论文标题
使用WiFi信号进行人机相互作用的联合人类取向 - 活性识别
Joint Human Orientation-Activity Recognition Using WiFi Signals for Human-Machine Interaction
论文作者
论文摘要
WiFi传感是新的WiFi 802.11bf标准的重要组成部分，该标准可以检测运动和测量距离。近年来，已经提出了一些机器学习方法，以供WiFi信号识别人类活动。但是，据我们所知，这些方法都没有使用WiFi信号探讨用户的方向预测。在具有多个智能设备的环境中，方向预测对于人机相互作用特别重要。在本文中，我们建议使用来自单个访问点（AP）或多个AP的WIFI信号的联合人类方向和活动识别的数据收集设置和机器学习模型。结果表明，在室内环境中具有很高准确性的联合取向 - 活性识别的可行性。
WiFi sensing is an important part of the new WiFi 802.11bf standard, which can detect motion and measure distances. In recent years, some machine learning methods have been proposed for human activity recognition from WiFi signals. However, to the best of our knowledge, none of these methods have explored orientation prediction of the user using WiFi signals. Orientation prediction is particularly critical for human-machine interaction in an environment with multiple smart devices. In this paper, we propose a data collection setup and machine learning models for joint human orientation and activity recognition using WiFi signals from a single access point (AP) or multiple APs. The results show feasibility of joint orientation-activity recognition in an indoor environment with a high accuracy.
