arXiv subjects

Yun Ho

Publications and source records attributed to Yun Ho.

4 recordsLinked to original sources

arXiv2026

ObjectEMS: Electrical Muscle Stimulation Without Electrodes on the User

Interactive electrical muscle stimulation (EMS) has revealed its promise as a portable interface for force-feedback. However, while much ink has been spilled about the advantages of EMS, few have investigated one of its central limitations: the need to attach electrodes to users. This has dramatically limited the application of EMS, especially in brief interactions or physical assistance with tools. To explore an alternative, we propose embedding electrodes (and stimulator) inside objects that the user interacts with. This is made possible because we identified multiple novel electrode placements that can elicit four distinct finger movements from the palm (no forearm stimulation). To illustrate this new way of implementing electrical muscle stimulation, we developed a set of self-contained interactive objects that use capacitive sensing to determine if a user's hand is in poses conducive to stimulation and then actuate the fingers from contact points between the hand and the grasped object. In our user study, we found that participants spent less time calibrating ObjectEMS than traditional EMS, and they felt less tethered while engaging with an EMS application via this novel approach. Our approach frees up the user's body from wearing electrodes and EMS stimulators, providing a new pathway for researching, implementing, or deploying actuated tangible interfaces. We demonstrate its potential via exemplary applications, such as a game controller with finger-level force feedback, a door handle with force feedback to prevent push-pull confusion, and more.

cs.HC↗

arXiv2025

Generative Muscle Stimulation: Providing Users with Physical Assistance by Constraining Multimodal-AI with Embodied Knowledge

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.

cs.HC↗

arXiv2024

GazePrompt: Enhancing Low Vision People's Reading Experience with Gaze-Aware Augmentations

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. Through a study with 13 low vision participants who performed well-controlled reading-aloud tasks with and without GazePrompt, we found that GazePrompt significantly reduced participants' line switching time, reduced word recognition errors, and improved their subjective reading experiences. A follow-up silent-reading study showed that GazePrompt can enhance users' concentration and perceived comprehension of the reading contents. We further derive design considerations for future gaze-based low vision aids.

cs.HC↗

arXiv2023

Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networks

Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference cases. However, different from conventional terrestrial base stations, LEO is deployed at considerable height and under high mobility, inducing substantially long delay and interference during transmission. As a result, acquiring the accurate channel state information between LEOs and ground users is challenging. Therefore, we construct a framework with a two-way transmission under unknown channel information and no data collected at long-delay ground gateway. In this paper, we propose hierarchical multi-agent multi-armed bandit resource allocation for LEO constellation (mmRAL) by appropriately assigning available radio resources. LEOs are considered as collaborative multiple macro-agents attempting unknown trials of various actions of micro-agents of respective resources, asymptotically achieving suitable allocation with only throughput information. In simulations, we evaluate mmRAL in various cases of LEO deployment, serving numbers of users and LEOs, hardware cost and outage probability. Benefited by efficient and resilient allocation, the proposed mmRAL system is capable of operating in homogeneous or heterogeneous orbital planes or constellations, achieving the highest throughput performance compared to the existing benchmarks in open literature.

cs.LG↗