用可穿戴传感器预测进食时机，让助餐机器人更懂用户节奏。
WAFFLE: A Wearable Approach to Bite Timing Estimation in Robot-Assisted Feeding
- 通过可穿戴设备捕捉头部动作、咀嚼和说话等信号，训练回归模型预测进餐时机。
- 15名健康参与者测试中，系统在控制感、理解度和负担感上优于或持平基线方法。
- 适用于不同用户、机器人硬件和用餐场景，尤其适合需要自然互动的助餐需求。
全球数百万人群需进食协助。机器人助餐系统有望提升残障人士自主性与生活质量，并减轻照护者负担，但其广泛应用受限于咬合时机估计难题——即机器人何时将食物送入口中的精准判断。本文提出WAFFLE（Wearable Approach For Feeding with LEarned bite timing），通过可穿戴传感器数据，捕捉头部运动、咀嚼和说话等自然用户信号，实现对咬合时机的高响应预测。我们在14名参与者的数据上训练监督回归模型，并引入用户可调的果断阈值，将预测结果转化为“继续”或“停止”指令。在15名无运动障碍者使用Obi助餐机器人的实验中，WAFFLE在控制感、机器人理解度和工作负荷等指标上表现不逊于或优于基线方法，且多数用户更偏好其用于个人与社交进餐。进一步在2名有运动障碍者家中使用Kinova 7DOF机器人进行的实证表明，该系统具备跨用户、跨硬件、跨机器人位姿、喂食轨迹、食物类型及个体/社交用餐场景的泛化能力。结果证实，WAFFLE能有效实现自然、反应式的咬合时机控制。
原文摘要 · Abstract (English)
Millions of people around the world need assistance with feeding. Robotic feeding systems offer the potential to enhance autonomy and quality of life for individuals with impairments and reduce caregiver workload. However, their widespread adoption has been limited by technical challenges such as estimating bite timing, the appropriate moment for the robot to transfer food to a user's mouth. In this work, we introduce WAFFLE: Wearable Approach For Feeding with LEarned bite timing, a system that accurately predicts bite timing by leveraging wearable sensor data to be highly reactive to natural user cues such as head movements, chewing, and talking. We train a supervised regression model on bite timing data from 14 participants and incorporate a user-adjustable assertiveness threshold to convert predictions into proceed or stop commands. In a study with 15 participants without motor impairments with the Obi feeding robot, WAFFLE performs statistically on par with or better than baseline methods across measures of feeling of control, robot understanding, and workload, and is preferred by the majority of participants for both individual and social dining. We further demonstrate WAFFLE's generalizability in a study with 2 participants with motor impairments in their home environments using a Kinova 7DOF robot. Our findings support WAFFLE's effectiveness in enabling natural, reactive bite timing that generalizes across users, robot hardware, robot positioning, feeding trajectories, foods, and both individual and social dining contexts.
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