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.

Electrical muscle stimulation (EMS) has been leveraged to assist in learning motor skills by actuating the user's muscles. However, existing systems provide static demonstration—actuating the correct movements, regardless of the user's learning progress. Instead, we contrast two versions of a piano-tutoring system: a conventional EMS setup that moves the participant's fingers to play the sequence of movements correctly, and a novel adaptive-EMS system 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) and was preferred by participants. We believe this approach could inspire new types of physical tutoring systems that promote adaptive over static guidance.

Techniques like electrical muscle stimulation (EMS) offer promise in assisting physical tasks by automating movements, e.g., shaking a spray-can or tapping a button. However, existing actuation systems improve the performance of a task that users are already focusing on (e.g., users are already focused on using the spray-can). 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. Thus, we explored whether automating a repetitive movement via EMS would reduce mental workload while users perform parallel tasks (e.g., focusing on writing an essay while EMS stirs a pot of soup). 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%).

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.

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.