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.
@article{arxiv.2510.03910,
title = {WAFFLE: A Wearable Approach to Bite Timing Estimation in Robot-Assisted Feeding},
author = {Akhil Padmanabha and Jessie Yuan and Tanisha Mehta and Rajat Kumar Jenamani and Eric Hu and Victoria de León and Anthony Wertz and Janavi Gupta and Ben Dodson and Yunting Yan and Carmel Majidi and Tapomayukh Bhattacharjee and Zackory Erickson},
journal= {arXiv preprint arXiv:2510.03910},
year = {2025}
}