{"title":"ClenchClick:利用咬牙增强现实的免提目标选择方法","authors":"Xiyuan Shen, Yukang Yan, Chun Yu, Yuanchun Shi","doi":"10.1145/3550327","DOIUrl":null,"url":null,"abstract":"We propose to explore teeth-clenching-based target selection in Augmented Reality (AR), as the subtlety in the interaction can be beneficial to applications occupying the user’s hand or that are sensitive to social norms. To support the investigation, we implemented an EMG-based teeth-clenching detection system (ClenchClick), where we adopted customized thresholds for different users. We first explored and compared the potential interaction design leveraging head movements and teeth clenching in combination. We finalized the interaction to take the form of a Point-and-Click manner with clenches as the confirmation mechanism. We evaluated the taskload and performance of ClenchClick by comparing it with two baseline methods in target selection tasks. Results showed that ClenchClick outperformed hand gestures in workload, physical load, accuracy and speed, and outperformed dwell in work load and temporal load. Lastly, through user studies, we demonstrated the advantage of ClenchClick in real-world tasks, including efficient and accurate hands-free target selection, natural and unobtrusive interaction in public, and robust head gesture input. investigated the interaction design, user experience in target selection tasks, and user performance in real-world tasks in a series of user studies. In our first user study, we explored nine potential designs and compared the three most promising designs (ClenchClick, ClenchCross-ingTarget, ClenchCrossingEdge) with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline in target selection tasks. ClenchClick had the best overall user experience with the lowest workload. It outperformed Hand Gesture in both physical and temporal load, and outperformed Dwell in temporal and mental load. In the second study, we evaluated the performance of ClenchClick with two detection methods (General and Personalized), in comparison with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline. Results showed that ClenchClick outperformed Hand Gesture in accuracy (98.9% v.s. 89.4%), and was comparable with Dwell in accuracy and efficiency. We further investigated users’ behavioral characteristics by analyzing their cursor trajectories in the tasks, which showed that ClenchClick was a smoother target selection method. It was more psychologically friendly and occupied less of the user’s attention. Finally, we conducted user studies in three real-world tasks which supported hands-free, social-friendly, and head gesture interaction. Results revealed that ClenchClick is an efficient and accurate target selection method when both hands are occupied. It is social-friendly and satisfying when performing in public, and can serve as activation to head gestures which significantly alleviates false positive issues.","PeriodicalId":20463,"journal":{"name":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.","volume":"27 1","pages":"139:1-139:26"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"ClenchClick: Hands-Free Target Selection Method Leveraging Teeth-Clench for Augmented Reality\",\"authors\":\"Xiyuan Shen, Yukang Yan, Chun Yu, Yuanchun Shi\",\"doi\":\"10.1145/3550327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose to explore teeth-clenching-based target selection in Augmented Reality (AR), as the subtlety in the interaction can be beneficial to applications occupying the user’s hand or that are sensitive to social norms. To support the investigation, we implemented an EMG-based teeth-clenching detection system (ClenchClick), where we adopted customized thresholds for different users. We first explored and compared the potential interaction design leveraging head movements and teeth clenching in combination. We finalized the interaction to take the form of a Point-and-Click manner with clenches as the confirmation mechanism. We evaluated the taskload and performance of ClenchClick by comparing it with two baseline methods in target selection tasks. Results showed that ClenchClick outperformed hand gestures in workload, physical load, accuracy and speed, and outperformed dwell in work load and temporal load. Lastly, through user studies, we demonstrated the advantage of ClenchClick in real-world tasks, including efficient and accurate hands-free target selection, natural and unobtrusive interaction in public, and robust head gesture input. investigated the interaction design, user experience in target selection tasks, and user performance in real-world tasks in a series of user studies. In our first user study, we explored nine potential designs and compared the three most promising designs (ClenchClick, ClenchCross-ingTarget, ClenchCrossingEdge) with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline in target selection tasks. ClenchClick had the best overall user experience with the lowest workload. It outperformed Hand Gesture in both physical and temporal load, and outperformed Dwell in temporal and mental load. In the second study, we evaluated the performance of ClenchClick with two detection methods (General and Personalized), in comparison with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline. Results showed that ClenchClick outperformed Hand Gesture in accuracy (98.9% v.s. 89.4%), and was comparable with Dwell in accuracy and efficiency. We further investigated users’ behavioral characteristics by analyzing their cursor trajectories in the tasks, which showed that ClenchClick was a smoother target selection method. It was more psychologically friendly and occupied less of the user’s attention. Finally, we conducted user studies in three real-world tasks which supported hands-free, social-friendly, and head gesture interaction. Results revealed that ClenchClick is an efficient and accurate target selection method when both hands are occupied. 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引用次数: 2
摘要
我们建议在增强现实(AR)中探索基于咬牙的目标选择,因为交互中的微妙之处可能有利于占用用户的手或对社会规范敏感的应用程序。为了支持调查,我们实现了一个基于肌电图的咬牙检测系统(ClenchClick),我们为不同的用户采用了定制的阈值。我们首先探索并比较了头部运动和咬牙结合的潜在交互设计。我们最终确定了互动的形式,以点击的方式,握紧作为确认机制。通过与两种基线方法在目标选择任务中的比较,我们评估了ClenchClick的任务负载和性能。结果表明,ClenchClick在工作量、物理负荷、准确性和速度上优于手势,在工作负荷和时间负荷上优于驻留。最后,通过用户研究,我们展示了ClenchClick在现实世界任务中的优势,包括高效准确的免提目标选择,自然而不引人注目的公共互动,以及强大的头部手势输入。在一系列的用户研究中,研究了交互设计、目标选择任务中的用户体验和现实任务中的用户表现。在我们的第一个用户研究中,我们探索了九种潜在的设计,并将三种最有前途的设计(ClenchClick, ClenchCross-ingTarget, ClenchCrossingEdge)与基于手的(手势)和无手的(Dwell)基线在目标选择任务中进行比较。ClenchClick具有最佳的总体用户体验和最低的工作负载。它在身体和时间负荷上都优于手势,在时间和精神负荷上优于Dwell。在第二项研究中,我们用两种检测方法(通用和个性化)评估了ClenchClick的性能,并与基于手的(手势)和免提的(Dwell)基线进行了比较。结果表明,ClenchClick在准确率上优于Hand Gesture (98.9% vs . 89.4%),在准确率和效率上与Dwell相当。通过分析用户在任务中的光标轨迹,我们进一步研究了用户的行为特征,结果表明ClenchClick是一种更平滑的目标选择方法。它在心理上更友好,占用用户的注意力更少。最后,我们在三个支持免提、社交友好和头部手势交互的现实世界任务中进行了用户研究。结果表明,当双手被占用时,ClenchClick是一种高效、准确的目标选择方法。在公共场合表演时,它是社交友好的,令人满意的,并且可以作为头部动作的激活,显着减轻假阳性问题。
ClenchClick: Hands-Free Target Selection Method Leveraging Teeth-Clench for Augmented Reality
We propose to explore teeth-clenching-based target selection in Augmented Reality (AR), as the subtlety in the interaction can be beneficial to applications occupying the user’s hand or that are sensitive to social norms. To support the investigation, we implemented an EMG-based teeth-clenching detection system (ClenchClick), where we adopted customized thresholds for different users. We first explored and compared the potential interaction design leveraging head movements and teeth clenching in combination. We finalized the interaction to take the form of a Point-and-Click manner with clenches as the confirmation mechanism. We evaluated the taskload and performance of ClenchClick by comparing it with two baseline methods in target selection tasks. Results showed that ClenchClick outperformed hand gestures in workload, physical load, accuracy and speed, and outperformed dwell in work load and temporal load. Lastly, through user studies, we demonstrated the advantage of ClenchClick in real-world tasks, including efficient and accurate hands-free target selection, natural and unobtrusive interaction in public, and robust head gesture input. investigated the interaction design, user experience in target selection tasks, and user performance in real-world tasks in a series of user studies. In our first user study, we explored nine potential designs and compared the three most promising designs (ClenchClick, ClenchCross-ingTarget, ClenchCrossingEdge) with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline in target selection tasks. ClenchClick had the best overall user experience with the lowest workload. It outperformed Hand Gesture in both physical and temporal load, and outperformed Dwell in temporal and mental load. In the second study, we evaluated the performance of ClenchClick with two detection methods (General and Personalized), in comparison with a hand-based (Hand Gesture) and a hands-free (Dwell) baseline. Results showed that ClenchClick outperformed Hand Gesture in accuracy (98.9% v.s. 89.4%), and was comparable with Dwell in accuracy and efficiency. We further investigated users’ behavioral characteristics by analyzing their cursor trajectories in the tasks, which showed that ClenchClick was a smoother target selection method. It was more psychologically friendly and occupied less of the user’s attention. Finally, we conducted user studies in three real-world tasks which supported hands-free, social-friendly, and head gesture interaction. Results revealed that ClenchClick is an efficient and accurate target selection method when both hands are occupied. It is social-friendly and satisfying when performing in public, and can serve as activation to head gestures which significantly alleviates false positive issues.