Contactless Estimation of Heart Rate, Arm Tremor from Footage of Elite Archers (www.mdpi.com)

Researchers demonstrated a non-contact method to estimate heart rate, heart rate variability (HRV) and micro–arm tremors from broadcast Olympic archery footage, then linked those biometric signals to shot scores. Using nine YouTube videos of elite competition, they applied camera-based physiological extraction and fine-motion analysis to capture HR/HRV (e.g., RMSSD) and sub-millimeter tremor patterns during aiming and release. Statistical tests—including group comparisons and ordinal logistic regression—were used to quantify associations between elevated heart rate, right-arm tremor magnitude, and lower performance, proposing HRV and micro-tremor metrics as meaningful indicators of readiness and precision. This work is significant for AI/ML because it moves biometric monitoring out of controlled labs and wearables into real-world, in-game video analysis, combining computer vision, remote photoplethysmography-like signal processing, and movement analytics. Technical implications include handling motion artifacts and variable video quality, validating physiological feature extraction against performance labels, and using interpretable statistical models to link signals to outcomes. Practically, the approach opens avenues for real‑time, contactless athlete monitoring and personalized training feedback in precision sports (archery, shooting, golf), while raising ML challenges around domain adaptation, robustness, and privacy when scaling to live broadcasts.