基于WiFi信号的老年人家居行为识别算法

Human activity recognition algorithm for elderly home based on WiFi signal

针对老年人家居行为识别中的隐私保护、跌倒检测和识别率低的问题,本文提出了一种新的基于WiFi信号的人体行为识别算法。首先,在模拟家居环境中自主采集了10种老年人日常行为(喝水、跌倒、坐-躺下等);然后对提取到的WiFi信道状态信息用巴特沃斯滤波器降噪,并使用主成分分析方法数据降维;最后将处理后有清晰特征的CSI信号输入到基于注意力的双向长短时记忆模型用于行为分类,高效的双向结构和注意力机制不仅产生了信息更丰富的特征,还提高了行为识别的泛化性能。实验结果表明,与一些基准方法相比,本文算法在公共数据集和自主采集的数据集上都能实现对所有行为的最佳识别性能,准确率分别为98%和96%。

Aiming at the problems of privacy protection,fall detection and low recognition rate in home behavior recognition of the elderly,a new human behavior recognition algorithm based on WiFi signal is proposed in this paper.Firstly,10 kinds of daily life behaviors of the elderly(drinking water,falling,sitting down,etc.)were collected in the simulated home environment;Then,the extracted WiFi channel state information is denoised by Butterworth filter,and the dimension is reduced by principal component analysis;Finally,the processed CSI signals with clear features are input into the attention based bidirectional long short-term memory model for behavior classification.The efficient bidirectional structure and attention mechanism not only produce more informative features,but also improve the generalization performance of behavior recognition;Experimental results show that,compared with some benchmark methods,the proposed algorithm can achieve the best recognition performance for all activities on both public data sets and self-collected data sets,and the accuracy rates are 98%and 96%respectively.