Open Access

ARTICLE

Using Mobile Technology to Construct a Network Medical Health Care System

Sung-Jung Hsiao¹, Wen-Tsai Sung^2,*

1 Department of Information Technology, Takming University of Science and Technology, Taipei City, 11451, Taiwan
2 Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan

* Corresponding Author: Wen-Tsai Sung. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 729-748. https://doi.org/10.32604/iasc.2022.020332

Received 19 May 2021; Accepted 20 June 2021; Issue published 22 September 2021

Abstract

In this study, a multisensory physiological measurement system was built with wireless transmission technology, using a DSPIC30F4011 as the master control center and equipped with physiological signal acquisition modules such as an electrocardiogram module, blood pressure module, blood oxygen concentration module, and respiratory rate module. The physiological data were transmitted wirelessly to Android-based mobile applications via the TCP/IP or Bluetooth serial ports of Wi-Fi. The Android applications displayed the acquired physiological signals in real time and performed a preliminary abnormity diagnosis based on the measured physiological data and built-in index diagnostic data provided by doctors, such as blood oxygen concentration, systolic pressure, and diastolic pressure. In addition, in this study, the R waves, which are the highest peaks of the PQRST waves in electrocardiograms, were analyzed and detected using the heart rate variability (HRV) for time-frequency analysis and calculation of the RR interval time series. In this study, the heart rate data in the same age group were collected, and the optimal value of the standard deviation of normal to normal (SDNN) during the time domain of a normal heartbeat was found using the particle swarm optimization (PSO) algorithm and set as the risk level of the SDNN time domain analysis. A spectral analysis on the activity of the autonomic nervous system (ANS) was performed and the preliminary analysis results were displayed on the Android handheld devices for comprehensive physiological data analysis and HRV time-frequency analysis. Healthcare needs are distributed at all levels, therefore user-friendly software interfaces have been written to meet the healthcare needs at all ages.

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Keywords

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