Sensors and Signal Processing Methods for a Wearable Physiological Parameters Monitoring System
The present paper presents some of the specific aspects of the development of a smartphone-based physiological parameters monitoring system intended for occupational safety applications. The system consists of a comfortable garment with integrated wireless sensors and a personal smartphone connected to the sensors via Bluetooth. A smartphone is used for data gathering and processing, heart and respiratory rate evaluation and information transmission. A reliability of the physiological parameter values obtained by the system highly depends on the signal processing and evaluation methods applied. The methods must ensure robustness for human motion artefacts in the signal and other noise prevailing in industrial environment. The monitoring system presented here combines relatively simple data gathering tools with certain novel digital signal processing algorithms that are useful in solving reliability problems caused by human motion artefacts and electromagnetic interference. The proposed method for the evaluation of respiratory and heart rate and the developed algorithms are based on the detection of distinctive peaks in respiratory and electrocardiogram signals using digital filtering and a moving difference window with an adaptive length. A median threshold criterion is applied to reject the false peaks in those signals caused by noise. The sensors and data processing algorithms were tested experimentally and the results were verified with a portable medical patient monitor MEC-1000. The differences between the values of corresponding parameters were insignificant, which means that the indications of the wearable monitoring system could be trustworthy and practically applicable.
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