Luo Xiaomu, Guan Qiuju, Tan Huoyuan, Gao Liwen, Wang Zhengfei, Luo Xiaoyan
School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou 510000, China.
College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture Engineering, Guangzhou 510000, China.
Sensors (Basel). 2017 Jul 29;17(8):1738. doi: 10.3390/s17081738.
Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system.
室内人体跟踪与活动识别是环境辅助生活中的基本但又相互关联的问题。在本文中，我们提出一种方法来同时解决这两个关键问题。我们构建了一个无线传感器网络（WSN），WSN内的传感器节点由热释电红外（PIR）传感器阵列组成。为了捕获人体目标的时空信息，每个PIR传感器的视场（FOV）通过掩码进行调制。利用一种改进的局部滤波算法来解码人体目标的位置。为了利用位置与活动之间的协同作用，我们设计了一个两层随机森林（RF）分类器。第一层的初始活动识别结果通过第二层RF结合各种有效特征进行细化。我们在一个模拟公寓中进行了实验。我们系统的平均定位误差约为0.85米。对于五种日常活动，10折交叉验证的平均准确率高于92%。这些令人鼓舞的结果表明了我们系统的有效性。
