老年患者跌倒检测系统的设计与实现
Design and implementation of a fall detection system for elderly patients
张敏 1张欢 1史晓娟 1梁卓文 1张娜1
作者信息
- 1. 空军军医大学西京医院骨科 西安 710032
- 折叠
摘要
目的:设计老年患者跌倒检测系统用以解决老年患者因意外跌倒而未能被及时发现的问题,提高医疗护理效率.方法:基于实时流传输协议(RTSP),结合YOLOv5和Kalman算法,采用Vue及Flask等技术设计老年患者跌倒检测系统,搭建可视化后台系统管理,通过多路视频流综合处理为医疗工作者提供统一管理平台,实现人体跌倒行为的自主检测与报警.选取2020-2022年在空军军医大学西京医院进行跌倒检测的30名健康志愿者,根据模拟行为类别将其分为正常行走组、蹲起组和跌倒组,每组10名,采用检测准确率和检测速度两项评价指标对跌倒检测性能进行评估,验证判断老年患者跌倒检测系统能否达到及时准确的跌倒检测与报警要求.结果:正常行走组、蹲起组和跌倒组的总体跌倒检测速率可达29帧/s,准确率可达95.24%,系统能够及时响应跌倒警报.结论:老年患者跌倒检测系统可辅助医护工作者及时发现并处理跌倒行为的发生,提升老年患者跌倒检测效率,能够满足老年患者跌倒行为的实时检测和报警.
Abstract
Objective:To design a fall detection system for elderly patients to solve the problem of elderly patients failing to detect accidental falls in time and to improve the efficiency of medical care.Methods:Based on real-time stream transmission protocol(RTSP),combined with YOLOv5 and Kalman algorithms,a fall detection system for elderly patients was designed by using Vue and Flask technologies.A visual background system management was established,and a unified management platform was provided for medical staff through comprehensive processing of multiple video streams to realize the autonomous detection and alarm of human fall behavior.30 healthy volunteers who underwent fall testing at Xijing Hospital of Air Force Medical University in 2020 to 2022 were selected and divided into normal walking group,squatting group and falling group according to the simulated behavioural categories,with 10 in each group.The fall detection performance was evaluated using two evaluation indicators:detection accuracy and detection speed to verify and determine whether the fall detection system for elderly patients can meet the requirements of timely and accurate fall detection and alarm.Results:The overall fall detection rate of the normal walking group,the squatting group and the falling group can reach 29 frames per second,and the accuracy rate can reach 95.24%.and the system can respond to the fall alarm in time.Conclusion:The fall detection system for elderly patients can assist medical staff to promptly detect and deal with the occurrence of falls,improve the efficiency of fall detection for elderly patients,and meet the real-time detection and alarm of fall behavior for elderly patients.
关键词
机器视觉/医疗护理/跌倒检测/YOLOv5算法Key words
Machine vision/Medical care/Fall detection/YOLOv5 algorithm引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据