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.

翻译：电肌肉刺激（EMS）能够提供物理辅助（例如，在喷漆前摇晃喷雾罐）。然而，EMS辅助具有高度专用性，因为它（1）是固定的（例如，一个程序用于摇晃喷雾罐，另一个用于开窗）；（2）缺乏情境感知（例如，用于烹饪的喷雾罐是喷食用油的，摇晃它并无必要）。相反，我们探索了一种不同的方法，该方法在生成肌肉刺激指令时会考虑用户的情境（例如，姿态、位置、周围环境）。由此产生的系统更具通用性——既能实现前所未有的EMS交互（例如，打开药瓶），又能复现现有系统（例如，Affordance++）而无需针对特定任务进行编程。该系统利用计算机视觉/大语言模型生成EMS指令，并通过肌肉刺激知识库和关节活动度限制对这些指令进行约束。在我们的用户研究中，我们发现参与者在生成式EMS的引导下成功完成了物理任务，即使EMS指令（故意）存在错误。参与者能够理解生成的姿态指令，甚至在强制错误情境下，也能理解部分指令、识别错误并重新向系统发起提示。我们相信，我们的概念标志着向更通用的EMS接口迈进了一步。