基于无线信号的人类行为检测和识别

物联网技术实现了物与物、人与物的全面互联，其中信息传感设备与人的交互需要对人体行为活动进行感知。目前广泛使用的有基于视觉和利用穿戴式传感器的识别方法，但这些方法在很多场景下应用有所限制。文章提出一种基于无线信号识别人类行为的方法，通过对通信中传输数据包状态的统计和分析，能够利用少量通信节点达到感知非携带设备的目标在室内检测区域行为活动的目的。对于不同的行为活动特征，采用序列最小优化算法、 K-最近邻算法等不同算法进行分类研究。相对于传统基于无线信号接收信号强度指标的免携带设备行为识别方法，文章提出的方法对不同运动速度等级的识别精度平均提高了 25.1%。

Detection and Recognition of Human Activity Based on Radio-Frequency Signals

The Internet of Things realizes the connection of human and objects. Activity recognition is necessary for the interaction between information sensing devices and human. Currently, vision-based and sensor based methods are widely used, but these methods are limited in many scenes. In this paper, a new radio-frequency-based activity recognition technique was proposed, in which a few communication nodes were deployed in the monitoring area for the device-free activity recognition by analyzing the transmission packet state information. The sequential minimal optimization and K-nearest neighbor algorithms were employed for classification. The classification accuracy of walking speed of the proposed method is improved by 25.1% on average compared to the traditional method based on received signal strength indication.