Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors
Zigurds Markovics1, Juris Lauznis2, Matiss Erins3, Olesja Minejeva4, Raivis Kivlenieks5
1Zigurds Markovics, Department of Computer Science and Information Technology, Riga Technical University, Latvia.
2Juris Lauznis, Department of Computer Science and Information Technology, Riga Technical University, Latvia.
3Matiss Erins, Department of Computer Science and Information Technology, Riga Technical University, Latvia.
4Olesja Minejeva, Department of Computer Science and Information Technology, Riga Technical University, Latvia.
5Raivis Kivlenieks, Department of Computer Science and Information Technology, Riga Technical University, Latvia.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 05 May 2019 | PP: 320-324 | Volume-8 Issue-2S2, May 2019 | Retrieval Number: B10670182S219/19©BEIESP
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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: The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method. The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants. There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethy smograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data. The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
Keywords: Heart Rate Variability, Wearable Sensor Biosignals, Photoplethysmography, Sensor Validation.
Scope of the Article: Smart Solutions – Wearable Sensors and Smart Glasses