Pub Date : 2024-01-10DOI: 10.1109/THMS.2023.3315519
Klaas O. Koerten;David. A. Abbink;Arkady Zgonnikov
Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-called phantom traffic jams are traffic jams that do not have a clear cause, such as a merging on-ramp or an accident. Phantom traffic jams make up 50% of all traffic jams and result from instabilities in the traffic flow that are caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.
{"title":"Haptic Shared Control for Dissipating Phantom Traffic Jams","authors":"Klaas O. Koerten;David. A. Abbink;Arkady Zgonnikov","doi":"10.1109/THMS.2023.3315519","DOIUrl":"https://doi.org/10.1109/THMS.2023.3315519","url":null,"abstract":"Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-called \u0000<italic>phantom traffic jams</i>\u0000 are traffic jams that do not have a clear cause, such as a merging on-ramp or an accident. Phantom traffic jams make up 50% of all traffic jams and result from instabilities in the traffic flow that are caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654819","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 : 2024-01-05DOI: 10.1109/THMS.2023.3339663
Douglas O'Shaughnessy
A review of techniques to improve distorted speech is presented, noting the strengths and weaknesses of common methods. Speech signals are discussed from the point of view of which features should be preserved to retain both naturalness and intelligibility. Enhancement methods range from classical spectral subtraction and Wiener filtering to recent deep neural network approaches. The difficulty of finding objective acoustic measures that approximate perceptual speech quality is discussed. Suggestions to improve these methods are made.
{"title":"Speech Enhancement—A Review of Modern Methods","authors":"Douglas O'Shaughnessy","doi":"10.1109/THMS.2023.3339663","DOIUrl":"https://doi.org/10.1109/THMS.2023.3339663","url":null,"abstract":"A review of techniques to improve distorted speech is presented, noting the strengths and weaknesses of common methods. Speech signals are discussed from the point of view of which features should be preserved to retain both naturalness and intelligibility. Enhancement methods range from classical spectral subtraction and Wiener filtering to recent deep neural network approaches. The difficulty of finding objective acoustic measures that approximate perceptual speech quality is discussed. Suggestions to improve these methods are made.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654896","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 : 2024-01-04DOI: 10.1109/THMS.2023.3342045
Xia Zhao;Zhao Li;Rui Fu;Chang Wang;Yingshi Guo
In recent years, the full-touch human–machine interface (HMI) mode has been widely used in vehicles built by Tesla. This interaction mode replaces the conventional physical interaction modality with a screen, and it has a good sense of technological experience. However, it is unclear whether this mode will make the driver's lateral control more challenging than the conventional mode (CM). To investigate this issue, two most common secondary tasks were designed: dialing and navigation entry tasks and real-world road experiments were conducted using two instrumented vehicles. The vehicle operating parameters and the driver manual data were collected in different modes, respectively. Interestingly, the opposite results were found regarding the effect of the full-touch mode (FTM) on the driver's lateral control ability in different secondary tasks. Compared with the CM, the lateral control ability decreased less during the dialing task relative to the baseline driving in the FTM, while the lateral control ability decreased more in the FTM during the navigation entry task. In addition, drivers’ lateral control decreased further as task difficulty and driving speed increased regardless of mode. This study provides a theoretical basis for the development of laws and regulations regarding full-touch HMI mode.
近年来，全触控人机界面（HMI）模式在特斯拉制造的汽车上得到了广泛应用。这种交互模式用屏幕取代了传统的物理交互方式，具有良好的科技体验感。然而，这种模式是否会使驾驶员的横向控制比传统模式（CM）更具挑战性，目前尚不清楚。为了研究这个问题，我们设计了两个最常见的辅助任务：拨号和导航输入任务，并使用两辆装有仪器的车辆进行了实际道路实验。分别在不同模式下收集了车辆运行参数和驾驶员手动数据。有趣的是，在不同次要任务中，全触控模式（FTM）对驾驶员横向控制能力的影响出现了相反的结果。与 CM 相比，全触控模式下驾驶员在拨号任务中的横向控制能力相对于基线驾驶下降较少，而全触控模式下驾驶员在导航输入任务中的横向控制能力下降较多。此外，无论驾驶模式如何，随着任务难度和驾驶速度的增加，驾驶员的横向控制能力都会进一步下降。本研究为全触控人机界面模式相关法律法规的制定提供了理论依据。
{"title":"What Challenges Does the Full-Touch HMI Mode Bring to Driver's Lateral Control Ability? A Comparative Study Based on Real Roads","authors":"Xia Zhao;Zhao Li;Rui Fu;Chang Wang;Yingshi Guo","doi":"10.1109/THMS.2023.3342045","DOIUrl":"https://doi.org/10.1109/THMS.2023.3342045","url":null,"abstract":"In recent years, the full-touch human–machine interface (HMI) mode has been widely used in vehicles built by Tesla. This interaction mode replaces the conventional physical interaction modality with a screen, and it has a good sense of technological experience. However, it is unclear whether this mode will make the driver's lateral control more challenging than the conventional mode (CM). To investigate this issue, two most common secondary tasks were designed: dialing and navigation entry tasks and real-world road experiments were conducted using two instrumented vehicles. The vehicle operating parameters and the driver manual data were collected in different modes, respectively. Interestingly, the opposite results were found regarding the effect of the full-touch mode (FTM) on the driver's lateral control ability in different secondary tasks. Compared with the CM, the lateral control ability decreased less during the dialing task relative to the baseline driving in the FTM, while the lateral control ability decreased more in the FTM during the navigation entry task. In addition, drivers’ lateral control decreased further as task difficulty and driving speed increased regardless of mode. This study provides a theoretical basis for the development of laws and regulations regarding full-touch HMI mode.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654665","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 : 2023-12-29DOI: 10.1109/THMS.2023.3344185
{"title":"2023 Index IEEE Transactions on Human-Machine Systems Vol. 53","authors":"","doi":"10.1109/THMS.2023.3344185","DOIUrl":"https://doi.org/10.1109/THMS.2023.3344185","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060152","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 : 2023-12-14DOI: 10.1109/THMS.2023.3305391
Ana C. De Oliveira;Ashish D. Deshpande
To determine the most effective interventions for poststroke patients, it is imperative to monitor the recovery process. Robotic exoskeletons' built-in sensing capabilities enable accurate kinematic measurement with no additional setup time. Although position sensors used in exoskeletons are accurate, a mismatch between the robot's and the human's joints can lead to inaccurate measurements. In addition, the robot's residual dynamics can interfere with human's natural movements and the kinematic metrics assessed in the robot would not be representative of the human's movement in free-motion. So far, the accuracy of robotic exoskeletons in assessing upper-body kinematics has not been verified. The bilateral upper-body Harmony exoskeleton has features favorable to minimize joint misalignments and the robot's residual dynamics. In this study, we examined Harmony's ability to accurately assess Cartesian-space kinematic parameters associated with the wearer's movement quality. We analyzed data collected from eight healthy participants that executed point-to-point movements with and without the presence of the robot and at fast and slow speeds. Ground truth was acquired with an optical motion capture, and we extracted the kinematic parameters from the measured data. The results suggest that Harmony can accurately measure kinematic parameters associated with movement quality, and these parameters could appropriately reflect wearer's natural movements at a slow speed. Therefore, Harmony could aid the evaluation of the effectiveness of different interventions, which is more sensitive and efficient than currently adopted clinical outcomes. This allows for individualization of a treatment plan and a detailed follow-up.
{"title":"Assessment of Upper-Body Movement Quality in the Cartesian-Space is Feasible in the Harmony Exoskeleton","authors":"Ana C. De Oliveira;Ashish D. Deshpande","doi":"10.1109/THMS.2023.3305391","DOIUrl":"https://doi.org/10.1109/THMS.2023.3305391","url":null,"abstract":"To determine the most effective interventions for poststroke patients, it is imperative to monitor the recovery process. Robotic exoskeletons' built-in sensing capabilities enable accurate kinematic measurement with no additional setup time. Although position sensors used in exoskeletons are accurate, a mismatch between the robot's and the human's joints can lead to inaccurate measurements. In addition, the robot's residual dynamics can interfere with human's natural movements and the kinematic metrics assessed in the robot would not be representative of the human's movement in free-motion. So far, the accuracy of robotic exoskeletons in assessing upper-body kinematics has not been verified. The bilateral upper-body Harmony exoskeleton has features favorable to minimize joint misalignments and the robot's residual dynamics. In this study, we examined Harmony's ability to accurately assess Cartesian-space kinematic parameters associated with the wearer's movement quality. We analyzed data collected from eight healthy participants that executed point-to-point movements with and without the presence of the robot and at fast and slow speeds. Ground truth was acquired with an optical motion capture, and we extracted the kinematic parameters from the measured data. The results suggest that Harmony can accurately measure kinematic parameters associated with movement quality, and these parameters could appropriately reflect wearer's natural movements at a slow speed. Therefore, Harmony could aid the evaluation of the effectiveness of different interventions, which is more sensitive and efficient than currently adopted clinical outcomes. This allows for individualization of a treatment plan and a detailed follow-up.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633789","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 : 2023-12-14DOI: 10.1109/THMS.2023.3329536
Frank Kulwa;Haoshi Zhang;Oluwarotimi Williams Samuel;Mojisola Grace Asogbon;Erik Scheme;Rami Khushaba;Alistair A. McEwan;Guanglin Li
The control performance of myoelectric prostheses would not only depend on the feature extraction and classification algorithms but also on interactions of dynamic window-based hyperparameters (WBHP) used to construct input signals. However, the relationship between these hyperparameters and how they influence the performance of the convolutional neural networks (CNNs) during motor intent decoding has not been studied. Therefore, we investigated the impact of various combinations of WBHP (window length and overlap) employed for the construction of raw two-dimensional (2-D) surface electromyogram (sEMG) signals on the performance of CNNs when used for motion intent decoding. Moreover, we examined the relationship between the window length of the 2-D sEMG and three commonly used CNN kernel sizes. To ensure high confidence in the findings, we implemented three CNNs, which are variants of the existing models, and a newly proposed CNN model. Experimental analysis was conducted using three distinct benchmark databases, two from upper limb amputees and one from able-bodied subjects. The results demonstrate that the performance of the CNNs improved as the overlap between consecutively generated 2-D signals increased, with 75% overlap yielding the optimal improvement by 12.62% accuracy and 39.60% F1-score compared to no overlap. Moreover, the CNNs performance was better for kernel size of seven than three and five across the databases. For the first time, we have established with multiple evidence that WBHP would substantially impact the decoding outcome and computational complexity of deep neural networks, and we anticipate that this may spur positive advancement in myoelectric control and related fields.
{"title":"A Multidataset Characterization of Window-Based Hyperparameters for Deep CNN-Driven sEMG Pattern Recognition","authors":"Frank Kulwa;Haoshi Zhang;Oluwarotimi Williams Samuel;Mojisola Grace Asogbon;Erik Scheme;Rami Khushaba;Alistair A. McEwan;Guanglin Li","doi":"10.1109/THMS.2023.3329536","DOIUrl":"https://doi.org/10.1109/THMS.2023.3329536","url":null,"abstract":"The control performance of myoelectric prostheses would not only depend on the feature extraction and classification algorithms but also on interactions of dynamic window-based hyperparameters (WBHP) used to construct input signals. However, the relationship between these hyperparameters and how they influence the performance of the convolutional neural networks (CNNs) during motor intent decoding has not been studied. Therefore, we investigated the impact of various combinations of WBHP (window length and overlap) employed for the construction of raw two-dimensional (2-D) surface electromyogram (sEMG) signals on the performance of CNNs when used for motion intent decoding. Moreover, we examined the relationship between the window length of the 2-D sEMG and three commonly used CNN kernel sizes. To ensure high confidence in the findings, we implemented three CNNs, which are variants of the existing models, and a newly proposed CNN model. Experimental analysis was conducted using three distinct benchmark databases, two from upper limb amputees and one from able-bodied subjects. The results demonstrate that the performance of the CNNs improved as the overlap between consecutively generated 2-D signals increased, with 75% overlap yielding the optimal improvement by 12.62% accuracy and 39.60% F1-score compared to no overlap. Moreover, the CNNs performance was better for kernel size of seven than three and five across the databases. For the first time, we have established with multiple evidence that WBHP would substantially impact the decoding outcome and computational complexity of deep neural networks, and we anticipate that this may spur positive advancement in myoelectric control and related fields.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654718","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 : 2023-12-14DOI: 10.1109/THMS.2023.3335731
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2023.3335731","DOIUrl":"https://doi.org/10.1109/THMS.2023.3335731","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10360367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633854","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 : 2023-12-14DOI: 10.1109/THMS.2023.3335735
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2023.3335735","DOIUrl":"https://doi.org/10.1109/THMS.2023.3335735","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10360341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633855","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 : 2023-12-14DOI: 10.1109/THMS.2023.3335733
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2023.3335733","DOIUrl":"https://doi.org/10.1109/THMS.2023.3335733","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10360347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633894","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 : 2023-12-11DOI: 10.1109/THMS.2023.3336294
Christopher Widdowson;Hyung-Jin Yoon;Naira Hovakimyan;Ranxiao Frances Wang
This article examines how people respond to the presence of a flying robot under various operating conditions using traditional human physiological measures and a novel head movement measurement. A central issue to the integration of flying robotic systems into human-populated environments is how to improve the level of comfort and safety for people around them. Traditional motion control algorithms in robotics tend to focus on the actual safety of collision avoidance. However, people's perceived safety is not necessarily equivalent to the actual safety of the vehicle. Therefore flight control systems must account for people's perception of safety beyond the actual safety of the aerial vehicles in order to allow for successful interaction between humans and the unmanned aerial vehicles (UAVs). Across three experiments participants passively observed quadrotor trajectories in a simulated virtual reality environment. Quadrotor flight characteristics were manipulated in terms of speed, altitude, and audibility to examine their effect on physiological arousal and head motion kinematics. Physiological arousal was greater when the quadrotor was flying with the audio on than off, and at eye-height than overhead, and decreased over repeated exposure. In addition, head acceleration away from the UAVs indicating defensive behavior was stronger for faster speed and audible UAVs. These data suggest head acceleration can serve as a new index specific for measuring perceived safety. Applications intended for human comfort need to consider constraints from specific measures of perceived safety in addition to traditional measures of general physiological arousal.
{"title":"A Novel Measure of Human Safety Perception in Response to Flight Characteristics of Collocated UAVs in Virtual Reality","authors":"Christopher Widdowson;Hyung-Jin Yoon;Naira Hovakimyan;Ranxiao Frances Wang","doi":"10.1109/THMS.2023.3336294","DOIUrl":"10.1109/THMS.2023.3336294","url":null,"abstract":"This article examines how people respond to the presence of a flying robot under various operating conditions using traditional human physiological measures and a novel head movement measurement. A central issue to the integration of flying robotic systems into human-populated environments is how to improve the level of comfort and safety for people around them. Traditional motion control algorithms in robotics tend to focus on the actual safety of collision avoidance. However, people's perceived safety is not necessarily equivalent to the actual safety of the vehicle. Therefore flight control systems must account for people's perception of safety beyond the actual safety of the aerial vehicles in order to allow for successful interaction between humans and the unmanned aerial vehicles (UAVs). Across three experiments participants passively observed quadrotor trajectories in a simulated virtual reality environment. Quadrotor flight characteristics were manipulated in terms of speed, altitude, and audibility to examine their effect on physiological arousal and head motion kinematics. Physiological arousal was greater when the quadrotor was flying with the audio on than off, and at eye-height than overhead, and decreased over repeated exposure. In addition, head acceleration away from the UAVs indicating defensive behavior was stronger for faster speed and audible UAVs. These data suggest head acceleration can serve as a new index specific for measuring perceived safety. Applications intended for human comfort need to consider constraints from specific measures of perceived safety in addition to traditional measures of general physiological arousal.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139661829","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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