摘要
为解决日益突出的老年人口居家养老和看护问题,设计了一种基于加速度传感器和高度传感器的可穿戴系统。通过将多传感器参数与卡尔曼滤波进行融合,结合四元数对人体姿态进行识别,实现跌倒检测,并实时监控异常行为。实验结果表明,基于多传感器参数融合的卡尔曼滤波和Mahony姿态角的解析算法,在测试样本中检测跌倒的准确率为95%,具有检测精度高、计算量小、检测方便等特点,能更好解决居家养老和看护问题。
In order to solve the increasingly prominent problems of home-based elderly care and care for the elderly population,a wearable system based on acceleration sensor and height sensor is designed.By fusing multi-sensor parameters with Kalman filter and combining quaternion to recognize human posture,fall detection is realized and abnormal behavior is monitored in real time.The experimental results show that the Kalman filter based on multi-sensor parameter fusion and the analytical algorithm of Mahony attitude angle can detect falls in the test samples with an accuracy of 95%.It has the characteristics of high detection accuracy,small amount of calculation and convenient detection,and can better solve the problems of home care and nursing.
作者
李小奇
郑建立
LI Xiao-qi;ZHENG Jian-li(School of Medical Instrument&Food Engineering,University of Shanghai for Science&Technology,Shanghai 200093,China)
出处
《软件导刊》
2022年第6期125-128,共4页
Software Guide
基金
国家重点研发计划项目子课题(2019YFC2005802)。
关键词
可穿戴
多传感器
卡尔曼融合
四元数
姿态角
wearable
multi-sensors
Kalman fusion
quaternion
attitude angles
