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。%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.
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