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Spectrum analysis of physiological signals of human activities

This paper investigates the impact of physiological maneuvers on the frequency component of photoplethysmograpy signal. Here, we have taken four different physiological states of sitting, standing, jogging and laying. Two groups of 5 to 10 healthy volunteers males and females are formed. The PPG sig...

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Main Authors: Kazmi, Syed Absar, Khan, Sheroz, Khalifa, Othman Omran, Shah, Mansoor Hussain
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2015
Subjects:
T10.5 Communication of technical information
Online Access:http://irep.iium.edu.my/50506/4/50506.pdf
http://irep.iium.edu.my/50506/7/50506-Spectrum%20Analysis%20of%20Physiological%20Signals%20of_SCOPUS.pdf
http://irep.iium.edu.my/50506/
http://dx.doi.org/10.1109/ICET.2015.7389197
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spelling my.iium.irep.505062017-10-19T01:39:46Z http://irep.iium.edu.my/50506/ Spectrum analysis of physiological signals of human activities Kazmi, Syed Absar Khan, Sheroz Khalifa, Othman Omran Shah, Mansoor Hussain T10.5 Communication of technical information This paper investigates the impact of physiological maneuvers on the frequency component of photoplethysmograpy signal. Here, we have taken four different physiological states of sitting, standing, jogging and laying. Two groups of 5 to 10 healthy volunteers males and females are formed. The PPG signal acquisition is performed by Easy Pulse analyzer sensor module. Each sample for each state was taken for one-minute duration at stopwatch keeping the consolidated state of volunteer prior to fetching of PPG signal. The Easy pulse analyzer module implicates the pulse oximetry working principle and get the signal from the finger tip of subjects, which determines the oxygen saturation in blood and passes the signal by the optical sensor via a sequential high and low pass op-amp filters and ultimately produces the conditioned PPG signal. The interfacing between the easy pulse analyzer and computing machine was done with the help of Arduino processing board. The Kubios HRV software was utilized in order to execute and manipulate PPG data (numerical values) samples in required format. The report sheet was generated which pertains the frequency and time domain paradigms and was analyzed for respective PPG signal according to the physiological conditions. The results for each data set among four physical states define the co-relation between the physical state and corresponding PPG signal. Moreover, the variation in frequency components is observed during the change in physiological condition. IEEE 2015-12 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/50506/4/50506.pdf application/pdf en http://irep.iium.edu.my/50506/7/50506-Spectrum%20Analysis%20of%20Physiological%20Signals%20of_SCOPUS.pdf Kazmi, Syed Absar and Khan, Sheroz and Khalifa, Othman Omran and Shah, Mansoor Hussain (2015) Spectrum analysis of physiological signals of human activities. In: 2015 International Conference on Emerging Technologies (ICET), 19th-20th Dec. 2015, Peshawar, Pakistan. http://dx.doi.org/10.1109/ICET.2015.7389197 doi:10.1109/ICET.2015.7389197
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T10.5 Communication of technical information
spellingShingle T10.5 Communication of technical information
Kazmi, Syed Absar
Khan, Sheroz
Khalifa, Othman Omran
Shah, Mansoor Hussain
Spectrum analysis of physiological signals of human activities
description This paper investigates the impact of physiological maneuvers on the frequency component of photoplethysmograpy signal. Here, we have taken four different physiological states of sitting, standing, jogging and laying. Two groups of 5 to 10 healthy volunteers males and females are formed. The PPG signal acquisition is performed by Easy Pulse analyzer sensor module. Each sample for each state was taken for one-minute duration at stopwatch keeping the consolidated state of volunteer prior to fetching of PPG signal. The Easy pulse analyzer module implicates the pulse oximetry working principle and get the signal from the finger tip of subjects, which determines the oxygen saturation in blood and passes the signal by the optical sensor via a sequential high and low pass op-amp filters and ultimately produces the conditioned PPG signal. The interfacing between the easy pulse analyzer and computing machine was done with the help of Arduino processing board. The Kubios HRV software was utilized in order to execute and manipulate PPG data (numerical values) samples in required format. The report sheet was generated which pertains the frequency and time domain paradigms and was analyzed for respective PPG signal according to the physiological conditions. The results for each data set among four physical states define the co-relation between the physical state and corresponding PPG signal. Moreover, the variation in frequency components is observed during the change in physiological condition.
format Conference or Workshop Item
author Kazmi, Syed Absar
Khan, Sheroz
Khalifa, Othman Omran
Shah, Mansoor Hussain
author_facet Kazmi, Syed Absar
Khan, Sheroz
Khalifa, Othman Omran
Shah, Mansoor Hussain
author_sort Kazmi, Syed Absar
title Spectrum analysis of physiological signals of human activities
title_short Spectrum analysis of physiological signals of human activities
title_full Spectrum analysis of physiological signals of human activities
title_fullStr Spectrum analysis of physiological signals of human activities
title_full_unstemmed Spectrum analysis of physiological signals of human activities
title_sort spectrum analysis of physiological signals of human activities
publisher IEEE
publishDate 2015
url http://irep.iium.edu.my/50506/4/50506.pdf
http://irep.iium.edu.my/50506/7/50506-Spectrum%20Analysis%20of%20Physiological%20Signals%20of_SCOPUS.pdf
http://irep.iium.edu.my/50506/
http://dx.doi.org/10.1109/ICET.2015.7389197
_version_ 1643613744680402944
score 13.211869

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