Oto-Meal：基于PPG和IMU的可穿戴耳式设备用于个性化饮食感知
Oto-Meal: Earable Sensing with PPG and IMU for Personalized Meal Awareness
- Southern University of Science and Technology（南方科技大学）
- Shenzhen Polytechnic University（深圳职业技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出无音频图像的耳式设备Oto-Meal，结合PPG与IMU，通过池化神经识别器和用户内记忆匹配器实现饮食感知，7用户数据集实验显示其低负担且效果优于单模态。
AI中文摘要：
饮食感知可帮助人们反思水分摄入、咀嚼节奏和进食时的对话情况，但许多饮食感知方法依赖摄像头、麦克风、食物照片或重复的自我记录。PPG（光体积描记法）和IMU（惯性测量单元）提供了一种更窄的感知路径，可捕捉与进食相关动作周围的生理和运动模式，无需原始音频、视频或照片。我们提出Oto-Meal，一种无音频和图像的耳式设备原型。其池化神经识别器采用两阶段事件/休息门和五分类行为分类器。此外，用户内协议评估了一个由带标签的目标用户示例构建的轻量级记忆匹配器。我们邀请了7名志愿者，收集了7用户数据集，用于混合用户训练、用户内记忆评估和模态 ablation（消融）。池化模型达到70.99%的事件准确率。在单独的记忆协议下，20%的目标用户校准达到80.38±0.84%的事件准确率和81.77±0.69%的级联准确率；使用60%校准，PPG+IMU达到85.13±0.57%的事件准确率，且优于仅IMU和仅PPU的情况。这些初步结果表明，具有可检查个性化的无音频和图像的耳式感知可支持低负担的饮食感知回顾。
英文摘要：
Meal awareness can help people reflect on hydration, chewing rhythm, and conversation-heavy meals, but many eating-sensing approaches rely on cameras, microphones, food photographs, or repeated self-logging. PPG and IMU offer a narrower sensing path by capturing physiological and motion patterns around meal-adjacent actions without raw audio, video, or photographs. We present Oto-Meal, an audio- and image-free earable prototype. Its pooled neural recognizer uses a two-stage event/rest gate and five-class behavior classifier. Separately, a within-user protocol evaluates a lightweight memory matcher built from labeled target-user examples. We invited seven volunteers and collected a seven-user dataset for mixed-user training, within-user memory evaluation, and modality ablation. The pooled model reaches 70.99% event accuracy. Under the separate memory protocol, 20% target-user calibration reaches 80.38 $\pm$ 0.84% event accuracy and 81.77 $\pm$ 0.69% cascade accuracy; with 60% calibration, PPG+IMU reaches 85.13 $\pm$ 0.57% event accuracy and outperforms IMU-only and PPG-only. These preliminary results suggest that audio- and image-free earable sensing with inspectable personalization can support low-burden meal-awareness review.
