Rapid eating is common yet difficult to regulate in situ, partly because people seldom notice pace changes and sustained self-monitoring is effortful. We present Earinter, a commodity-earbud-based closed-loop system that integrates in-the-wild sensing, real-time reasoning, and theory-grounded just-in-time (JIT) intervention to regulate eating pace during daily meals. Earinter repurposes the earbud's bone-conduction voice sensor to capture chewing-related vibrations and estimate eating pace as chews per swallow (CPS) for on-device inference. With data collected equally across in-lab and in-the-wild sessions, Earinter achieves reliable chewing detection (F1 = 0.97) and accurate eating pace estimation (MAE: 0.18 $\pm$ 0.13 chews/min, 3.65 $\pm$ 3.86 chews/swallow), enabling robust tracking for closed-loop use. Guided by Dual Systems Theory and refined through two Wizard-of-Oz pilots, Earinter adopts a user-friendly design for JIT intervention content and delivery policy in daily meals. In a 13-day within-subject field study (N=14), the closed-loop system significantly increased CPS and reduced food-consumption speed, with statistical signs of carryover on retention-probe days and acceptable user burden. Our findings highlight how single-modality commodity earables can support practical, theory-driven closed-loop JIT interventions for regulating eating pace in the wild.

翻译：快速进食现象普遍存在，但难以进行现场调节，部分原因在于人们很少注意到进食速度的变化，且持续的自我监测需要耗费精力。本研究提出Earinter，一种基于商用耳塞的闭环系统，集成了野外感知、实时推理和基于理论的即时干预功能，旨在日常用餐过程中调节进食速度。Earinter重新利用耳塞的骨传导语音传感器捕捉与咀嚼相关的振动，并以每吞咽次数咀嚼数估计进食速度，实现设备端推理。通过实验室与野外场景均衡采集的数据，Earinter实现了可靠的咀嚼检测（F1 = 0.97）和精确的进食速度估计（平均绝对误差：0.18 ± 0.13 次咀嚼/分钟，3.65 ± 3.86 次咀嚼/吞咽），为闭环应用提供了稳健的追踪能力。在双重系统理论指导下，并通过两次向导式模拟实验优化，Earinter采用用户友好的设计，制定了适用于日常用餐的即时干预内容与实施策略。在一项为期13天的受试者内现场研究中（N=14），该闭环系统显著提升了每吞咽咀嚼数并降低了进食速度，在保留测试日观察到统计显著的持续效应，且用户负担处于可接受水平。我们的研究结果表明，单模态商用可穿戴耳塞能够支持实用性强、理论驱动的闭环即时干预系统，在自然环境中有效调节进食速度。