Contactless Detection of Heartbeat and Cardiopulmonary Modeling using Vector Analyzer
M. Raja1, S. Dhanasekaran2, C. Bala Subramanian3
1M. Raja, Department of Computer Science and Engineering, Kalaslingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
2Dr. S. Dhanasekaran, Department of Computer Science and Engineering, Kalaslingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
3Mr. C. Bala Subramanian, Department of Computer Science and Engineering, Kalaslingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 16 December 2019 | Manuscript Published on 30 December 2019 | PP: 419-423 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11421292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1142.1292S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a days, Modern world makes it difficult for some individuals to care for their health. Urban air pollution, employment pressure, and an uneven diet increase a person’s likelihood of being infected. In practice, until serious things, some of the infections would not provoke any symptoms. Heart rate (HR) is a measure of physiological activity. This article introduces contactless heartbeat detection and cardiopulmonary modeling. Our suggested microwave system uses a vector network analyzer to demonstrate the potential to detect the heartbeat signal at distinguishable frequency ranges and at distinct output energy concentrations. The model comprising the heartbeat and breathing signals are provided based on variables obtained from actual measurements. To separate the heartbeat and breathing signals, various processing methods are used. For separate signal-to noise ratios, wavelet filters possess greater accuracy over standard filters in order to determine heart rate and heart rate variation.
Keywords: Coronary Heart Disease, Ultra-Wideband (UWB) Radar, Doppler Hypothesis.
Scope of the Article: Data Visualization using IoT