Date of Award
Master of Science in Electrical Engineering (MSEE)
Technology is advancing tremendously in this day and age where more and more devices are being deployed in the market of digital devices; so much so that the market is being oversaturated with many different kinds of digital devices to simplify our everyday needs in life. This advancement can have negative consequences, however, there may also be benefits that stem from this technological advancement- especially in healthcare.
One possible benefit is to utilize the device’s capabilities to give real time feedback to people with medical diagnoses. Furthermore, that device could be connected to a stream of other devices to have a greater impact towards diagnosing and treating patients’ health concerns. This platform is known as the Internet of Things (I.o.T.). The I.o.T. is a network of wireless physical devices that are capable of communicating with one via embedded systems, microcontroller devices coupled with sensors, or any other device that is capable of acquiring data.
In this thesis, we investigated the speech of people with Parkinson's disease (PWPD) by applying the I.o.T. computing interface architecture to provide a better solution for diagnosis involving healthcare providers, patients, and administrators. First, we utilized a localized or mobile approach to acquire audio data from a patient with Parkinson’s disease. We investigated the different capabilities of wireless connected devices such as a micro-controller to collect audio data from PWPD, transfer the data securely over the network, process the data for specific means of data extraction and diagnosis, save sensitive audio data extraction, and provided a structural means of data interaction for the user of the processed data to be visualized in a Graphical User Interface (GUI) setting. Our novel means of data collection, transfer, and processing which deploys a micro-controller allows for a localized network usage that minimizes security risks and improves the mobile device’s processing which in turn saves on power consumption, and allows for quick extracted information to be saved on the cloud that decreases the risk for privacy issues. Our method also allows for doctor and patient interactions where a doctor has the ability to evaluate the user’s (who also happens to be the patient) progress without having to physically meet the user. We expect that our methodology and implementation of the I.o.T. architecture will improve treatment of PWPD.
Monteiro, Admir, "Smart Fog Computing Interface for Healthcare Domain Applications" (2016). Open Access Master's Theses. Paper 1046.