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.
Due to the limited resources of wireless sensor network, low efficiency of real-time communication scheduling, poor safety defects, and so forth, a queuing performance evaluation approach based on regular expression match is proposed, which is a method that consists of matching preprocessing phase, validation phase, and queuing model of performance evaluation phase. Firstly, the subset of related sequence is generated in preprocessing phase, guiding the validation phase distributed matching. Secondly, in the validation phase, the subset of features clustering, the compressed matching table is more convenient for distributed parallel matching. Finally, based on the queuing model, the sensor networks of task scheduling dynamic performance are evaluated. Experiments show that our approach ensures accurate matching and computational efficiency of more than 70%; it not only effectively detects data packets and access control, but also uses queuing method to determine the parameters of task scheduling in wireless sensor networks. The method for medium scale or large scale distributed wireless node has a good applicability.
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