  Automatic detection of emotion has the potential to revolutionize mental
health and wellbeing. Recent work has been successful in predicting affect from
unimodal electrocardiogram (ECG) data. However, to be immediately relevant for
real-world applications, physiology-based emotion detection must make use of
ubiquitous photoplethysmogram (PPG) data collected by affordable consumer
fitness trackers. Additionally, applications of emotion detection in healthcare
settings will require some measure of uncertainty over model predictions. We
present here a Bayesian deep learning model for end-to-end classification of
emotional valence, using only the unimodal heartbeat time series collected by a
consumer fitness tracker (Garmin V\'ivosmart 3). We collected a new dataset for
this task, and report a peak F1 score of 0.7. This demonstrates a practical
relevance of physiology-based emotion detection `in the wild' today.
