Chewing side preference (CSP) has been identified both as a risk factor for temporomandibular disorders (TMD) and behavioral manifestation. Despite TMDs affecting roughly one third of the global population, assessment mainly relies on clinical examinations and self-reports, offering limited insight into everyday jaw function. Continuous CSP monitoring could provide an objective proxy for functional asymmetries. Prior wearable approaches, however, mostly use specialized form factors and demonstrate limited performance. We therefore present CHOMP, the first system for chewing side detection using earphones. Employing OpenEarable 2.0, we collected data from 20 participants with microphones, a bone-conduction microphone, IMU, PPG, and a pressure sensor across eleven foods, five non-chewing activities, and three noise conditions. We apply the Continuous Wavelet Transform to each sensing modality and use the resulting multi-channel scalograms as inputs to CNN-based classifiers. Microphones achieve the strongest single-sensor unit performance, with median F1 scores of 94.5% in leave-one-food-out (LOFO) and 92.6% in leave-one-subject-out (LOSO) cross-validations. Fusing sensing modalities further improves performance to 97.7% for LOFO and 95.4% for LOSO, with additional evaluations under noise interference indicating robust performance. Our results establish earphones as a practical platform for continuous CSP monitoring, enabling clinicians and patients to assess jaw function in everyday life.

翻译：咀嚼侧偏好已被确认为颞下颌关节紊乱病的风险因素和行为表现。尽管全球约有三分之一人口受颞下颌关节紊乱病影响，其评估主要依赖临床检查和自我报告，难以反映日常颌骨功能。持续监测咀嚼侧偏好可为功能不对称性提供客观指标。然而，现有可穿戴方案多采用专用设备形态且性能有限。为此，我们提出首个基于耳机的咀嚼侧检测系统CHOMP。通过采用OpenEarable 2.0设备，我们采集了20名参与者在十一种食物、五种非咀嚼活动及三种噪声条件下的多模态数据，包括麦克风、骨传导麦克风、惯性测量单元、光电容积描记和压力传感器数据。我们对各传感模态应用连续小波变换，将生成的多通道尺度图作为基于CNN分类器的输入。麦克风在单传感器单元中表现最佳，留一食物交叉验证的中位F1分数达94.5%，留一被试交叉验证达92.6%。多模态融合将性能进一步提升至留一食物交叉验证97.7%和留一被试交叉验证95.4%，噪声干扰下的附加评估表明系统具有鲁棒性能。本研究证实耳机可作为持续监测咀嚼侧偏好的实用平台，为临床医师和患者评估日常颌骨功能提供新途径。