| 引用本文: | 徐礼胜,靳雁冰,王琦文,李锡勇,印重.多传感器融合的穿戴式心率监测系统[J].哈尔滨工业大学学报,2015,47(5):97.DOI:10.11918/j.issn.0367-6234.2015.05.017 |
| XU Lisheng,JIN Yanbing,WANG Qiwen,LI Xiyong,YIN Zhong.Multi-sensor fusion for wearable heart rate monitoring system[J].Journal of Harbin Institute of Technology,2015,47(5):97.DOI:10.11918/j.issn.0367-6234.2015.05.017 |
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| 多传感器融合的穿戴式心率监测系统 |
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徐礼胜1,2, 靳雁冰1, 王琦文1, 李锡勇1, 印重3
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(1.东北大学 中荷生物医学与信息工程学院, 110819 沈阳; 2. 医学影像计算教育部重点实验室(东北大学), 110004 沈阳; 3.沈阳药科大学 医疗器械学院, 110016 沈阳)
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| 摘要: |
| 为提高日常行为下心率监测准确率,用多传感器融合的方法分别融合与生物电生理和生物机械力密切相关的心电、脉搏波信号,实现基于Android平台的高可靠、穿戴式心率监测系统． 使用本系统和ST-1212心电工作站进行了18例日常行为下不同动作不同强度的同步采集和分析实验． 通过分析信号时域特征得到反映信号质量高低的信号质量指数,根据质量指数自适应调节卡尔曼滤波器对两路信号获得的心率做最优估计,最后通过卡尔曼滤波残差调节权重得到融合心率． 结果表明, 融合心率相比单从心电或者脉搏波信号所得心率准确度提高46%以上。该系统通过多传感器融合的方式能有效降低干扰对心率估计的影响,可相对长时间地进行心率低负荷连续监测． |
| 关键词: 心率 多传感器融合 Android平台 穿戴式 信号质量指数 卡尔曼滤波器 |
| DOI：10.11918/j.issn.0367-6234.2015.05.017 |
| 分类号:R318.6 |
| 基金项目:国家自然科学基金 (5,8);教育部博士点基金 (20110042120037);中央高校基本科研业务费 (N1102190017). |
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| Multi-sensor fusion for wearable heart rate monitoring system |
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XU Lisheng1,2, JIN Yanbing1, WANG Qiwen1, LI Xiyong1, YIN Zhong3
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(1. School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, 110819 Shenyang, China; 2. Key Laboratory of Medical Image Computing of Ministry of Education(Northeastern University), 110004 Shenyang, China; 3. School of Medical Devices, Shenyang Pharmaceutical University, 110016 Shenyang, China)
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| Abstract: |
| To improve the accuracy of heart rate (HR) in daily behaviors, multi-sensor fusion method was used in this paper to fuse ECG and pulse wave (PW)whichis closely related to biological electrophysiology and biomechanics, respectively. And a wearable heart rate monitoring system with high reliability based on Android platform was achieved. The proposed system and ST-1212 ECG workstation were used for 18 cases simultaneousexperiment of different motion intensity in daily behaviors. Signal quality indices (SQI) that reflect the level of signal quality were calculated by analyzing the signal characteristics in time domain, and then Kalman-Filter (KF) was adaptively regulated to make the optimal estimation of the HR derivedfrom the dual-channel signal according to SQI, and finally KF residuals were used to adjust the weights to get the fused HR.The results indicate that the fused HR can improve the accuracy more than 46% than those derived from ECG or PW directly. The system can effectively reduce the artifact on HR estimationby using multi-sensor fusion method, thus it can be used for continuous monitoring of HR with low physiological and mental burden for a relatively long time. |
| Key words: heart rate multi-sensor fusion Android platform wearable signal quality indices Kalman filter |
