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Automatic detection of emotion has the potential to revolutionize mentalhealth and wellbeing. Recent work has been successful in predicting affect fromunimodal electrocardiogram (ECG) data. However, to be immediately relevant forreal-world applications, physiology-based emotion detection must make use ofubiquitous photoplethysmogram (PPG) data collected by affordable consumerfitness trackers. Additionally, applications of emotion detection in healthcaresettings will require some measure of uncertainty over model predictions. Wepresent here a Bayesian deep learning model for end-to-end classification ofemotional valence, using only the unimodal heartbeat time series collected by aconsumer fitness tracker (Garmin V\'ivosmart 3). We collected a new dataset forthis task, and report a peak F1 score of 0.7. This demonstrates a practicalrelevance of physiology-based emotion detection `in the wild' today.

End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker