Emotion dysregulation is a known risk factor for a variety of maladaptive eating behaviors, including emotional eating (Crockett, Myhre, & Rokke, 2015; Lavender et al., 2015). New passive sensing technologies offer the prospect of detecting emotion dysregulation in real-time through measurement of heart rate variability (HRV), a transdiagnostic bio-signal of emotion regulation, which may in turn signal risk of engaging in a maladaptive eating behavior. In the current study, our primary aim was to test the hypothesis that momentary changes in HRV can be used to detect risk of experiencing an emotional eating episode in an ecologically valid setting using a wrist worn sensor with acceptable classification accuracy. Participants were 21 adults with clinically significant emotional eating behaviors. Participants wore the Empatica E4 wrist-sensor and tracked all emotional eating episodes using ecological momentary assessment for four weeks. Time and frequency domain features of HRV were extracted in the 30-min period preceding emotional eating episodes and control cases (defined as the 30 min prior to an EMA survey that did not contain an emotional eating episode). Support vector machine (SVM) learning models were implemented using time domain and frequency domain features. SVM models using frequency domain features achieved the highest classification accuracy (77.99%), sensitivity (78.75%), and specificity (75.00%), consistent with standards deemed acceptable for the prediction of event-level health behavior. SVM models using time domain features still performed above chance, though were less accurate at classifying episodes (accuracy 63.48%, sensitivity 62.68%, and specificity 70.00%) and did not meet acceptable classification accuracy. Wearable sensors that assess HRV show promise as a tool for capturing risk of engaging in emotional eating episodes.