Pub Date : 2024-07-18DOI: 10.1109/THMS.2024.3427293
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3427293","DOIUrl":"https://doi.org/10.1109/THMS.2024.3427293","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725625","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 : 2024-07-18DOI: 10.1109/THMS.2024.3427300
{"title":"Call for Papers: Special Issue on Trustworthy Human-Autonomy Teaming: Featured Research from the 4th International Conference on Human-Machine Systems (ICHMS 2024)","authors":"","doi":"10.1109/THMS.2024.3427300","DOIUrl":"https://doi.org/10.1109/THMS.2024.3427300","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725664","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 : 2024-07-18DOI: 10.1109/THMS.2024.3427297
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2024.3427297","DOIUrl":"https://doi.org/10.1109/THMS.2024.3427297","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604671","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725576","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 : 2024-07-18DOI: 10.1109/THMS.2024.3427295
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3427295","DOIUrl":"https://doi.org/10.1109/THMS.2024.3427295","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10604716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725663","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 : 2024-07-16DOI: 10.1109/THMS.2024.3422267
Aura Ximena Gonzalez-Cely;Cristian Felipe Blanco-Diaz;Teodiano Bastos-Filho;Camilo Arturo Rodriguez-Diaz
Prevention is key to avoid pressure ulcer generation in people with mobility restrictions. In recent years, preventive medicine has focused on posture control by considering people who frequently have the same position for too long, such as wheelchair users. Optical fiber sensors have gained recognition for their applications in biomedical engineering; however, approaches to assistive devices, such as wheelchairs, have been relatively unexplored. This study proposes a polymeric-optical-fiber (POF) sensing system based on machine learning (ML) for human posture recognition in an electrical wheelchair-based human machine interface (HMI). The ML-based model was used to classify time- and frequency-domain features obtained from a matrix of POF-based pressure sensors and 24 photodetectors during the execution of eight body postures. In an offline stage, multiclassification was conducted using k-nearest neighbors (KNN), decision tree, extra tree classifier (ETC), and random forest, where the best performance, in terms of accuracy (ACC), was obtained through the use of ETC (94%). Hence, this classifier was implemented in real-time, where the wheelchair-based HMI achieved a CPU time of approximately 117 ms, and an ACC higher than 96%, outperforming the metrics previously reported in the literature. We believe that this study contributes to the development of smart assistive systems that integrate ML and soft sensors to recognize body postures in an HMI, which is a promising approach for preventing the generation of pressure ulcers in wheelchair users.
预防是避免行动不便者产生压疮的关键。近年来，预防医学将重点放在姿势控制上，考虑到那些经常长时间保持同一姿势的人，如轮椅使用者。光纤传感器因其在生物医学工程中的应用而获得认可；然而，用于辅助设备（如轮椅）的方法却相对较少。本研究提出了一种基于机器学习（ML）的聚合光纤（POF）传感系统，用于识别电动轮椅人机界面（HMI）中的人体姿势。基于 ML 的模型被用于对从基于 POF 的压力传感器矩阵和 24 个光电探测器获得的时域和频域特征进行分类，这些特征是在八个身体姿势的执行过程中获得的。在离线阶段，使用 k-近邻（KNN）、决策树、额外树分类器（ETC）和随机森林进行了多重分类，其中使用 ETC 的准确率（ACC）最高（94%）。因此，该分类器是实时实现的，基于轮椅的人机界面的 CPU 时间约为 117 毫秒，ACC 高于 96%，优于以前文献中报告的指标。我们相信，这项研究有助于开发智能辅助系统，该系统集成了人工智能和软传感器，可识别人机界面中的身体姿势，是防止轮椅使用者产生压疮的一种有前途的方法。
{"title":"Real-Time Posture Identification System for Wheelchair Users Preventing the Generation of Pressure Ulcers","authors":"Aura Ximena Gonzalez-Cely;Cristian Felipe Blanco-Diaz;Teodiano Bastos-Filho;Camilo Arturo Rodriguez-Diaz","doi":"10.1109/THMS.2024.3422267","DOIUrl":"10.1109/THMS.2024.3422267","url":null,"abstract":"Prevention is key to avoid pressure ulcer generation in people with mobility restrictions. In recent years, preventive medicine has focused on posture control by considering people who frequently have the same position for too long, such as wheelchair users. Optical fiber sensors have gained recognition for their applications in biomedical engineering; however, approaches to assistive devices, such as wheelchairs, have been relatively unexplored. This study proposes a polymeric-optical-fiber (POF) sensing system based on machine learning (ML) for human posture recognition in an electrical wheelchair-based human machine interface (HMI). The ML-based model was used to classify time- and frequency-domain features obtained from a matrix of POF-based pressure sensors and 24 photodetectors during the execution of eight body postures. In an offline stage, multiclassification was conducted using k-nearest neighbors (KNN), decision tree, extra tree classifier (ETC), and random forest, where the best performance, in terms of accuracy (ACC), was obtained through the use of ETC (94%). Hence, this classifier was implemented in real-time, where the wheelchair-based HMI achieved a CPU time of approximately 117 ms, and an ACC higher than 96%, outperforming the metrics previously reported in the literature. We believe that this study contributes to the development of smart assistive systems that integrate ML and soft sensors to recognize body postures in an HMI, which is a promising approach for preventing the generation of pressure ulcers in wheelchair users.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141722593","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 : 2024-07-09DOI: 10.1109/THMS.2024.3408804
P. A. Hancock;Jessica Cruit
Changes in technology, particularly for example in commercial air operations, have led to incremental increases in the number and variety of associated safety procedures and checklists. This work addresses concerns about how people and systems respond to unanticipated events, as applicable to such commercial air operations, and to examine whether these “ if–then ” approaches prove sufficient to respond to prospective operational uncertainties. The current work draws from the literature on systems resilience, cognitive flexibility, and adaptation to consider how response strategies that are integrated into human–machine training at all levels of operation can aid in effective resolution to such unanticipated events. A number of scientific insights, methods, and domains are identified as being able to be employed to avoid catastrophic failure in current and prospective operational environments. While heuristics for advisement do provide an initial level of defensive protection, evolving airspace operations need to be adaptive to, and resilient in respect of, emerging and even unanticipated challenges. Prospective response strategies need to encompass both the demands that can be evidently foreseen and those that remain at present, indeterminate. Resilience in responding appears to be a primary dimension of success in relation to these challenges. The information herein distilled can increase operator performance and aviation systems’ response to nonproceduralized and unanticipated events as well as being applied to a vast array of other safety-critical operations beyond this one realm.
{"title":"Resilience in Operators, Technologies, and Systems","authors":"P. A. Hancock;Jessica Cruit","doi":"10.1109/THMS.2024.3408804","DOIUrl":"10.1109/THMS.2024.3408804","url":null,"abstract":"Changes in technology, particularly for example in commercial air operations, have led to incremental increases in the number and variety of associated safety procedures and checklists. This work addresses concerns about how people and systems respond to unanticipated events, as applicable to such commercial air operations, and to examine whether these “\u0000<sc>if–then</small>\u0000” approaches prove sufficient to respond to prospective operational uncertainties. The current work draws from the literature on systems resilience, cognitive flexibility, and adaptation to consider how response strategies that are integrated into human–machine training at all levels of operation can aid in effective resolution to such unanticipated events. A number of scientific insights, methods, and domains are identified as being able to be employed to avoid catastrophic failure in current and prospective operational environments. While heuristics for advisement do provide an initial level of defensive protection, evolving airspace operations need to be adaptive to, and resilient in respect of, emerging and even unanticipated challenges. Prospective response strategies need to encompass both the demands that can be evidently foreseen and those that remain at present, indeterminate. Resilience in responding appears to be a primary dimension of success in relation to these challenges. The information herein distilled can increase operator performance and aviation systems’ response to nonproceduralized and unanticipated events as well as being applied to a vast array of other safety-critical operations beyond this one realm.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567776","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 : 2024-07-02DOI: 10.1109/THMS.2024.3414208
Francesco De Pace;Federico Manuri;Matteo Bosco;Andrea Sanna;Hannes Kaufmann
This article presents a brain–computer interface (BCI) coupled with an augmented reality (AR) system to support human–robot interaction in controlling a robotic arm for pick-and-place tasks. BCIs can process steady-state visual evoked potentials (SSVEPs), which are signals generated through visual stimuli. The visual stimuli may be conveyed to the user with AR systems, expanding the range of possible applications. The proposed approach leverages the capabilities of the NextMind BCI to enable users to select objects in the range of the robotic arm. By displaying a visual anchor associated with each object in the scene with projected AR, the NextMind device can detect when users focus their eyesight on one of them, thus triggering the pick-up action of the robotic arm. The proposed system has been designed considering the needs and limitations of mobility-impaired people to support them when controlling a robotic arm for pick-and-place tasks. Two different approaches for positioning the visual anchors are proposed and analyzed. Experimental tests involving users show that both approaches are highly appreciated. The system performances are extremely robust, thus allowing the users to select objects in an easy, fast, and reliable way.
本文介绍了一种与增强现实（AR）系统相结合的脑机接口（BCI），用于支持人机交互，控制机械臂完成拾放任务。脑机接口可以处理稳态视觉诱发电位（SSVEPs），这是通过视觉刺激产生的信号。视觉刺激可通过 AR 系统传达给用户，从而扩大了可能的应用范围。所提出的方法充分利用了 NextMind BCI 的功能，使用户能够选择机械臂范围内的物体。通过投射 AR 显示与场景中每个物体相关的视觉锚点，NextMind 设备可以检测用户何时将视线集中在其中一个物体上，从而触发机械臂的拾取动作。该系统的设计考虑到了行动不便者的需求和局限性，以支持他们控制机械臂完成拾放任务。系统提出并分析了两种不同的视觉锚定位方法。用户参与的实验测试表明，这两种方法都受到了高度评价。系统性能非常稳定，因此用户可以轻松、快速、可靠地选择对象。
{"title":"Supporting Human–Robot Interaction by Projected Augmented Reality and a Brain Interface","authors":"Francesco De Pace;Federico Manuri;Matteo Bosco;Andrea Sanna;Hannes Kaufmann","doi":"10.1109/THMS.2024.3414208","DOIUrl":"10.1109/THMS.2024.3414208","url":null,"abstract":"This article presents a brain–computer interface (BCI) coupled with an augmented reality (AR) system to support human–robot interaction in controlling a robotic arm for pick-and-place tasks. BCIs can process steady-state visual evoked potentials (SSVEPs), which are signals generated through visual stimuli. The visual stimuli may be conveyed to the user with AR systems, expanding the range of possible applications. The proposed approach leverages the capabilities of the NextMind BCI to enable users to select objects in the range of the robotic arm. By displaying a visual anchor associated with each object in the scene with projected AR, the NextMind device can detect when users focus their eyesight on one of them, thus triggering the pick-up action of the robotic arm. The proposed system has been designed considering the needs and limitations of mobility-impaired people to support them when controlling a robotic arm for pick-and-place tasks. Two different approaches for positioning the visual anchors are proposed and analyzed. Experimental tests involving users show that both approaches are highly appreciated. The system performances are extremely robust, thus allowing the users to select objects in an easy, fast, and reliable way.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10581874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516478","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}
With rapid advancements in artificial intelligence (AI) technologies, AI-powered machines are increasingly capable of collaborating with humans to enhance decision-making in various human–machine collaboration scenarios, e.g., medical diagnosis, criminal justice, and autonomous driving. As a result, human–machine computing (HMC) has emerged as a promising computing paradigm that integrates the expertise of humans with the reliable data processing capabilities of machines. Using HMC to facilitate the processing of domain-specific tasks has a lot of potential, but is limited in system-level scalability, i.e., there is no one common easy-to-use interface. In this article, we present human-machine operating system (hmOS) , an open extensible platform for researchers to experiment with HMC for investigating system-centric human–machine collaboration problems. hmOS supports flexible human–machine collaboration on the strength of the quality-aware task decomposition and allocation. To achieve that, the underlying system architecture and runtime environment are first developed to build a foundational abstraction for the kernel of hmOS . Second, hmOS facilitates flexible human–machine collaboration through a suitability-based task allocation mechanism, quality estimation guided by fuzzy rules, and iterative feedback on result tuning. We implement the newly proposed hmOS in a prototype featuring interactive interfaces. Finally, we conduct extensive and realistic experiments to validate the effectiveness of our platform across diverse tasks, showcasing the broad feasibility of hmOS .
{"title":"hmOS: An Extensible Platform for Task-Oriented Human–Machine Computing","authors":"Hui Wang;Zhiwen Yu;Yao Zhang;Yanfei Wang;Fan Yang;Liang Wang;Jiaqi Liu;Bin Guo","doi":"10.1109/THMS.2024.3414432","DOIUrl":"10.1109/THMS.2024.3414432","url":null,"abstract":"With rapid advancements in artificial intelligence (AI) technologies, AI-powered machines are increasingly capable of collaborating with humans to enhance decision-making in various human–machine collaboration scenarios, e.g., medical diagnosis, criminal justice, and autonomous driving. As a result, human–machine computing (HMC) has emerged as a promising computing paradigm that integrates the expertise of humans with the reliable data processing capabilities of machines. Using HMC to facilitate the processing of domain-specific tasks has a lot of potential, but is limited in system-level scalability, i.e., there is no one common easy-to-use interface. In this article, we present human-machine operating system \u0000<monospace>(hmOS)</monospace>\u0000, an open extensible platform for researchers to experiment with HMC for investigating system-centric human–machine collaboration problems. \u0000<monospace>hmOS</monospace>\u0000 supports flexible human–machine collaboration on the strength of the quality-aware task decomposition and allocation. To achieve that, the underlying system architecture and runtime environment are first developed to build a foundational abstraction for the kernel of \u0000<monospace>hmOS</monospace>\u0000. Second, \u0000<monospace>hmOS</monospace>\u0000 facilitates flexible human–machine collaboration through a suitability-based task allocation mechanism, quality estimation guided by fuzzy rules, and iterative feedback on result tuning. We implement the newly proposed \u0000<monospace>hmOS</monospace>\u0000 in a prototype featuring interactive interfaces. Finally, we conduct extensive and realistic experiments to validate the effectiveness of our platform across diverse tasks, showcasing the broad feasibility of \u0000<monospace>hmOS</monospace>\u0000.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516476","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 : 2024-07-02DOI: 10.1109/THMS.2024.3413781
Suatmi Murnani;Noor Akhmad Setiawan;Sunu Wibirama
The human gaze is a promising input modality for interactive applications due to its advantages: giving benefits to motion-impaired people while providing faster, intuitive, and easy interaction. The most common form of gaze interaction is object selection. During the last decade, gaze gestures and smooth pursuit-based interaction have been emerging techniques for spontaneous object selection in various gaze-controlled applications. Unfortunately, the challenge of spontaneous interaction demands no prior gaze-to-screen calibration, which leads to inaccurate object selection. To overcome the accuracy issue, this article proposes a novel method for spontaneous gaze interaction based on Pearson product-moment correlation as a measure of similarity, an exponential moving average filter for signal denoising, and a hidden Markov model to perform eye movement classification. Based on experimental results, our approach yielded the best object selection accuracy and success time of $text{89.60}pm text{10.59}%$ and $text{4364}pm text{235.86}$ ms, respectively. Our results imply that spontaneous interaction for gaze-controlled applications is possible with careful consideration of the underlying techniques to handle noisy data generated by the eye tracker. Furthermore, the proposed method is promising for future development of interactive touchless display systems that comply with the health protocols of the World Health Organization during the COVID-19 pandemic.
{"title":"Robust Object Selection in Spontaneous Gaze-Controlled Application Using Exponential Moving Average and Hidden Markov Model","authors":"Suatmi Murnani;Noor Akhmad Setiawan;Sunu Wibirama","doi":"10.1109/THMS.2024.3413781","DOIUrl":"10.1109/THMS.2024.3413781","url":null,"abstract":"The human gaze is a promising input modality for interactive applications due to its advantages: giving benefits to motion-impaired people while providing faster, intuitive, and easy interaction. The most common form of gaze interaction is object selection. During the last decade, gaze gestures and smooth pursuit-based interaction have been emerging techniques for spontaneous object selection in various gaze-controlled applications. Unfortunately, the challenge of spontaneous interaction demands no prior gaze-to-screen calibration, which leads to inaccurate object selection. To overcome the accuracy issue, this article proposes a novel method for spontaneous gaze interaction based on Pearson product-moment correlation as a measure of similarity, an exponential moving average filter for signal denoising, and a hidden Markov model to perform eye movement classification. Based on experimental results, our approach yielded the best object selection accuracy and success time of \u0000<inline-formula><tex-math>$text{89.60}pm text{10.59}%$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$text{4364}pm text{235.86}$</tex-math></inline-formula>\u0000 ms, respectively. Our results imply that spontaneous interaction for gaze-controlled applications is possible with careful consideration of the underlying techniques to handle noisy data generated by the eye tracker. Furthermore, the proposed method is promising for future development of interactive touchless display systems that comply with the health protocols of the World Health Organization during the COVID-19 pandemic.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516475","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}
Hands are a fundamental tool humans use to interact with the environment and objects. Through hand motions, we can obtain information about the shape and materials of the surfaces we touch, modify our surroundings by interacting with objects, manipulate objects and tools, or communicate with other people by leveraging the power of gestures. For these reasons, sensorized gloves, which can collect information about hand motions and interactions, have been of interest since the 1980s in various fields, such as human–machine interaction and the analysis and control of human motions. Over the last 40 years, research in this field explored different technological approaches and contributed to the popularity of wearable custom and commercial products targeting hand sensorization. Despite a positive research trend, these instruments are not widespread yet outside research environments and devices aimed at research are often ad hoc solutions with a low chance of being reused. This article aims to provide a systematic literature review for custom gloves to analyze their main characteristics and critical issues, from the type and number of sensors to the limitations due to device encumbrance. The collection of this information lays the foundation for a standardization process necessary for future breakthroughs in this research field.
{"title":"A Systematic Review on Custom Data Gloves","authors":"Valerio Belcamino;Alessandro Carfì;Fulvio Mastrogiovanni","doi":"10.1109/THMS.2024.3394674","DOIUrl":"10.1109/THMS.2024.3394674","url":null,"abstract":"Hands are a fundamental tool humans use to interact with the environment and objects. Through hand motions, we can obtain information about the shape and materials of the surfaces we touch, modify our surroundings by interacting with objects, manipulate objects and tools, or communicate with other people by leveraging the power of gestures. For these reasons, sensorized gloves, which can collect information about hand motions and interactions, have been of interest since the 1980s in various fields, such as human–machine interaction and the analysis and control of human motions. Over the last 40 years, research in this field explored different technological approaches and contributed to the popularity of wearable custom and commercial products targeting hand sensorization. Despite a positive research trend, these instruments are not widespread yet outside research environments and devices aimed at research are often ad hoc solutions with a low chance of being reused. This article aims to provide a systematic literature review for custom gloves to analyze their main characteristics and critical issues, from the type and number of sensors to the limitations due to device encumbrance. The collection of this information lays the foundation for a standardization process necessary for future breakthroughs in this research field.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500639","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}
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