Quantification of Blood Flow Velocity Using Color Sensing

Author

Aditya Deepak Sanghani, California Polytechnic State University, San Luis ObispoFollow

DOI: https://doi.org/10.15368/theses.2015.145
Available at: https://digitalcommons.calpoly.edu/theses/1490

Date of Award

10-2015

Degree Name

MS in Electrical Engineering

Department/Program

Electrical Engineering

Advisor

Tina Smilkstein

Abstract

Blood flow velocity is an important parameter that can give information on several pathologies including atherosclerosis, glaucoma, Raynaud’s phenomenon, and ischemic stroke [2,5,6,10]. Present techniques of measuring blood flow velocity involve expensive procedures such as Doppler echocardiography, Doppler ultrasound, and magnetic resonance imaging [11,12]. They cost from $8500-$20000. It is desired to find a low-cost yet equally effective solution for measuring blood flow velocity. This thesis has a goal of creating a proof of concept device for measuring blood flow velocity.

Finger blood flow velocity is investigated in this project. The close proximity to the skin of the finger’s arteries makes it a practical selection. A Red Green Blue (RGB) color sensor is integrated with an Arduino Uno microcontroller to analyze color on skin. The initial analysis involved utilization of red RGB values to measure heart rate; this was performed to validate the sensor. This test achieved similar results to an experimental control as the measurements had error ranging from 0% to 6.67%.

The main analysis was to measure blood flow velocity using 2 RGB color sensors. The range of velocity found was 5.20cm/s to 12.22cm/s with an average of 7.44cm/s. This compared well with the ranges found in published data that varied from 4cm/s to 19cm/s. However, there is an error associated with the device that affects the accuracy of the results. The apparatus has the limitation of collecting data between sensors every 102-107ms, so there is a maximum error of 107ms. The average finger blood flow velocity of 7.44cm/s may actually be between 6.17cm/s and 9.39cm/s due to the sampling error. In addition, mean squared error analysis found that the most likely time difference between pulses among those found is 739ms, which corresponds to 5.21cm/s.

Although there is error in the system, the tests for heart rate along with the obtained range and average for finger blood velocity data provided a method for analyzing blood flow velocity. Finger blood velocity was examined in a much more economical manner than its traditional methods that cost between $8500-$20000. The cost for this entire thesis was $99.66, which is a maximum of 1.17% of the cost.