AI 中文总结
本文提出Sensorimotor Stickies可重配置穿戴式体感平台，通过模块化黏贴模块与配套应用实现多场景闭环体感训练，经多类评估验证了其技术可行性与用户可配置性。
AI 中文摘要
闭环体感训练系统可通过感知运动并提供实时反馈来提升学习效果，但多数系统为固定实现，绑定单一任务，尽管其核心技术（惯性与触觉感知、振动触觉提示、基于规则的逻辑）保持不变。本文提出Sensorimotor Stickies，一种可重配置穿戴式平台，将感知与振动触觉反馈视为可按需贴附于身体的模块化黏贴模块。该平台包含用于IMU感知的小型化黏合模块、可选触觉感知模块、振动触觉执行模块；支持低功耗固件与BLE基础设施，可实现原始数据流式传输与电机控制，无需针对特定任务重写代码；配套移动应用提供基于身体中心的共享模型，用于模块贴放、校准及反馈定制。这些组件共同支持训练场景、用户需求及反馈设置的重配置。我们通过技术特性表征、配置应用演示、从业者介导的配置会话及终端用户研究对平台进行评估，验证了其技术可行性、重配置范围及终端用户在首次设置、校准与任务内反馈重配置方面的可配置性。
英文摘要
Closed-loop sensorimotor training systems can improve learning by sensing movement and delivering real-time feedback, yet most are built as fixed implementations tied to a single task, even though the core technology (inertial and tactile sensing, vibrotactile cueing, rule-based logic) remains the same. We present Sensorimotor Stickies, a reconfigurable on-body platform that treats sensing and vibrotactile feedback as modular stickies that can be patched onto the body as needed. The platform includes miniaturized adhesive modules for IMU sensing, optional tactile sensing, and vibrotactile actuation; low-power firmware and BLE infrastructure for raw streaming and motor control without task-specific rewrites; and a companion mobile app that provides a shared body-centered model for placement, calibration, and feedback authoring. Together, these components enable reconfiguration across training scenarios, user needs, and feedback setups. We evaluate the platform through technical characterization, configured application demonstration, practitioner-mediated configuration sessions, and an end-user study, demonstrating technical feasibility, reconfiguration breadth, and end-user configurability for first-time setup, calibration, and within-task feedback reconfiguration.
