Taeyoung Yeon

3546, Mudd Library

2233 Tech Dr

Evanston, IL 60208

About Me

I am an incoming PhD student at NYU Tandon in the Human-Centered Technology Innovation and Design program, advised by Professor Vedant Das Swain. Previously, I served as a Post-Baccalaureate Research Fellow at Northwestern University, advised by Professor Karan Ahuja. I earned a Bachelor of Science in Computer Science and Engineering from Seoul National University. Additionally, I worked at the mobile game company, Awesomepiece, as a Full Stack Game Developer.

My research interests include Human-Computer Interaction (HCI), On-Device Machine Learning (ML), Ubiquitous Computing, and real-time healthcare applications.

news

Mar 10, 2026	Submitted a paper to ISMAR 2026 and a paper to ECCV 2026.
Feb 09, 2026	Our paper has been accepted to CVPR 2026 Findings!
Nov 13, 2025	Submitted a paper to CVPR 2026.
Jul 07, 2025	My first author academic paper (WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatches) was accepted to ICMI’25. Check my homepage for details.
May 01, 2025	Submitted a paper to ICMI 2025.

publications

WatchHAR: Real-time On-device Human Activity Recognition System for Smartwatches

Taeyoung Yeon, Vasco Xu, Hank Hoffman, and 1 more author

In Proceedings of the 27th ACM International Conference on Multimodal Interaction, 2025

Abs PDF Video

Despite advances in practical and multimodal human activity recognition (HAR), a system that runs entirely on smartwatches in unconstrained environments remains elusive. We present WatchHAR, an audio and inertial-based HAR system that operates fully on smartwatches, addressing privacy and latency issues associated with external data processing. By optimizing each component of the pipeline, WatchHAR achieves compounding performance gains. We introduce a novel architecture that unifies sensor data pre-processing and inference into an end-to-end trainable module, significantly accelerating performance by 25× while maintaining over 90% accuracy on 25+ activity classes. WatchHAR outperforms state-of-the-art models in terms of accuracy while running on the smartwatch directly at 30 Hz for multimodal activity classification and 40 Hz for activity event detection. This research advances edge-based activity recognition, realizing smartwatches’ potential as standalone, minimally-invasive activity tracking devices.