IEEE Transactions on Human-Machine Systems最新文献_第8页

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

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 : 2024-04-01 DOI: 10.1109/THMS.2024.3380293

引用次数: 0

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 : 2024-04-01 DOI: 10.1109/THMS.2024.3380291

引用次数: 0

Connect. Support. Inspire. 连接。支持。激发灵感。

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.2024.3380309

引用次数: 0

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 : 2024-04-01 DOI: 10.1109/THMS.2024.3380295

引用次数: 0