A wireless sensor-based smart-clothing platform for ECG monitoring achieved a signal-to-noise ratio exceeding 37 dB and average QRS sensitivity and positive prediction above 99.5%.
Does a wireless sensor-based smart-clothing platform accurately detect ECG signals and reduce power consumption?
A novel smart-clothing platform for ECG monitoring demonstrates high signal quality, accurate QRS detection, and significant power savings in technical evaluations.
The goal of this study is to use wireless sensor technologies to develop a smart clothes service platform for health monitoring. Our platform consists of smart clothes, a sensor node, a gateway server, and a health cloud. The smart clothes have fabric electrodes to detect electrocardiography (ECG) signals. The sensor node improves the accuracy of QRS complexes detection by morphology analysis and reduces power consumption by the power-saving transmission functionality. The gateway server provides a reconfigurable finite state machine (RFSM) software architecture for abnormal ECG detection to support online updating. Most normal ECG can be filtered out, and the abnormal ECG is further analyzed in the health cloud. Three experiments are conducted to evaluate the platform's performance. The results demonstrate that the signal-to-noise ratio (SNR) of the smart clothes exceeds 37 dB, which is within the "very good signal" interval. The average of the QRS sensitivity and positive prediction is above 99.5%. Power-saving transmission is reduced by nearly 1980 times the power consumption in the best-case analysis.
Wang et al. (Thu,) conducted a other in ECG Monitoring. Wireless Sensor-Based Smart-Clothing Platform was evaluated on Platform performance including signal-to-noise ratio and QRS sensitivity. A wireless sensor-based smart-clothing platform for ECG monitoring achieved a signal-to-noise ratio exceeding 37 dB and average QRS sensitivity and positive prediction above 99.5%.
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