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A portable motion tolerant blood pressure monitor

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Project Overview

Blood pressure is a critical measurement to assess an individual's vascular health. The most commonly used instrument to measure blood pressure is a sphygmomanometer. This device measures blood pressure by attaching an inflating and deflating a cuff around the patients upper arm and having a professional listen with a stethoscope. Electrical sphygmomanometers are also commonly used to measure a patient's blood pressure; however, they are also bulky and are ineffective in providing a healthcare professional with regular updates on a patients instantaneous blood pressure. Also, this method is not feasible in loud environments or when the patient is moving. The purpose of our project is to create a blood pressure sensor that can accurately and regularly measure a patient's blood pressure in loud and motion-heavy environments while reducing the effective size of the product. This design would enable healthcare professionals to be able to acquire a patient's blood pressure instantly and frequently without having to work around a sphygmomanometer, which would be crucial for emergency medical transports of patients with massive blood loss (hypotension) or cardiac arrest/ stroke (hypertension). It could also be used in the wearables industry or for military purposes. Last semesters main goal was to be able to identify blood pressure in a variety of different noisy environments. The ability to recognize a signal in noisy environments would set this instrument apart from its competitors. Last semester was mostly focused on testing while still, with sound and with motion. Standard deviation of these tests increased from former to latter. The control data exhibited similar numbers to the stethoscope. The current semester has been focused on converting a MATLAB algorithm that was post-processed on a computer to the raspberry pi on the device. In addition, work was done on calibration of the microphones and creating an automated pump. The device now pumps, provides a blood pressure and a heart rate as a standalone device. Although the filtering phase of the raspberry pi algorithm causes a high variance for the systolic blood pressure and will have to be altered next semester.

Team Picture

Team members from left to right: Jacob Mundale, Michael Giordano, Patrick Kasl, Owen Seymour
Team members from left to right: Jacob Mundale, Michael Giordano, Patrick Kasl, Owen Seymour

Contact Information

Team Members

  • Jacob Mundale - Team Leader & BSAC
  • Owen Seymour - Communicator
  • Michael Giordano - BWIG
  • Patrick Kasl - BPAG

Advisor and Client

  • Prof. Amit Nimunkar - Advisor
  • Dr. Dennis Bahr - Client

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