Pub Date : 2024-04-22DOI: 10.1109/THMS.2024.3385105
Yong Ding;Mingchen Zou;Yueyang Teng;Yue Zhao;Xingyu Jiang;Xiaoyu Cui
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.
{"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}
Pub Date : 2024-04-15DOI: 10.1109/THMS.2024.3381574
Md Zobaer Islam;Brenden Martin;Carly Gotcher;Tyler Martinez;John F. O'Hara;Sabit Ekin
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.
{"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}
Pub Date : 2024-04-11DOI: 10.1109/THMS.2024.3381094
Yunmei Liu;Joseph Berman;Albert Dodson;Junho Park;Maryam Zahabi;He Huang;Jaime Ruiz;David B. Kaber
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.
本研究旨在通过实验测试基于肌电图的不同假肢控制模式对用户任务表现、认知工作量和感知可用性的影响，为这些假肢控制界面的进一步以人为本的设计和应用提供参考。我们招募了 30 名健全参与者，对直接控制 (DC)、模式识别 (PR) 和连续控制 (CC) 三种控制模式进行了主体间比较。我们采用了多种以人为本的评估方法，包括任务表现、认知工作量和可用性评估。为确保结果不依赖于任务，本研究使用了两种不同的测试任务，包括衣夹重新定位任务和南安普顿手部评估程序--门把手任务。结果显示，每种控制模式在不同任务中的表现各不相同。当任务对角度调整精度要求较高时，PR 控制模式的表现优于 DC 控制模式。在认知工作量方面，CC 模式在减少用户任务负荷方面优于 DC。就任务性能和认知负荷而言，CC 和 PR 控制似乎都是 DC 的有效替代方案。此外，我们还发现，在比较控制模式时，多任务测试和多方面评估对于避免任务或方法引起的评估偏差至关重要。因此，未来的研究需要更多的样本和不同的设计，以扩大对假肢装置特征和工作量关系的理解。
{"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}
Pub Date : 2024-04-11DOI: 10.1109/THMS.2024.3381074
Anuradha Singh;Saeed Ur Rehman;Sira Yongchareon;Peter Han Joo Chong
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.
{"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}
Pub Date : 2024-04-08DOI: 10.1109/THMS.2024.3381116
Jiyao Wang;Ran Tu;Ange Wang;Dengbo He
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.
汽车动力系统电气化已成为全球的主流趋势。然而，在现有技术条件下，续航里程焦虑仍然是电池电动汽车（BEV）普及的主要障碍。以往的研究发现，用户对 BEV 的续航里程估计系统（RES）的信任与他们的续航里程焦虑有关。然而，尚未探究信任度的影响因素。因此，我们设计了一份调查问卷，以模拟与 BEV 用户对 RES 信任直接相关的因素（即隐性因素）和间接相关的因素（即显性因素）。按照三层自动化信任框架（即处置信任、情景信任和学习信任），设计了一份问卷并进行了在线发放。共收集了 367 份来自中国大陆 BEV 用户的有效样本。研究采用贝叶斯网络（BN）和回归分析相结合的混合方法（即 BN- 回归混合方法）来探索各因素之间的潜在拓扑关系。研究发现，四个隐性因素（即对 BEV 品牌的敏感性、对 RES 的了解、用户的情绪稳定性和对手机电池估算系统的信任）与 BEV 用户对 RES 的信任直接相关。此外，研究还发现四个显性因素（即用户的最高学历、地区充电基础设施发展情况、BEV 品牌和家庭收入）与用户对可再生能源的信任间接相关。本研究进一步证明了使用BN-回归混合方法探索社会心理因素之间拓扑关系的有效性。未来旨在调节用户对 RES 信任度的策略可以针对拓扑结构中不同层次的因素。
{"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}
Pub Date : 2024-04-01DOI: 10.1109/THMS.2024.3380299
{"title":"Present a World of Opportunity","authors":"","doi":"10.1109/THMS.2024.3380299","DOIUrl":"https://doi.org/10.1109/THMS.2024.3380299","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 2","pages":"227-227"},"PeriodicalIF":3.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10486968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340109","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 : 2024-04-01DOI: 10.1109/THMS.2024.3380293
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/THMS.2024.3380293","DOIUrl":"https://doi.org/10.1109/THMS.2024.3380293","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 2","pages":"226-226"},"PeriodicalIF":3.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10486987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340107","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 : 2024-04-01DOI: 10.1109/THMS.2024.3380291
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3380291","DOIUrl":"https://doi.org/10.1109/THMS.2024.3380291","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 2","pages":"C2-C2"},"PeriodicalIF":3.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10486988","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340105","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 : 2024-04-01DOI: 10.1109/THMS.2024.3380295
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3380295","DOIUrl":"https://doi.org/10.1109/THMS.2024.3380295","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 2","pages":"C3-C3"},"PeriodicalIF":3.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10486939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140340092","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：