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作 者:徐甲栋 陈强[1] 徐一雄 杨文龙 张新宇 梁明远 XU Jiadong;CHEN Qiang;XU Yixiong;YANG Wenlong;ZHANG Xinyu;LIANG Mingyuan(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science Shanghai 201620,China;Shanghai Institute of Space Control Technology,Shanghai 201620,China)
机构地区:[1]上海工程技术大学电子电气工程学院,上海201620 [2]上海航天控制技术研究所,上海201620
出 处:《传感器与微系统》2022年第7期77-80,共4页Transducer and Microsystem Technologies
基 金:国家自然科学基金青年科学基金资助项目(61705127);上海市科技委员会重点项目(18511101600)。
摘 要:为了保障老年人身体健康,设计了一种基于微机电系统(MEMS)传感器可穿戴式的实时跌倒检测系统。系统以微处理器STM32F103VET6为核心,采用了MEMS传感器MPU6050、ATGM332D定位模块和SIM900A数据传输模块采集和传输数据,通过欧拉角旋转矩阵将加速度和角速度数据相融合,降低MPU6050内置陀螺仪漂移误差,得到更准确的姿态角。基于改进的多级阈值跌倒检测算法,提高了跌倒检测的准确率,工程大智能看护微信小程序结合云数据库技术,实现了实时查看老人状态和位置的功能。实验结果表明:该系统的检测准确率为97.75%,能够有效检测到老人的跌倒信息并实时定位报警,通过工程大智能看护微信小程序可及时救助跌倒老人。In order to guarantee health of the elderly,a wearable real-time fall detection system based on MEMS sensor is designed.The system takes the microprocessor STM32F103VET6 as the core,uses MEMS sensor MPU6050,ATGM332D positioning module and SIM900A data transmission module to collect and transmit data.Through Euler angle rotation matrix,the acceleration and angular velocity data are fused to reduce the drift error of MPU6050 built-in gyroscope and obtain more accurate attitude angle.Based on the improved multi-level threshold fall detection algorithm,the accuracy of fall detection is improved.The wechat small program of Engineering University intelligent nursing combined with cloud database technology realizes the function of real-time viewing the status and position of the elderly.The experimental results show that the detection accuracy of the system is 97.75%,which can effectively detect the fall information of the elderly and real-time positioning alarm.With the help of wechat small program of Engineering University intelligent nursing,it can timely rescue the fallen elderly.
分 类 号:TP274.2[自动化与计算机技术—检测技术与自动化装置]
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