基于WiFi背景噪音的被动式人体行为识别研究

Research on passive human activity recognition using WiFi ambient signals

利用WiFi背景噪音,传统K-NN和Bagging算法可有效识别较少人体行为,但对较多状态:无人、走、坐、站、睡、跌倒、跑,实验发现,单纯使用K-NN和Bagging算法分类效果并不理想,故设计了一种新的融合算法.实验结果证实,融合算法相较于K-NN和Bagging算法可以大幅提高识别准确率,将新算法应用于多人混合状态识别也取得较好的识别准确率.

Although traditional k-nearest neighbor （K-NN） and Bagging can recognize effectively less human activities using WiFi ambient signal, recognition by either alone of the seven states, namely, empty, walking, sitting, standing, sleeping, falling and running, is not ideal. To improve recognition rates, a new algorithm, fusion algorithm, was designed. It significantly outperforms K-NN and Bagging in terms of recognition ratios in both single-subject and multi-subject exoeriments.