摘要
为解决国内电动养老床功能单一、技术落后等问题,提出一种基于健康数据挖掘的智能养老床系统.首先通过非接触式生命体征传感器采集人体心跳和呼吸信号,压力传感器采集体重信号,基于无线模块将采集的生理参数发送给远程服务器.然后对比分类与聚类方法在心率、呼吸率数据挖掘中的效率,基于k-Means聚类实现健康数据挖掘算法.最后通过聚类算法多次迭代,优化系统参数.实验证明本文提出的基于健康数据挖掘的智能养老床系统不仅能很好地解决传统电动养老床功能单一、技术落后的问题,还能为后续健康护理等提供准确的健康数据保障.
In order to solve the problems of being functionally simple and technologically outdated of domestic electric homecare bed,a smart homecare bed based on health data mining is proposed.Firstly,the human heartbeat and the breath are collected by the non-contact vital sign sensor,and the weight is collected by the pressure sensor.The collected physiological parameters are transmitted to the remote server by wireless module.Then,by comparing the efficiency of classification and clustering methods in data mining of heart rate and respiratory rate,k-Means clustering algorithm is adopted and implemented for health data mining.Finally,the system parameters are optimized by multiple iterations through the clustering algorithm.The experiment proves that the smart homecare bed system based on health data mining proposed in this paper can not only solve the problems existing in the traditional electric homecare bed,but also provide accurate health data guarantee for subsequent health care service.
作者
杨海宇
王志武
沈惠吉
YANG Haiyu;WANG Zhiwu;SHEN Huiji(School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University,Shanghai 201100,China;Zhejiang Jiecang Linear Actuator Technology Co.,Ltd,Shaoxing 312500,China;Ningbo Healthkey Motion Technology Co.,Ltd,Ningbo 315300,China)
出处
《宁波大学学报（理工版）》
CAS
2019年第6期56-60,共5页
Journal of Ningbo University：Natural Science and Engineering Edition
关键词
电动养老床
生理数据采集
数据挖掘
聚类
electric homecare bed
physiological data collection
data mining
clustering
