Discovery Design Notes-converted

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Oct 30, 2023

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DISCOVERY DESIGN NOTE FOR ECARDIO Team 48 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

DAW Feature: Audio track to MIDI Track Converter Implementation: • MATLAB - Most suitable program to facilitate this process - Deals with Signal Processing on a grander level than most programs and offers more options for audio signal processing as well. - MATLAB also allows filtering processes for sounds whether it be for music, detection, or vocal inflections and what not. - There are several functions that go along with it as well just a few examples • What we have below are MIDI recordings for certain particular instruments in the DAW Logic Pro X • And what we have here is the bounced instruments recorded into one audio track • In simple terms, the Converter would turn something like this

• Back into this Solar Power Idea Instead dumping that water in the middle of the desert what if we recycle it using solar power since we already in the desert in the Middle East and use it as irrigation water for the military own farms so they will not totally depend on contractors. And also they save money too Electronics Idea: Voice ID- Voice recognition for phones instead of facial recognition XTAKEN: Rockford Fosgate: Create a portable piece of hardware that can monitor the temperature of a loudspeaker voice coil in real time. Ideally hardware is modular or expandable to monitor multiple loudspeakers simultaneously. Capture and log temperature vs time on in a plain text file for analysis later (Arduino, raspberry pi, etc, but not tethered to a computer). Deliverables: document how the device works, how to reproduce building another, well documented code, and deliver a working physical copy that can be started using immediately. A stretch goal to also monitor rms voltage and current to the loudspeaker." XTAKEN:Portable at-home Sepsis predictor for at-risk patients using Machine Learning Description: Sepsis is a leading cause of death worldwide, and 80% of patients have sepsis onset outside hospital settings. Real-time, at-home health monitoring for at-risk patients is a potential solution for predicting sepsis onset and providing timely intervention. The goal of this project is build a portable, printed-circuit board (PCB) level device that can replicate the system shown in the Figure below. The PCB device should accept ECG signal, perform amplification and filtering, and then send to 1a machine learning (ML) model that

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will detect sepsis at least 4 hours before onset with high accuracy. The PCB device should be able to communicate the sepsis prediction results to a mobile app." Mode Switching Glove Apparatus: Wherein a person controlling a robot wears a glove and switches robotic modes using the glove features. This is a human-robot interaction project. Example: astronaut is exploring a planetary surface. This is quite dangerous, so he has a robot/rover to his side. He can control the robot using the glove, so this will make it easy to command from a distance. He can switch the mode to have the robot follow him. If he sees something interesting he can have the robot investigate it by switching to “exploration mode” and pointing a laser to the item to be investigated (maybe a rock). Once the robot gets there, a mode can be initialized to do certain tasks. Other applications can include on-Earth tasks like mechanical assistant, rescue missions etc. So it's a lot of electronics, signal processing and robotics. Decided : Prediction of cardiovascular diseases from ECG: this is very similar to the sepsis task, we have a dataset for different CVDs that can be predicted from ECG signal. There are also publicly available dataset for this. Methodology model for predictor

Tools: Building pcb Create schematic of components Copper trace

Routing traces Send pcb design to manufacturer Send custom made PCB to JLCPCBOnline Items needed : PCB pending cardiovascular dataset, Nvidia board AI to facilitate machine learning, MATLAB(for ) to detect sepsis 4 hours before onset, for the mobile app(Xcode or swift), pcb

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