Kaylee Yaxuan Li

I am a PhD student at the Computer Science Engineering Department, at University of Michigan, where I work on Human-Computer Interaction, Ubiquitious Computing, Sensing, and Machine Learning. My PhD advisor is Alanson Sample and Kang G. Shin.

From 2019 to 2017, I worked at Intel, as part of Intel RealSense. I primarily designed computer vision algorithms for efficient hardware ASICs, including the Intel RealSense R200 and D400 RGB-D sensors. Additionally, I worked on software APIs, active illumination systems, human-computer interaction devices, and helped develop demos for trade shows, including CES 2012-2016.

I have an MS in Computer Science (AI focus) from Stanford University, where I was a research assistant for Silvio Savarese and a teaching assistant for Fei-Fei Li (CS131 & CS231N). I have a BS in EECS from UC Berkeley, where I worked in Kris Pister's lab.

Email  /  GitHub  /  Google Scholar  /  LinkedIn

Research

I'm interested in computer vision, machine learning, optimization, graphics and robotics.

ECG Signal Construction From Heart Sounds via Single Node, Surface Acoustic Sensing

Effective means of enabling single-lead, non-intrusive, and dry electrocardiogram (ECG) measurements offer the potential for prolonged cardiac rhythm monitoring of mobile users in non-clinical environments. However, existing ECG measurement approaches require accurate electrode placement, cumbersome wiring, and require users to be stationary. Alternatively, current heart sound-based approaches such as phonocardiograms lack the sensitivity and precision to detect crucial cardiac rhythm features and are vulnerable to environmental noise. This work utilizes a wide bandwidth surface-acoustic-wave microphone on the neck to capture heart sounds via the carotid artery. A cross-modal autoencoder, a state-of-the-art algorithm for signal modality conversion, is proposed to transform heart acoustic signals into corresponding ECG waveforms. Results from a 9 participant study demonstrate the effectiveness of constructing a PQRST waveform from acoustic heart sounds and accurately determining critical PQRST metrics. Finally, mobile acoustic ECG wave construction of a user walking is demonstrated, laying the groundwork for unobtrusive, long-term, low-cost daily cardiac rhythm monitoring.

Intel Projects

Besides my work on the RealSense depth sensors and the publications above, a sampling of my publicly disclosed work

Other Projects

These include coursework, side projects and unpublished research work.

Design and source code from Jon Barron's website