End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker
This addresses the problem of practical emotion detection for mental health applications using affordable, ubiquitous devices, though it is incremental by adapting existing methods to new data types.
The paper tackled emotion detection from heartbeat data collected by consumer fitness trackers, achieving a peak F1 score of 0.7 using a Bayesian deep learning model on a new dataset.
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 Vívosmart 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.