Phone-based CSI Hand Gesture Recognition with Lightweight Image-Classification Model
Phone-based CSI Hand Gesture Recognition with Lightweight Image-Classification Model
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DOI：
10.1145/3565287.3617613
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发表时间：
2023-10
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影响因子：
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通讯作者：
Ashkan Arabi;Michael Straus;Zijie Tang;Zhengkun Ye;Yan Wang
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作者：
Ashkan Arabi;Michael Straus;Zijie Tang;Zhengkun Ye;Yan Wang
随着岁月的流逝，智能手机正成为日常生活的重要组成部分，使用户比以往任何时候都更加与他们互动。但是，有时候用户很难直接操作其设备。当前，用户可以触摸其设备进行直接交互，也可以使用语音命令进行更简单的任务。尽管这两种方法非常有能力与设备互动，但它们有局限性。触摸物理设备并不总是实用的，而语音命令在大声的环境中变得无效。一个很好的例子是，如果用户在嘈杂的环境中洗碗，那么物理控制和语音命令都不方便。现有的智能手机CSI手势识别系统取决于手动特征提取，随着手势的数量和复杂性的增长，可能很难实现。我们研究通过实施和测试这种体系结构的性能，使用轻巧的图像分类模型以最少的预处理使用的可行性。我们从三台设置和两款手机中收集了五个手势的数据，我们的系统能够获得90.0％的精度。此外，我们研究了不同人员，距离和电话对系统性能的影响。
As years pass, smartphones are becoming a larger part of daily lives, causing users to interact with them more than ever. There are moments, however, when it becomes difficult for the user to operate their device directly. Currently, a user can either touch their devices for direct interaction, or use voice commands for simpler tasks. Although these two methods are very capable means of interacting with the devices, they have their limitations. Touching a physical device is not always practical, while voice commands become ineffective in loud environments. A good example would be if the user is washing dishes in a noisy environment, where neither physical control nor voice commands are convenient. Existing systems of smartphone CSI gesture recognition rely on manual feature extraction which could be hard to implement as gestures grow in number and complexity. We study the feasibility of using lightweight image classification models with minimal preprocessing by implementing and testing the performance of such an architecture. We collect data for five gestures from three setups and two phones, on which our system is able to obtain 90.0% accuracy. Additionally, we investigate the impact of different people, distances, and phones on the system's performance.