Date of Award

2020

Document Type

Thesis

Degree Name

Master of Science in Engineering (MSE)

Department

Electrical and Computer Engineering

Committee Chair

Emil Jovanov

Committee Member

Aleksandar Milenkovic

Committee Member

Karen Frith

Subject(s)

Body area networks (Electronics), Equilibrium (Physiology)--Testing, Internet of things

Abstract

In an aging society with increased lifespans, falls represent a serious risk for older adults. Balance is a key indicator of mobility, stability, and risk of falls. Wireless body area networks (WBAN) provide opportunity for unobtrusive monitoring and assessment of balance impairments and mobility. This thesis presents an implementation of WBAN system for monitoring of balance in older adults in smart homes. The system features two wireless inertial sensor nodes, a smart watch, an always-on home server, and an Android smartphone application. The sensors are placed on the user’s lower back and forehead. We designed and implemented custom smart inertial sensors that synchronize with the connected home server running on a Raspberry Pi 3B Linux controller. We implemented a smartphone application to automate 4 stage balance test, recommended by the CDC. The application initiates the balance test, while server synchronizes sensors, acquires and processes data from the sensors, and collects all records. The user subsequently receives a test summary, while raw data is stored on the home server for additional post-processing by the smartphone application using Python scripts.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.