基于无线传感器网络的室内人体日常动作识别 Indoor Human Activity Recognition Using Wireless Sensor Networks

Abstract

实现室内人体定位跟踪与动作智能识别在人口老龄化社会具有重要的现实意义。本文提出了一种通过构造无线传感器网络(Wireless sensor network, WSN)同时解决这两个相关问题的方法。在WSN中，每个热释电红外(Pyroelectric Infrared, PIR)传感器的视场(Field of View, FOV)通过两个自由度(Degrees of freedom, DOF)分割来实现调制，通过位置信息的编码解码实现了人体目标的粗略定位。通过相邻两个传感器节点的数据融合扩大了监测区域，同时提高了人体定位的精确度。动作的持续时间是动作识别的一个关键特征，为此本文构造了一个两层的随机森林(Random Forest, RF)分类器。第一层随机森林用于识别每个数据帧的动作类型，第二层随机森林利用相同动作的持续时间作为有效的特征判断最终的动作类型。实验在真实的室内环境中进行，5种日常动作的10折交叉验证平均准确率高于93%。实验结果表明本文提出的方法可以同时有效地实现人体定位跟踪与日常动作识别。 Human locomotion tracking and activity recognition in the indoor environment are fundamental problems for healthy aging. In this paper, we propose a method to deal with these two coherent problems simultaneously by constructing a wireless sensor network (WSN). In the WSN, the Field of View (FOV) of each Pyroelectric Infrared (PIR) sensor is modulated by two degrees of freedom (DOF) segmentation, which provides coarse location information of the human target. Data fusion of the adjacent sensor nodes enlarges the monitored region and improves the human localization accuracy. To incorporate the activity lasting time as a crucial cue for activity recognition, we build a two-layer Random Forest (RF) classifier. The first layer is utilized to label the activity type for each data frame, and the second layer will utilize the lasting time of the same activity as a useful feature for the final activity classification. We conducted experiments in a mock apartment, and the average mean accuracy for 10-fold cross validation of 5 kinds of daily activities is above 93%. The encouraging results show that our method can achieve human tracking and daily activity recognition simultaneously and effectively.