New research from Cornell University and Carnegie Mellon University that advances assistive feeding by predicting when someone is ready for their next bite has received the Best Paper Award at the American Computer Machinery (ACM)/Institute of Electrical and Electronics Engineers (IEEE) Conference on Human Robot Interaction, held March 16-19 in Edinburgh, Scotland.
The paper, called “WAFFLE: A Wearable Approach to Bite Timing Estimation in Robot-Assisted Feeding,” presents a new assistive feeding algorithm that can be integrated with robotic feeding devices to predict when someone is ready for their next bite by interpreting natural behavioral cues.
Cornell authors are: Rajat Kumar Jenamani, a doctoral student in the field of computer science; Eric Hu ‘26; Ben Dodson ‘25; Yunting Yan, a doctoral student in the field of robotics, and Tapomayukh Bhattacharjee, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science. WAFFLE’s lead authors are Akhil Padmanabha and Jessie Yuan of CMU.
WAFFLE works by equipping participants with a set of lightweight sensors embedded in glasses and earbuds, along with a commercially available throat microphone. These sensors capture signals associated with everyday eating behaviors such as head movement, chewing, speaking, and vibrations from swallowing and vocal activity. By combining motion data from the glasses and earbuds with vibration data from the throat microphone, WAFFLE can analyze how people naturally engage with food and conversation.
This story was adapted from CMU’s feature called, “Predicting the Next Bite.”