WAFFLE: A Wearable Approach to Bite Timing Estimation in Robot-Assisted Feeding

2025-10-04

AI Summary

This study addresses the challenge of accurately predicting bite timing in robot-assisted feeding. We propose a wearable-sensing–based, personalized learning framework for bite-intent recognition. By fusing inertial (IMU) and audio sensor data—capturing natural head motion, chewing, and speech—we train a supervised regression model to predict bite onset. An adaptive decision module enables user-defined autonomy thresholds and ensures compatibility with diverse robotic platforms and real-world dining environments. The framework demonstrates strong generalizability across users, devices, and contexts, and has been validated with both able-bodied and motor-impaired populations. User experience evaluation shows significant improvements—or parity—over baseline methods in perceived control, robot comprehension, and cognitive load; the majority of participants expressed clear preference for our approach.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

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.

Problem

• Estimating appropriate bite timing for robot-assisted feeding systems
• Predicting bite timing using wearable sensors and user movement cues
• Creating generalizable bite timing across users and dining contexts

Innovation

• Wearable sensor data predicts bite timing
• Supervised regression model with adjustable threshold
• Generalizes across users robots and dining contexts