Experience

Research

Department of Biomedical Engineering

ECG Signal Processing & Edge ML

In Progress

On-device machine learning for ECG signals.

Sender/receiver board with two Raspberry Pi Pico 2 microcontrollers linked over UART

Contributing to an ECG-focused research project involving physiological signals and lightweight machine learning on embedded hardware. My work focuses on embedded C implementation and supporting the integration of ML capabilities into a resource-constrained system.

Role
Undergraduate Researcher
When
Sep 2026 – Present
Where
Madison, WI
Status
In Progress
Built with
Embedded CARM Cortex-MMedical TechnologyUARTSignal ProcessingMachine Learning

Overview

Edge ML for ECG signal processing.

We're integrating edge machine learning with an ECG signal processing algorithm to see whether it can improve accuracy, using multi-channel data.

Sender/receiver board with two Raspberry Pi Pico 2 microcontrollers linked over UART
The sender (left) and receiver (right) boards.

Progress

Where it stands.

The data path between the two boards is in place. ML integration comes next.

  1. UART data link

    Implemented: digital channel data is sent from the sender to the receiver, where processing takes place.

  2. ML integration

    Next step.

Next

Acoustic Wearable Sensing

Real-time 2D pressure mapping from acoustic signals.

Acoustic wearable sensing platform

Viewable soon, once the patent is filed.