物联技术视角下的医院妇产科护理模型研究

Research on Nursing Model of Obstetrics and Gynecology in Hospital under the Perspective of IoT

为了缓解护理人员紧缺与护理工作量增加的问题,给产妇提供更加及时、科学、有效的服务,将物联网技术应用于产妇护理工作。通过智能传感器采集产妇的心率、血压等参数,应用粒子群算法优化极限学习机模型对病患的异常身体状态进行识别和预警。通过传感器网络采集身体信号,由无线网络传输至系统服务器进行数据分析,实现远程的无人护理和监控。经过2个月的实验监测,所提出的方法能有效地识别产妇的异常状态,对比其他算法准确率提升了约8%,且系统具有较高的实时性、灵活性和智能性。

In order to alleviate the tension of nursing staff and the increase of nursing work,and provide more timely,scientific and effective services for puerpera,the Internet of Things technology is applied to puerpera nursing work,and collects puerpera's heart rate,blood pressure and other parameters through intelligent sensors.Particle swarm optimization(PSO)is used to optimize the extreme learning machine(ELM)model for abnormal body state recognition and early warning.Body signals are collected through the sensor network,and transferred to the system server for data processing and analysis through the wireless network.Remote unattended care and monitoring are realized.After two months of experimental monitoring,the results show that the method proposed can effectively identify the abnormal state of the maternal,and the accuracy rate of human health monitoring system is improved by 8%.At the same time,the system has high real-time,flexibility and intelligence.