egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks

Egocentric vision systems aim to understand the spatial surroundings and the wearer's behavior inside it, including motions, activities, and interaction with objects. Since a person's attention and situational responses are influenced by their physiological state, egocentric systems must also detect this state for better context awareness. In this paper, we propose egoPPG, a novel task for egocentric vision systems to extract a person's heart rate (HR) as a key indicator of the wearer's physiological state from the system's built-in sensors (e.g., eye tracking videos). We then propose EgoPulseFormer, a method that solely takes eye-tracking video as input to estimate a person's photoplethysmogram (PPG) from areas around the eyes to track HR values-without requiring additional or dedicated hardware. We demonstrate the downstream benefit of EgoPulseFormer on EgoExo4D, where we find that augmenting existing models with tracked HR values improves proficiency estimation by 14%. To train and validate EgoPulseFormer, we collected a dataset of 13+ hours of eye-tracking videos from Project Aria and contact-based blood volume pulse signals as well as an electrocardiogram (ECG) for ground-truth HR values. 25 participants performed diverse everyday activities such as office work, cooking, dancing, and exercising, which induced significant natural motion and HR variation (44-164 bpm). Our model robustly estimates HR (MAE=8.82 bpm) and captures patterns (r=0.81). Our results show how egocentric systems may unify environmental and physiological tracking to better understand user actions and internal states.