אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Cardiovascular drift, the progressive rise in heart rate during sustained exercise at stable intensity, is a sensitive indicator of physiological strain that traditionally requires continuous physiological instrumentation. We show that this latent quantity can be estimated from video alone, with no physiological sensor required at inference time. We propose AsymSGAT, a compact spatio-temporal graph attention network that predicts cumulative cardiac drift from 3D running pose under a session-calibrated protocol: one baseline lap of video establishes a personalized pose reference, after which drift on subsequent laps is predicted directly from changes in running dynamics. The model operates on 3D pose sequences and uses graph-based spatial reasoning over body joints together with temporal attention to capture motion patterns associated with fatigue and cardiovascular load. This seminar will present the motivation, data collection protocol, pose-based modeling approach, experimental setup, and results, demonstrating how video-driven multimodal learning can support non-invasive estimation of physiological dynamics during running.