{"title":"Design of a Robotic System Featured With High Operation Transparency for Quantifying Arm Impedance During Ultrasound Scanning","authors":"Baoshan Niu, Dapeng Yang, Yangjunjian Zhou, Le Zhang, Qi Huang, Yikun Gu","doi":"10.1109/thms.2024.3442537","DOIUrl":"https://doi.org/10.1109/thms.2024.3442537","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199165","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 : 2024-09-10DOI: 10.1109/thms.2024.3450689
Tsung-Han Yang, Yi-Chun Du, Cheng-Bin Xu, Wei-Siang Ciou
{"title":"Development of a MR Training System for Living Donor Liver Transplantation Using Simulated Liver Phantom and ICP Tracking Technology","authors":"Tsung-Han Yang, Yi-Chun Du, Cheng-Bin Xu, Wei-Siang Ciou","doi":"10.1109/thms.2024.3450689","DOIUrl":"https://doi.org/10.1109/thms.2024.3450689","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199164","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 : 2024-08-30DOI: 10.1109/THMS.2024.3434573
Shuhei Watanabe;Takahiko Horiuchi
To design the “Kansei value” aspect of a product, it is useful to design multilayered relationships of perceptual and affective responses via the physical or psychophysical properties of the product. However, because they are qualitative and ambiguous, designing a model is time-consuming. Moreover, the design was conducted by hypothesis and trial-and-error by the experimenter. In this article, we developed a method to automatically construct several semioptimal structures by applying a genetic algorithm to model design based on structural equation modeling, using the results of image measurement and subjective evaluation experiments on various material samples. Under set convergence conditions, the method constructed statistically optimized structures that represent the relationships among adjectives describing perception and affective, and the properties. A semantic validation was performed to determine the final model. As a result, the proposed method could be used to construct a model that can be interpreted as semantically and statistically superior compared to methods in related studies. A unique feature of this article was the use of the physical and psychophysical properties obtained by measurements in the construction of a multilayer model. Also, the advantage of this method is that it can be used to construct important structures that may be overlooked.
{"title":"Layered Modeling of Affective, Perception, and Visual Properties: Optimizing Structure With Genetic Algorithm","authors":"Shuhei Watanabe;Takahiko Horiuchi","doi":"10.1109/THMS.2024.3434573","DOIUrl":"10.1109/THMS.2024.3434573","url":null,"abstract":"To design the “Kansei value” aspect of a product, it is useful to design multilayered relationships of perceptual and affective responses via the physical or psychophysical properties of the product. However, because they are qualitative and ambiguous, designing a model is time-consuming. Moreover, the design was conducted by hypothesis and trial-and-error by the experimenter. In this article, we developed a method to automatically construct several semioptimal structures by applying a genetic algorithm to model design based on structural equation modeling, using the results of image measurement and subjective evaluation experiments on various material samples. Under set convergence conditions, the method constructed statistically optimized structures that represent the relationships among adjectives describing perception and affective, and the properties. A semantic validation was performed to determine the final model. As a result, the proposed method could be used to construct a model that can be interpreted as semantically and statistically superior compared to methods in related studies. A unique feature of this article was the use of the physical and psychophysical properties obtained by measurements in the construction of a multilayer model. Also, the advantage of this method is that it can be used to construct important structures that may be overlooked.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199167","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 : 2024-08-13DOI: 10.1109/THMS.2024.3434680
Peng Liu;Yueying Chu;Guanqun Wang;Zhigang Xu
Failures of the automated driving system (ADS) in automated vehicles (AVs) can damage driver–ADS cooperation (e.g., causing trust damage) and traffic safety. Researchers suggest infusing a human-like ability, active trust repair, into automated systems, to mitigate broken trust and other negative impacts resulting from their failures. Trust repair is regarded as a key ergonomic design in automated systems. Trust repair strategies (e.g., apology) are examined and supported by some evidence in controlled environments, however, rarely subjected to empirical evaluations in more naturalistic environments. To fill this gap, we conducted a test track study, invited participants ( N = 257) to experience an ADS failure, and tested the influence of the ADS’ trust repair on trust and other psychological responses. Half of participants ( n = 128) received the ADS’ verbal message (consisting of apology, explanation, and promise) by a human voice ( n = 63) or by Apple's Siri ( n = 65) after its failure. We measured seven psychological responses to AVs and ADS [e.g., trust and behavioral intention (BI)]. We found that both strategies cannot repair damaged trust. The human-voice-repair strategy can to some degree mitigate other detrimental influences (e.g., reductions in BI) resulting from the ADS failure, but this effect is only notable among participants without substantial driving experience. It points to the importance of conducting ecologically valid and field studies for validating human-like trust repair strategies in human–automation interaction and of developing trust repair strategies specific to safety-critical situations.
{"title":"To Err is Automation: Can Trust be Repaired by the Automated Driving System After its Failure?","authors":"Peng Liu;Yueying Chu;Guanqun Wang;Zhigang Xu","doi":"10.1109/THMS.2024.3434680","DOIUrl":"10.1109/THMS.2024.3434680","url":null,"abstract":"Failures of the automated driving system (ADS) in automated vehicles (AVs) can damage driver–ADS cooperation (e.g., causing trust damage) and traffic safety. Researchers suggest infusing a human-like ability, active trust repair, into automated systems, to mitigate broken trust and other negative impacts resulting from their failures. Trust repair is regarded as a key ergonomic design in automated systems. Trust repair strategies (e.g., apology) are examined and supported by some evidence in controlled environments, however, rarely subjected to empirical evaluations in more naturalistic environments. To fill this gap, we conducted a test track study, invited participants (\u0000<italic>N</i>\u0000 = 257) to experience an ADS failure, and tested the influence of the ADS’ trust repair on trust and other psychological responses. Half of participants (\u0000<italic>n</i>\u0000 = 128) received the ADS’ verbal message (consisting of apology, explanation, and promise) by a human voice (\u0000<italic>n</i>\u0000 = 63) or by Apple's Siri (\u0000<italic>n</i>\u0000 = 65) after its failure. We measured seven psychological responses to AVs and ADS [e.g., trust and behavioral intention (BI)]. We found that both strategies cannot repair damaged trust. The human-voice-repair strategy can to some degree mitigate other detrimental influences (e.g., reductions in BI) resulting from the ADS failure, but this effect is only notable among participants without substantial driving experience. It points to the importance of conducting ecologically valid and field studies for validating human-like trust repair strategies in human–automation interaction and of developing trust repair strategies specific to safety-critical situations.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199166","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 : 2024-08-07DOI: 10.1109/THMS.2024.3432864
Suvodip Chakraborty;Peter Kiefer;Martin Raubal
From the initial phases of human–computer interaction, where the computer was unaware of the users' mental states, we are now progressing toward cognition-aware user interfaces. One crucial cognitive state considered by research on cognition-aware user interfaces is the cognitive load. Eye-tracking has been suggested as one particularly unobtrusive method for estimating cognitive load. Although the accuracy of cognitive load detection has improved in recent work, it is still insufficient for cognition-aware user interfaces, which require high accuracy for getting accepted by the user. This article introduces two new eye-tracking metrics for estimating perceived cognitive load based on benign anisocoria (BA). Unlike previous pupil-based metrics, our metrics are based on pupil size asymmetry between the left and right eye. As a case study, we illustrate the effectiveness of the proposed metrics on a recently published eye-tracking dataset recorded under laboratory conditions. The results show that our proposed features based on BA can improve the performance of classifiers for detecting the perceived mental workload associated with an $N$ -back test. The best classification accuracy was 84.24% while the classification accuracy in the absence of the proposed features was 81.91% for the light gradient boosting classifier.
在人机交互的最初阶段，计算机对用户的心理状态一无所知，而现在，我们正朝着认知感知用户界面的方向迈进。认知感知用户界面研究考虑的一个关键认知状态是认知负荷。眼球跟踪被认为是估算认知负荷的一种特别不显眼的方法。虽然认知负荷检测的准确度在最近的研究中有所提高，但对于认知感知用户界面来说仍然不够，因为用户界面需要高准确度才能被用户接受。本文介绍了两种新的眼动跟踪指标，用于估计基于良性异视（BA）的感知认知负荷。与以往基于瞳孔的指标不同，我们的指标基于左右眼瞳孔大小的不对称。作为一项案例研究，我们在最近发布的实验室条件下记录的眼动数据集上说明了所提指标的有效性。结果表明，我们提出的基于 BA 的特征可以提高分类器在检测与 N$-back 测试相关的感知脑力劳动负荷时的性能。光梯度提升分类器的最佳分类准确率为 84.24%，而在没有使用所提出特征的情况下，分类准确率为 81.91%。
{"title":"Estimating Perceived Mental Workload From Eye-Tracking Data Based on Benign Anisocoria","authors":"Suvodip Chakraborty;Peter Kiefer;Martin Raubal","doi":"10.1109/THMS.2024.3432864","DOIUrl":"10.1109/THMS.2024.3432864","url":null,"abstract":"From the initial phases of human–computer interaction, where the computer was unaware of the users' mental states, we are now progressing toward cognition-aware user interfaces. One crucial cognitive state considered by research on cognition-aware user interfaces is the cognitive load. Eye-tracking has been suggested as one particularly unobtrusive method for estimating cognitive load. Although the accuracy of cognitive load detection has improved in recent work, it is still insufficient for cognition-aware user interfaces, which require high accuracy for getting accepted by the user. This article introduces two new eye-tracking metrics for estimating perceived cognitive load based on benign anisocoria (BA). Unlike previous pupil-based metrics, our metrics are based on pupil size asymmetry between the left and right eye. As a case study, we illustrate the effectiveness of the proposed metrics on a recently published eye-tracking dataset recorded under laboratory conditions. The results show that our proposed features based on BA can improve the performance of classifiers for detecting the perceived mental workload associated with an \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000-back test. The best classification accuracy was 84.24% while the classification accuracy in the absence of the proposed features was 81.91% for the light gradient boosting classifier.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10629234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931222","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 : 2024-08-02DOI: 10.1109/THMS.2024.3426302
Dongwon Kim;Jong-Moon Hwang
It is generally accepted that stimuli with different time intervals lead to different contributions of relevant learning substrates. We investigated postural responses in men to trains of perturbations with different time intervals. A total of 12 male volunteers with no neurological deficits (age: 33.33±3.12 S.D.) experienced a sequence of perturbations. Two sequences of perturbations by a translational plate to still standing participants in the anterior-posterior direction were designed and administered: the first sequence consisted of 24 repeated perturbations with an interval of 5 s, while the second sequence consisted of ones with an interval of 2.5 s. A perturbation of a smaller magnitude was inserted into each sequence as a catch trial. We found that while a shorter interval and a longer interval both led to the learning effect (P < 0.05), a shorter interval results in a stiff strategy, presumably through muscle cocontraction, minimizing the degree of body sway. The learned motor response continued after experiencing the catch trial (P < 0.05). The results imply that stimulus intervals could lead to a different adaptation mechanism in the neuromotor system in the way to regain postural stability. Also, our results suggest that training for male individuals with repetition of postural perturbations with a shorter time interval leads to a stiff strategy with a greater degree. A stiff strategy could lower the ability to cope with unexpected postural threats, possibly leading to falls.
{"title":"Responses to External Repeated Perturbations Vary With Time Intervals","authors":"Dongwon Kim;Jong-Moon Hwang","doi":"10.1109/THMS.2024.3426302","DOIUrl":"10.1109/THMS.2024.3426302","url":null,"abstract":"It is generally accepted that stimuli with different time intervals lead to different contributions of relevant learning substrates. We investigated postural responses in men to trains of perturbations with different time intervals. A total of 12 male volunteers with no neurological deficits (age: 33.33±3.12 S.D.) experienced a sequence of perturbations. Two sequences of perturbations by a translational plate to still standing participants in the anterior-posterior direction were designed and administered: the first sequence consisted of 24 repeated perturbations with an interval of 5 s, while the second sequence consisted of ones with an interval of 2.5 s. A perturbation of a smaller magnitude was inserted into each sequence as a catch trial. We found that while a shorter interval and a longer interval both led to the learning effect (P < 0.05), a shorter interval results in a stiff strategy, presumably through muscle cocontraction, minimizing the degree of body sway. The learned motor response continued after experiencing the catch trial (P < 0.05). The results imply that stimulus intervals could lead to a different adaptation mechanism in the neuromotor system in the way to regain postural stability. Also, our results suggest that training for male individuals with repetition of postural perturbations with a shorter time interval leads to a stiff strategy with a greater degree. A stiff strategy could lower the ability to cope with unexpected postural threats, possibly leading to falls.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886117","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 : 2024-08-02DOI: 10.1109/THMS.2024.3432735
Nan Zhang;Tianqi Huang;Hongen Liao
In traditional clinical practice, doctors often have to deal with 3D information based on 2D-displayed medical images. There is a considerable mismatch between the 2D and 3D dimensions in image interaction during clinical diagnosis, making image manipulation challenging and time-consuming. In this study, we explored 3D sketching for medical images using true 2D-3D interlinked visualization and interaction, presenting a novel AR environment named ARMedicalSketch. It supports image display enhancement preprocessing and 3D interaction tasks for original 3D medical images. Our interaction interface, based on 3D autostereoscopic display technology, provides both floating 3D display and 2D tablet display while enabling glasses-free visualization. We presented a method of 2D-3D interlinked visualization and interaction, employing synchronized projection visualization and a virtual synchronized interactive plane to establish an integrated relationship between 2D and 3D displays. Additionally, we utilized gesture sensors and a 2D touch tablet to capture the user's hand information for convenient interaction. We constructed the prototype and conducted a user study involving 23 students and 2 clinical experts. The controlled study compared our proposed system with a 2D display prototype, showing enhanced efficiency in interacting with medical images while maintaining 2D interaction accuracy, particularly in tasks involving strong 3D spatial correlation. In the future, we aim to further enhance the interaction precision and application scenarios of ARMedicalSketch.
在传统临床实践中，医生往往需要根据二维显示的医学图像处理三维信息。在临床诊断过程中，图像交互的 2D 和 3D 维度之间存在相当大的不匹配，使得图像处理变得具有挑战性且耗时。在这项研究中，我们利用真正的 2D-3D 互联可视化和交互技术探索了医学图像的 3D 草图绘制，并提出了一种名为 ARMedicalSketch 的新型 AR 环境。它支持原始三维医学图像的图像显示增强预处理和三维交互任务。我们的交互界面基于三维自动立体显示技术，可提供浮动三维显示和二维平板显示，同时实现裸眼可视化。我们提出了一种 2D-3D 互联可视化和交互方法，采用同步投影可视化和虚拟同步交互平面来建立 2D 和 3D 显示屏之间的集成关系。此外，我们还利用手势传感器和二维触摸板来捕捉用户的手部信息，从而实现便捷的交互。我们制作了原型并进行了用户研究，共有 23 名学生和 2 名临床专家参与。对照研究将我们提出的系统与二维显示器原型进行了比较，结果表明，在保持二维交互准确性的同时，我们提高了与医学图像交互的效率，尤其是在涉及三维空间相关性较强的任务中。未来，我们的目标是进一步提高 ARMedicalSketch 的交互精度和应用场景。
{"title":"ARMedicalSketch: Exploring 3D Sketching for Medical Image Using True 2D-3D Interlinked Visualization and Interaction","authors":"Nan Zhang;Tianqi Huang;Hongen Liao","doi":"10.1109/THMS.2024.3432735","DOIUrl":"10.1109/THMS.2024.3432735","url":null,"abstract":"In traditional clinical practice, doctors often have to deal with 3D information based on 2D-displayed medical images. There is a considerable mismatch between the 2D and 3D dimensions in image interaction during clinical diagnosis, making image manipulation challenging and time-consuming. In this study, we explored 3D sketching for medical images using true 2D-3D interlinked visualization and interaction, presenting a novel AR environment named ARMedicalSketch. It supports image display enhancement preprocessing and 3D interaction tasks for original 3D medical images. Our interaction interface, based on 3D autostereoscopic display technology, provides both floating 3D display and 2D tablet display while enabling glasses-free visualization. We presented a method of 2D-3D interlinked visualization and interaction, employing synchronized projection visualization and a virtual synchronized interactive plane to establish an integrated relationship between 2D and 3D displays. Additionally, we utilized gesture sensors and a 2D touch tablet to capture the user's hand information for convenient interaction. We constructed the prototype and conducted a user study involving 23 students and 2 clinical experts. The controlled study compared our proposed system with a 2D display prototype, showing enhanced efficiency in interacting with medical images while maintaining 2D interaction accuracy, particularly in tasks involving strong 3D spatial correlation. In the future, we aim to further enhance the interaction precision and application scenarios of ARMedicalSketch.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886116","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}
This article investigated the automatic recognition of felt and musically communicated emotions using electroencephalogram (EEG), electrocardiogram (ECG), and acoustic signals, which were recorded from eleven musicians instructed to perform music in order to communicate happiness, sadness, relaxation, and anger. Musicians' self-reports indicated that the emotions they musically expressed were highly consistent with those they actually felt. Results showed that the best classification performances, in a subject-dependent classification using a KNN classifier were achieved by using features derived from both the EEG and ECG (with an accuracy of 98.11%). Which was significantly more accurate than using ECG features alone, but was not significantly more accurate than using EEG features alone. The use of acoustic features alone or in combination with EEG and/or ECG features did not lead to better performances than those achieved with EEG plus ECG or EEG alone. Our results suggest that emotion detection of playing musicians, both felt and musically communicated, when coherent, can be classified in a more reliable way using physiological features than involving acoustic features. The reported machine learning results are a step toward the development of affective brain–computer interfaces capable of automatically inferring the emotions of a playing musician in real-time.
{"title":"Emotion Recognition of Playing Musicians From EEG, ECG, and Acoustic Signals","authors":"Luca Turchet;Barry O'Sullivan;Rupert Ortner;Christoph Guger","doi":"10.1109/THMS.2024.3430327","DOIUrl":"10.1109/THMS.2024.3430327","url":null,"abstract":"This article investigated the automatic recognition of felt and musically communicated emotions using electroencephalogram (EEG), electrocardiogram (ECG), and acoustic signals, which were recorded from eleven musicians instructed to perform music in order to communicate happiness, sadness, relaxation, and anger. Musicians' self-reports indicated that the emotions they musically expressed were highly consistent with those they actually felt. Results showed that the best classification performances, in a subject-dependent classification using a KNN classifier were achieved by using features derived from both the EEG and ECG (with an accuracy of 98.11%). Which was significantly more accurate than using ECG features alone, but was not significantly more accurate than using EEG features alone. The use of acoustic features alone or in combination with EEG and/or ECG features did not lead to better performances than those achieved with EEG plus ECG or EEG alone. Our results suggest that emotion detection of playing musicians, both felt and musically communicated, when coherent, can be classified in a more reliable way using physiological features than involving acoustic features. The reported machine learning results are a step toward the development of affective brain–computer interfaces capable of automatically inferring the emotions of a playing musician in real-time.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886137","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 : 2024-07-24DOI: 10.1109/THMS.2024.3421538
Jongkil Jay Jeong;Syed Wajid Ali Shah;Ashish Nanda;Robin Doss;Mohammad Nosouhi;Jeb Webb
Physical authentication devices (PADs) offer a higher level of security than other authentication technologies commonly used in multifactor authentication (MFA) schemes because they are much less vulnerable to attack. However, PAD uptake remains significantly lower than that for SMS and app-based approaches, accounting for only 10% of all authentication technologies currently being utilized in MFA. Prior studies indicate that the primary reason for this low adoption rate is due to negative users' perceptions and attitudes toward the usability of PADs; many of these studies often skew toward a particular set of users (e.g., young university students, etc.), often creating a bias toward what usable security entails. To address this limitation, we have formulated an original research methodology that segments users into specific groups based on their user characteristics (i.e., age, education, and experience) and examines how each group defines usability and ranks their preferences regarding certain security features. Based on a survey of 410 participants, our results indicate that there are indeed different usable security preferences for each user group, and we, therefore, provide recommendations on how existing PADs might be enhanced to support usability and improve adoption rates.
与多因素身份验证（MFA）方案中常用的其他身份验证技术相比，物理身份验证设备（PAD）具有更高的安全性，因为它们更不易受到攻击。然而，与基于短信和应用程序的方法相比，物理身份验证设备的使用率仍然很低，只占目前多因素身份验证（MFA）中使用的所有身份验证技术的 10%。先前的研究表明，采用率低的主要原因是用户对 PAD 可用性的负面看法和态度；其中许多研究往往偏向于特定的用户群体（如年轻的大学生等），往往会对可用的安全性产生偏见。为了解决这一局限性，我们制定了一种独创的研究方法，根据用户特征（如年龄、教育程度和经验）将用户划分为特定的群体，并研究每个群体如何定义可用性以及他们对某些安全功能的偏好程度。在对 410 名参与者进行调查的基础上，我们的结果表明，每个用户群体确实有不同的可用安全偏好，因此，我们就如何增强现有 PAD 以支持可用性和提高采用率提出了建议。
{"title":"User Characteristics and Their Impact on the Perceived Usable Security of Physical Authentication Devices","authors":"Jongkil Jay Jeong;Syed Wajid Ali Shah;Ashish Nanda;Robin Doss;Mohammad Nosouhi;Jeb Webb","doi":"10.1109/THMS.2024.3421538","DOIUrl":"10.1109/THMS.2024.3421538","url":null,"abstract":"Physical authentication devices (PADs) offer a higher level of security than other authentication technologies commonly used in multifactor authentication (MFA) schemes because they are much less vulnerable to attack. However, PAD uptake remains significantly lower than that for SMS and app-based approaches, accounting for only 10% of all authentication technologies currently being utilized in MFA. Prior studies indicate that the primary reason for this low adoption rate is due to negative users' perceptions and attitudes toward the usability of PADs; many of these studies often skew toward a particular set of users (e.g., young university students, etc.), often creating a bias toward what usable security entails. To address this limitation, we have formulated an original research methodology that segments users into specific groups based on their user characteristics (i.e., age, education, and experience) and examines how each group defines usability and ranks their preferences regarding certain security features. Based on a survey of 410 participants, our results indicate that there are indeed different usable security preferences for each user group, and we, therefore, provide recommendations on how existing PADs might be enhanced to support usability and improve adoption rates.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772570","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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