In order to detect Electrocardiograph (E.C.G.) signals in people & x2019;s daily life accurately, in this study, a wearable real-time dynamic E.C.G. signal detection system based on the Internet of things technology was designed and implemented. Under the STM32 WeChat processor, a flexible fabric was used as the base of the sensor. It can process the collected E.C.G. signal through amplification and filtering of signal conditioning module, so as to satisfy the conversion of A/D. The E.C.G. signal acquisition front end and other hardware and software were designed based on AD8232 chip. And then an adaptive filter was designed based on standardized LMS algorithm (N.L.M.S.). E.C.G. signal in MIT-BIH database was used to detect the accuracy of R wave detection. In order to detect the restraining effect of baseline drift and motion artifact in E.C.G. after filtering, the accuracy of R wave detection in different movements of healthy personnel was tested. The results showed that after using the N.L.M.S. algorithm & x2019;s adaptive filter to detect E.C.G. signals in MIT-BIH database, the accuracy (De & x0025;), sensitivity (Se & x0025;), and specificity (Sp & x0025;) calculated by the R-wave were all above 99 & x0025;. It was then worn on the experimenter & x2019;s chest and the E.C.G. signals were detected while experimenters sat still. It is found that it can restrain baseline drift in E.C.G. signal acquisition. In addition, when the experimenter did static standing, walking slowly, squatting and chest expansion, the detection rate of R wave was above 95 & x0025;. Therefore, the designed system can monitor E.C.G. signals quickly and accurately.
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