THMS.2024.3382468

Nikolaos Bourbakis;Anargyros Angeleas

引用次数: 0

CST Framework: A Robust and Portable Finger Motion Tracking Framework CST 框架：稳健、便携的手指运动跟踪框架

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-22 DOI: 10.1109/THMS.2024.3385105

Yong Ding;Mingchen Zou;Yueyang Teng;Yue Zhao;Xingyu Jiang;Xiaoyu Cui

{"title":"CST Framework: A Robust and Portable Finger Motion Tracking Framework","authors":"Yong Ding;Mingchen Zou;Yueyang Teng;Yue Zhao;Xingyu Jiang;Xiaoyu Cui","doi":"10.1109/THMS.2024.3385105","DOIUrl":"10.1109/THMS.2024.3385105","url":null,"abstract":"Finger motion tracking is a significant challenge in the field of motion capture. However, existing technology for finger motion tracking often requires the wearing of a heavy device and a laborious calibration process to track the bending angle of each joint; this can be challenging, particularly because the motion of each finger has a high coupling characteristic. To address this issue, in this work, we have proposed a compressed sensing-based tracking (CST) framework that enables the estimation of the bending angle of all hand joints using sensors smaller than the number of hand joints. Our framework also integrates a real-time calibration function, which significantly simplifies the calibration process. We developed a glove with multiple liquid metal sensors and an inertial measurement unit to evaluate the effectiveness of our CST framework. The experimental results show that our CST framework can achieve high-speed and accurate hand arbitrary motion capture with only 12 sensors. The motion-tracking gloves developed on this basis are user-friendly and particularly suitable for human–computer interaction applications in robot control, the metaverse and other fields.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"282-291"},"PeriodicalIF":3.6,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140636621","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

Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing 利用红外光波传感技术进行非接触式呼吸异常检测

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-15 DOI: 10.1109/THMS.2024.3381574

Md Zobaer Islam;Brenden Martin;Carly Gotcher;Tyler Martinez;John F. O'Hara;Sabit Ekin

{"title":"Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing","authors":"Md Zobaer Islam;Brenden Martin;Carly Gotcher;Tyler Martinez;John F. O'Hara;Sabit Ekin","doi":"10.1109/THMS.2024.3381574","DOIUrl":"10.1109/THMS.2024.3381574","url":null,"abstract":"Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and noninvasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies. The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision, and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5 m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the LWS setup.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"292-303"},"PeriodicalIF":3.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592650","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

Human-Centered Evaluation of EMG-Based Upper-Limb Prosthetic Control Modes 以人为本的 EMG 上肢假肢控制模式评估

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-11 DOI: 10.1109/THMS.2024.3381094

Yunmei Liu;Joseph Berman;Albert Dodson;Junho Park;Maryam Zahabi;He Huang;Jaime Ruiz;David B. Kaber

{"title":"Human-Centered Evaluation of EMG-Based Upper-Limb Prosthetic Control Modes","authors":"Yunmei Liu;Joseph Berman;Albert Dodson;Junho Park;Maryam Zahabi;He Huang;Jaime Ruiz;David B. Kaber","doi":"10.1109/THMS.2024.3381094","DOIUrl":"10.1109/THMS.2024.3381094","url":null,"abstract":"The aim of this study was to experimentally test the effects of different electromyographic-based prosthetic control modes on user task performance, cognitive workload, and perceived usability to inform further human-centered design and application of these prosthetic control interfaces. We recruited 30 able-bodied participants for a between-subjects comparison of three control modes: direct control (DC), pattern recognition (PR), and continuous control (CC). Multiple human-centered evaluations were used, including task performance, cognitive workload, and usability assessments. To ensure that the results were not task-dependent, this study used two different test tasks, including the clothespin relocation task and Southampton hand assessment procedure-door handle task. Results revealed performance with each control mode to vary among tasks. When the task had high-angle adjustment accuracy requirements, the PR control outperformed DC. For cognitive workload, the CC mode was superior to DC in reducing user load across tasks. Both CC and PR control appear to be effective alternatives to DC in terms of task performance and cognitive load. Furthermore, we observed that, when comparing control modes, multitask testing and multifaceted evaluations are critical to avoid task-induced or method-induced evaluation bias. Hence, future studies with larger samples and different designs will be needed to expand the understanding of prosthetic device features and workload relationships.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"271-281"},"PeriodicalIF":3.6,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592649","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

Human Vital Signs Estimation Using Resonance Sparse Spectrum Decomposition 利用共振稀疏频谱分解估算人体生命体征

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-11 DOI: 10.1109/THMS.2024.3381074

Anuradha Singh;Saeed Ur Rehman;Sira Yongchareon;Peter Han Joo Chong

{"title":"Human Vital Signs Estimation Using Resonance Sparse Spectrum Decomposition","authors":"Anuradha Singh;Saeed Ur Rehman;Sira Yongchareon;Peter Han Joo Chong","doi":"10.1109/THMS.2024.3381074","DOIUrl":"10.1109/THMS.2024.3381074","url":null,"abstract":"The noncontact measurement and monitoring of human vital signs has evolved into a valuable tool for efficient health management. Because of the greater penetration capability through material and clothes, which is less affected by environmental conditions such as illumination, temperature, and humidity, mmWave radar has been extensively researched for human vital sign measurement in the past years. However, interference due to unwanted clutter, random body movement, and respiration harmonics make accurate retrieval of the heart rate (HR) difficult. This article proposes a resonance sparse spectrum decomposition (RSSD) algorithm and harmonics used algorithm (HUA) for accurate HR extraction. RSSD addresses the clutter and random body movement effects from phase signals, while HUA uses harmonics to extract HR accurately. A set of controlled experiments was conducted under different scenarios, and the proposed method is validated against ground truth HR/RR data collected by a smart vest. Our results show an accuracy of up to 98%–100% for distances up to 2 m. The method substantially improves HR estimation accuracy by effectively mitigating the effects of noise in the phase signal, even under heavy clutter and moderate body movement. Our results demonstrate that the proposed method effectively counters harmonic interference for accurate estimation of HR comparable to RR estimation up to a distance of 4 m from the radar sensor.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"304-316"},"PeriodicalIF":3.6,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592756","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

Trust in Range Estimation System in Battery Electric Vehicles–A Mixed Approach 电池电动汽车里程估计系统中的信任--混合方法

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-08 DOI: 10.1109/THMS.2024.3381116

Jiyao Wang;Ran Tu;Ange Wang;Dengbo He

{"title":"Trust in Range Estimation System in Battery Electric Vehicles–A Mixed Approach","authors":"Jiyao Wang;Ran Tu;Ange Wang;Dengbo He","doi":"10.1109/THMS.2024.3381116","DOIUrl":"10.1109/THMS.2024.3381116","url":null,"abstract":"The electrification of vehicle power systems has become a dominant trend worldwide. However, with current technologies, range anxiety is still a major obstacle to the popularization of battery electric vehicles (BEVs). Previous research has found that users’ trust in the BEVs’ range estimation system (RES) is associated with their range anxiety. However, influential factors of trust in RES have not yet been explored. Thus, a questionnaire was designed to model the factors that are directly (i.e., implicit factors) and indirectly (i.e., explicit factors) associated with BEV users’ trust in RES. Following the three-layer automation trust framework (i.e., dispositional trust, situational trust, and learned trust), a questionnaire was designed and administrated online. In total, 367 valid samples were collected from BEV users in mainland China. A mixed approach combining Bayesian network (BN) and regression analyses (i.e., BN–regression mixed approach) was proposed to explore the potential topological relationships among factors. Four implicit factors (i.e., sensitivity to BEV brand, knowledge of RES, users’ emotional stability, and trust in the battery estimation system of their phones) have been found to be directly associated with BEV users’ trust in RES. Furthermore, four explicit factors (i.e., users’ highest education, regional charging infrastructure development, BEV brand, and household income) were found to be indirectly associated with users’ trust in RES. This study further demonstrates the effectiveness of using a BN–regression mixed approach to explore topological relationships among social–psychological factors. Future strategies aiming to modulate trust in RES can target toward factors in different levels of the topological structure.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 3","pages":"250-259"},"PeriodicalIF":3.6,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140592648","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

Present a World of Opportunity 呈现一个充满机遇的世界

IF 3.6 3区计算机科学

IEEE Transactions on Human-Machine Systems Pub Date : 2024-04-01 DOI: 10.1109/THMS.2024.3380299

引用次数: 0