摘要
针对人体在处于不同状态下呼吸幅度和速率的不同,并综合目前已有的呼吸监测方法,提出了一种基于卡尔曼滤波算法的可穿戴式惯性测量单元的呼吸状态监测方法。首先将两个惯性测量单元固定在人体的前胸与后背,测量人体在不同运动状态下呼吸时胸廓收缩和扩展所引起的加速度与角速度变化,在对前胸、后背惯性测量单元所测量的数据进行坐标变换空间对准后,采用差分模型下的卡尔曼滤波算法解算出运动员单纯呼吸运动状态下的加速度变化曲线,进而得到不同运动状态下运动员的呼吸频率和呼吸深度参数,该参数可作为评价运动员身体素质的重要指标。最后在搭建的可穿戴式惯性测量单元呼吸监测实验平台中进行模型验证,并将人体不同姿态下的实验结果与标准呼吸面罩测量结果比对,准确率均达到90%以上,实现了对运动员呼吸状态的便携式、可穿戴式实时精确测量。
In view of the differences in respiratory amplitudes and rates of human body under different condi- tions,and combined with the existing respiratory monitoring method,a respiratory status monitoring method based on Kalman filtering algorithm is proposed for the wearable inertial measurement unit (IMU).First,two IMUs are fixed at the chest and back of human body to measure the changes of acceleration and angular velocity caused by the contraction and expansion of the thoracic cavity during breathing in different motion states.After implementing the coordinate transformation space alignment for the measured data of the IMUs of the human body's front chest and back,a Kalman filter algorithm in the differential model is used to calculate the acceleration change curve of the athlete in the state of pure breathing movement.Then the breathing frequency and depth parameters of the athlete in different movement states are obtained,which can be used as an important index to evaluate the athlete's physical fitness.Finally,the model verification is performed in the respiratory monitoring experiment platform of wearable IMUs.The experimental results for different postures of the human body are compared with the standard respiratory mask measurement results, which show that the accuracy rate is above 90%,and the proposed method can realize the real-time accurate measurement of athlete's respiratory states with portable and wearable IMUs.
作者
杨海
罗涛
李莉
梁海波
张禾
YANG Hai;LU0Tao;LI Li;LIANG Haibo;ZHANG He(School of Mechatronic Engineering,Southwest Petroleum University,Chengdu 610500,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2018年第5期591-596,共6页
Journal of Chinese Inertial Technology
基金
国家自然科学基金联合基金培育项目(U1610111)
西南石油大学青年教师"过学术关"项目(201799010002)
关键词
呼吸状态监测
惯性测量单元
差分模型
卡尔曼滤波
可穿戴式
respiratory status monitoring
inertial measurement unit
difference model
Kalman filter
wearable
