体域网中一种基于压缩感知的人体动作识别方法

摘要

Energy efficiency is a primary challenge in wireless body sensor networks for the long-term physical movement monitoring. In order to reduce the energy consumption while maintaining the sufficient classification accuracy of the human activity, a compressed classification approach is explored combining classification with data compressing based on sparse representation and compressed sensing. The proposed approach firstly compresses the sensing data by random projection on the sensor nodes, and then recognizes activities on compressed samples after transmitting to the central node by sparse representation, which can reduce the energy transmission of original data. The performance of the method is evaluated on the opened Wearable Action Recognition Database (WARD). Experimental results are validated that the compressed classifier achieves comparable recognition accuracy on the compressed sensing data.%　　能量有效性是无线体域网在面向长时间健康监测应用的首要挑战。该文引入压缩感知和稀疏表示理论同时解决人体活动监测中的动作识别和数据压缩问题，探索在达到一定动作识别率的同时降低传感器节点的能耗。该文提出的压缩分类动作识别方法首先在传感器节点利用随机投影对传感数据进行压缩，传到中心节点后再利用稀疏表示对压缩采样数据进行分类与识别，可减少传感器节点处理、传输原始数据所带来的能耗。在公开的可穿戴式传感器动作识别数据库WARD(Wearable Action Recognition Database)验证算法性能，实验结果表明该动作识别方法能有效地对随机投影后的低维采样数据进行识别，具有与传统识别方法相比拟的动作识别准确率。