Progress Report EEE488-converted

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School

Clemson University *

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343

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Mechanical Engineering

Date

Oct 30, 2023

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pdf

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7

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Project Report For: The Prediction of Cardiovascular diseases from ECG Team 42 Team Awesome Team Members Anirudh Kumar Gregory Romero Hassan Ourogneni Stephanie Wesh 31 st October 2021 Faculty Advisor Arindam Sanyal Assistant Professor School of Electrical, Computer, and Energy Engineering Arizona State University
Abstract/Executive Summary: The overall substance of this report is to discuss the findings that have been researched for a portable at-home cardiovascular disease predictor that uses technology such as the mechanisms of a PCB to communicate signals to an ECG after filtering the signals and sending it to an ML model. The prediction results should be able to communicate results to a mobile app. The purpose of this device is to aid people who have a predisposition to cardiovascular disease to better bolster them to track their risk. The methodology of this project would be to first purchase any relevant equipment. Second divide this process into two teams, one team would work particularly on the design and the maneuvers of the PCB while the other team would work on the software portion and the machine learning of the device, the second team will also complete the process of condensing the detection device into a mobile app. Introduction: The report is about the architecture, planning, and design for the substance of the entire project. The overall problem that needs to be tackled is being able to facilitate the risks of the illness, in which this case is cardiovascular diseases in patients who have had a history of related illness or fall under certain risk categories to develop such illness. Such device would be able to detect the illness in the span of four hours before onset. This would all be communicated onto an app that would be comprehensible for the average user to decipher. Upon research we have found the following materials that would help facilitate the process for our proposed ML model for this project. We will be using the NVIDIA Jetson Nano developer kit. We were also provided with a cardiovascular dataset to use as reference or data to incorporate into an ML model. We also were able to find cables that would be able to accommodate the transfer of the ECG from the computer into a PCB for modifying. As far as shortcomings, so far, we have not run into any as we have not started to build this yet. In short, we will be able to discuss further the main pathways of our device including its ability to communicate to a PCB built from scratch to a model, the methodology of how it would work in a step-by-step process which can be considered our approach, what also can be discussed were former ideas that were also contemplated that bare some similarity or complete dissimilarity before taking on this final project. All in all, our main findings had mostly to with how the device would be able to communicate with each aspect to finally be condensed into something that can be comprehensible for the average user. Approach: Hassan Ouro-Gneni Hassan’s skills pertain more to Power Related he also had an interest in signal processing related projects as well. He has great experience and is
skilled in hardware and circuitry which is also a great and vital aspect required for this project Gregory Romero Gregory’s skil ls are more aligned to robotics and hardware design as well which is also a great skill needed for this project as far as the physical part of it. Hassan and Greg both have experience with PCB’s which would be the design that initiates the device for its communicative design Anirudh Kumar Anirudh’s skills are related to anything to with audio, image, and signal processing. He has taken an ASIC design class and UGTA for EEE241, 120, and 304. He is very skilled in signals and systems as well and his skills are very useful for the ML model and the coding for the second half of the project. Stephanie Wesh Anirudh and Stephanie have very similar interests. Specifically audio processing and dsp. Initially the project was supposed to be related to audio processing but because of such diverse skills we were able to find a project that would be able to encompass all our skills equally. The final design for this device would ultimately be readable to the reader but in commencement would first start off by building a PCB once all materials for such has been required whether it be custom, or hand made. Once that aspect will be completed the AI part of this device will be designed by the second half of the team which deals primarily in software and machine learning. Figure 1: Depicts visual methodology of communication between PCB and ML model Project requirements for this would be partially hardware which will account for about 50 percent and the other half would account for more software related completion. Things that will be needed to successfully ensure its successful execution would be cable wires that are both suitable for a computer and the PCB, an ML model which will be implemented through. Another requirement we will need will also be a coding program which can be Xcode, Swift, ect.
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