CAJ | 학술논문

基于人体传感器网络（BSN ）对人体动作的识别，在远程医疗服务中具有重要应用。搭建了一个基于BSN的人体动作监测平台，实验中通过固定在人体腰部和大腿上的两个加速度传感器节点，来采集人体日常生活中的7个动作所产生的加速度信号。特征提取包含传感器节点在3个轴上信号的时域和频域信息，并采用神经网络和分层的方法融合信息对7个动作进行分类和识别。实验结果表明，应用所搭建的BSN平台和识别方法，采用两个传感器节点识别人体日常生活中的7个动作具有很高的正确率。
기우인체전감기망락（BSN ）대인체동작적식별，재원정의료복무중구유중요응용。탑건료일개기우BSN적인체동작감측평태，실험중통과고정재인체요부화대퇴상적량개가속도전감기절점，래채집인체일상생활중적7개동작소산생적가속도신호。특정제취포함전감기절점재3개축상신호적시역화빈역신식，병채용신경망락화분층적방법융합신식대7개동작진행분류화식별。실험결과표명，응용소탑건적BSN평태화식별방법，채용량개전감기절점식별인체일상생활중적7개동작구유흔고적정학솔。
Human activity recognition based on body sensor networks (BSN) has broad application in remote medical service .A monitoring platform based on BSN is established ,and acceleration signals of 7 human activities are collected from two sensor nodes mounted on the waist and the thigh of the user .The extractions of features include time domain and frequency domain information of tri-axis accelerometer and then 7 human daily activities are classified by using neural network and hierarchical method . The experimental results show that the established monitoring platform and proposed recognition method can achieve satisfactory performance for 7 human daily activity recognition .