无线AM:通过商品Wi-Fi实现跨域手势识别
Jiahao Xie1, Zhenfen Li1, Chao Feng1
1School of Information Science and Technology, Northwest University, Xi'an 710127, China.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
无线网络传感和深度学习可以实现保护隐私的手势识别. Wi-AM框架使用最小的数据准确地识别新领域的手势,克服现有系统的局限性.
科学领域:
- 智能多媒体技术是智能多媒体技术.
- 人与计算机的互动.
- 机器学习用于信号处理.
背景情况:
- 与计算机视觉系统相比,基于射频 (RF) 的手势识别提供了增强的用户隐私.
- 将Wi-Fi传感与深度学习相结合,为智能多媒体应用提供了新的机会.
- 目前的基于射频的系统在域泛化方面遇到了困难,并且需要广泛的数据来适应新的环境.
研究的目的:
- 提出Wi-AM,这是一个用于保护隐私的手势识别的新框架.
- 解决现有系统在跨领域性能和数据要求方面的局限性.
- 以最小的标记数据在新领域实现准确的手势识别.
主要方法:
- 开发了一个多域对抗方案,以最大限度地减少特定域的数据分布差异,并提取可转移的手势特征.
- 实施了超级学习框架,以快速适应未见的领域,使用少量学习.
- 利用Wi-Fi传感和深度学习来获取和分析手势数据.
主要成果:
- 无线AM在识别未见领域的手势方面实现了高精度,即使每种手势只有一种样本.
- 在现实数据集中,一个样本的平均准确率为82.13%,三个样本的平均准确率为86.76%.
- 多域对抗和元学习方法有效地减少了对新领域大数据集的需求.
结论:
- 无线AM显著改善跨领域的手势识别性能,同时保持用户隐私.
- 拟议的框架为基于射频的手势识别的广泛采用提供了一个实际的解决方案.
- 最小的数据要求使Wi-AM能够适应各种现实世界的场景,并使其高效.
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