Headar

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2023-09-27 DOI:10.1145/3610900
Xiaoying Yang, Xue Wang, Gaofeng Dong, Zihan Yan, Mani Srivastava, Eiji Hayashi, Yang Zhang
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Abstract

Nod and shake of one's head are intuitive and universal gestures in communication. As smartwatches become increasingly intelligent through advances in user activity sensing technologies, many use scenarios of smartwatches demand quick responses from users in confirmation dialogs, to accept or dismiss proposed actions. Such proposed actions include making emergency calls, taking service recommendations, and starting or stopping exercise timers. Head gestures in these scenarios could be preferable to touch interactions for being hands-free and easy to perform. We propose Headar to recognize these gestures on smartwatches using wearable millimeter wave sensing. We first surveyed head gestures to understand how they are performed in conversational settings. We then investigated positions and orientations to which users raise their smartwatches. Insights from these studies guided the implementation of Headar. Additionally, we conducted modeling and simulation to verify our sensing principle. We developed a real-time sensing and inference pipeline using contemporary deep learning techniques, and proved the feasibility of our proposed approach with a user study (n=15) and a live test (n=8). Our evaluation yielded an average accuracy of 84.0% in the user study across 9 classes including nod and shake as well as seven other signals -- still, speech, touch interaction, and four non-gestural head motions (i.e., head up, left, right, and down). Furthermore, we obtained an accuracy of 72.6% in the live test which reveals rich insights into the performance of our approach in various realistic conditions.
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Headar
点头和摇头是一种直观的、通用的交流手势。随着用户活动感知技术的进步,智能手表变得越来越智能,许多智能手表的使用场景需要用户在确认对话框中快速响应,接受或拒绝提议的操作。这些建议的行动包括拨打紧急电话,接受服务建议,启动或停止锻炼计时器。在这些情况下,头部手势可能比触摸交互更可取,因为它不需要手,而且易于操作。我们建议Headar使用可穿戴毫米波传感技术来识别智能手表上的这些手势。我们首先调查了头部手势,以了解它们在对话环境中的表现。然后,我们调查了用户举起智能手表的位置和方向。这些研究的见解指导了Headar的实施。此外,我们还进行了建模和仿真来验证我们的传感原理。我们使用当代深度学习技术开发了一个实时传感和推理管道,并通过用户研究(n=15)和现场测试(n=8)证明了我们提出的方法的可行性。我们的评估在9个类别的用户研究中产生了84.0%的平均准确率,包括点头和摇晃以及其他七种信号——静止、语音、触摸交互和四种非手势头部运动(即头向上、向左、向右和向下)。此外,我们在现场测试中获得了72.6%的准确率,这揭示了我们的方法在各种现实条件下的性能的丰富见解。
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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