基于 WiFi 背景噪音的被动式人体行为识别研究

摘要

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 experiments .%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。