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Dynamic Detection of PPG(Photo Plethysmography) Signals Using Mems Self Designed Sensors
Objectives: To design Micro Electro Mechanical System (MEMS) based sensor with novel design considerations to detect/ identify PhotoPlethysmography(PPG) signals when the person is in dynamic mode. To simulate the above design using COMSOL Multiphysics and evaluate the results with the existing methodologies. Methods/Statistical Analysis: A MEMS based sensor with a four T-shaped cantilever with middle load hanged structure is designed in COMSOL Multiphysics and after attaining the required resonant frequency of PPG, by using capacitive actuation technique the acquired mechanical signals are evaluated and digitised. Findings: PPG sensors optically recognize changes in tidal volume of blood (i.e., changes in the force of light perceived) in the vascular bed of little scale tissue by reflection from or transmission through the tissue. If the person is in static mode it is very easy to detect blood flow rate. If suppose the person is not in rest, i.e., may be dynamic we will observe many variations in blood flow rate. So it is very difficult to estimate the blood flow rate. This Patch type device easily measures the blood flow rate. Application/Improvements: Mainly these type of devices are useful to detect variations in blood flow rate in human whether in static condition or in dynamic condition. In future we are planning to develop implantable sensors with reporting system to doctor in particular intervals of time. This may be helpful to doctors to cater the needs of human society at right time whenever it is needed
Capacitive Actuation, Dynamic, Implantable, MEMS, PPG, Static.
- Khan E. A robust heart rate monitoring scheme using photo plethysmographic signals corrupted by intense motion artifacts.IEEE Transactions on Biomedical Engineering. 2015; 63(3): 550–62. Crossref PMid:26276979
- Grace kanmani P. Lab view based abnormal Muscular movement and fall detection using MEMS accelerometer during the occurrence of seizure. Indian Journal of Science and Technology. 2014; 7(10): 1625–31.
- Prince Nagpal, Manish Mehta, Kamaljeet Rangra, Ravinder Aggarwal. Optimization of capacitive MEMS pressure sensor for RF telemetry. International Journal of Scientific and Engineering Research. 2011; 2(10): 1–4.
- Lazaro J. Pulse rate variability analysis for discrimination of sleep-apnea-related decreases in the amplitude fluctuations of pulse photoplethysmographic signal in children.IEEE Journal of Biomedical and Health Informatics.2014;18(1):240–46. Crossref PMid:24403422
- Po LM, Xu X, Feng L, Li Y. Frame adaptive ROI for photo plethysmography signal extraction from fingertip video captured by smartphone. IEEE International Symposium on Circuits and Systems, Lisbon:2015.p.1634–37. Crossref
- Wannenburg J, Malekian R. Body sensor network for mobile health monitoring, a diagnosis and anticipatingsystem. IEEE Sensors Journal. 2015 Aug; 15(12): 6839–52. Crossref
- Wong MYM, Leung HK, Pickwell-MacPherson E, Gu W B.Contactless recording of photoplethysmogram on a sleeping bed. Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBC.2009.p. 907–10.
- Fezri Aziz SS, Hishamuddin NAM, Saad FS . Wearable heart rate monitor using photo plethysmography for motion.IEEE Conference on Biomedical Engineering and Sciences (IECBES), 2014. p. 1015–18.
- Zhang Z. Heart rate monitoring from wrist-type photo plythesmographic (PPG) signals during intensive physical exercise.IEEE Global Conference on Signal and Information Processing (Global SIP), 2014.p.698–702. Crossref
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