CHOMP: Multimodal Chewing Side Detection with Earphones

Abstract

Chewing-side preference (CSP) is a risk factor for temporomandibular disorders (TMDs) and a behavioral manifestation. Although TMDs affect roughly one-third of the global population, assessment relies on clinical examinations and self-reports, providing limited insight into everyday jaw function. We present CHOMP, the first earphone-based chewing-side detection system for continuous CSP monitoring. Using OpenEarable 2.0, we collected multimodal data from 20 participants with microphones, a bone-conduction microphone, IMU, PPG, and a pressure sensor across diverse foods, activities, and acoustic-interference conditions. CHOMP models paired-ear temporal feature sequences using modality-specific bidirectional GRUs, multimodal fusion, and prototype-based classification with an optional short user adaptation. Microphones achieve the strongest single-sensor performance, with median macro F1 scores of 97.2% under leave-one-food-out (LOFO) and 95.7% under leave-one-subject-out (LOSO) evaluation after user adaptation. Multimodal fusion reaches 98.0% under LOFO and 97.3% under adapted LOSO. We demonstrate CHOMP's performance under three acoustic-interference conditions and within a cafeteria setting. Our results establish earphones as a practical platform for everyday CSP monitoring and jaw-function assessment.