Yun Ho
PhD Student, University of Chicago
Yun Ho | HCI PhD student
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Yun Ho is a Computer Science Ph.D. student at University of Chicago, advised by Professor Pedro Lopes.
Her work focuses on Human-Computer Interaction. She explores how people make sense of and build relationship with physical assistance systems that communicate with users through proprioception (users’ own movements).
Yun has published her work in top Human-Computer Interaction (HCI) venues, including ACM CHI, with two Best Paper Awards (CHI 2024, CHI 2026).
Publications
Generative Muscle Stimulation: Providing Users with Physical Assistance by Constraining Multimodal-AI with Embodied Knowledge
Yun Ho*, Romain Nith*, Peili Jiang, Steven He, Bruno Felalaga, Shan-Yuan Teng, Rhea Seeralan, Pedro Lopes. CHI’26 (full paper)
Best Paper Award (top 1%)
Electrical muscle stimulation (EMS) can support physical assistance (e.g., shaking a spray can before painting). However, EMS-assistance is highly-specialized because it is (1) fixed (e.g., one program for shaking spray cans, another for opening windows); and (2) non-contextual (e.g., a spray can for cooking dispenses cooking oil, not paint—shaking it is unnecessary). Instead, we explore a different approach where muscle-stimulation instructions are generated considering the user’s context (e.g., pose, location, surroundings). The resulting system is more general—enabling unprecedented EMS-interactions (e.g., opening a pill-bottle) yet also replicating existing systems (e.g., Affordance++) without task-specific programming. It uses computer vision/large language models to generate EMS-instructions, constraining these to a muscle stimulation knowledge-base & joint-limits. In our user study, we found participants successfully completed physical tasks while guided by generative-EMS, even when EMS-instructions were (purposely) erroneous. Participants understood generated-gestures and, even during forced-errors, understood partial-instructions, identified errors, and re-prompted the system. We believe our concept marks a shift toward more general-purpose EMS-interfaces.
Adaptive Electrical Muscle Stimulation Improves Muscle Memory
Siya Choudhary*, Romain Nith*, Yun Ho*, Jas Brooks, Mithil Guruvugari, Pedro Lopes. CHI’25 (full paper)
Electrical muscle stimulation (EMS) can assist in learning motor skills. However, existing EMS systems provide static demonstration—actuating the correct movements, regardless of the user’s learning progress. Instead, we propose a novel adaptive-EMS that changes its guidance strategy based on the participant’s performance. The adaptive-EMS dynamically adjusts its guidance: (1) demonstrate by playing the entire sequence when errors are frequent; (2) correct by lifting incorrect fingers and actuating the correct one when errors are moderate; and (3) warn by lifting incorrect fingers when errors are low. We found that adaptive-EMS improved learning outcomes (recall) compared to traditional EMS—leading to improved “muscle memory.”
SplitBody: Reducing Mental Workload while Multitasking via Muscle Stimulation
Romain Nith, Yun Ho, Pedro Lopes. In Proc. CHI’24 (full paper)
Best Paper Award (top 1%)
Techniques like electrical muscle stimulation (EMS) offer promise in assisting physical tasks by automating movements. However, existing actuation systems improve the performance of a task that users are already focusing on. Instead, we investigate whether these interactive-actuation systems (e.g., EMS) offer any benefits if they automate a task that happens in the background of the user’s focus. In our study, participants performed a cognitively-demanding multitask aided by EMS (SplitBody condition) or performed by themselves (baseline). We found that with SplitBody performance increased (35% on both tasks, 18% on the non-EMS-automated task), physical-demand decreased (31%), and mental-workload decreased (26%).
GazePrompt: Enhancing Low Vision People’s Reading Experience with Gaze-Aware Augmentations
Ru Wang, Zach Potter, Yun Ho, Daniel Killough, Linxiu Zeng, Sanbrita Mondal, Yuhang Zhao. In Proc. CHI’24 (full paper)
Reading is a challenging task for low vision people. While conventional low vision aids (e.g., magnification) offer certain support, they cannot fully address the difficulties faced by low vision users, such as locating the next line and distinguishing similar words. To fill this gap, we present GazePrompt, a gaze-aware reading aid that provides timely and targeted visual and audio augmentations based on users’ gaze behaviors. GazePrompt includes two key features: (1) a Line-Switching support that highlights the line a reader intends to read; and (2) a Difficult-Word support that magnifies or reads aloud a word that the reader hesitates with. We found that GazePrompt significantly reduced participants’ line switching time, reduced word recognition errors, and improved their subjective reading experiences.
