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Infrared camera: Mid wave: 3.0-5.0 microns Resolution: 640*512 pixels with 14bits

Vital Sign Estimation from Passive Thermal Video. Ming Yang 2 , Qiong Liu 1 , Thea Turner 1 , Ying Wu 2 1 FX Palo Alto Laboratory, Inc., 3400 Hillview Ave., Palo Alto, CA 94304 2 Dept. of EECS, Northwestern Univ., 2145 Sheridan Rd., Evanston, IL 60208.

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Infrared camera: Mid wave: 3.0-5.0 microns Resolution: 640*512 pixels with 14bits

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  1. Vital Sign Estimation from Passive Thermal Video Ming Yang2, Qiong Liu1, Thea Turner1, Ying Wu2 1 FX Palo Alto Laboratory, Inc., 3400 Hillview Ave., Palo Alto, CA 94304 2 Dept. of EECS, Northwestern Univ., 2145 Sheridan Rd., Evanston, IL 60208 • Heart rate estimation results • Point-by-point comparisons Goal Challenges Experiments • Infrared camera: • Mid wave: 3.0-5.0 microns • Resolution: 640*512 pixels with 14bits • Frame rate: 30/60/115 fps • Sensitivity: about 25mK • Accurate subject alignment for temporal signal extraction, e.g. involuntary muscular movements are inevitable. • Robust harmonic analysis with low signal-to-noise ratio (SNR) temperature modulation signal, e.g. modulation magnitude 0.1K vs. camera sensitivity 0.025K. • Respiratory rate estimation results • Test dataset: Age 20-60, F:8 and M:12 • 20 subjects for heart rate estimation • 7 subjects for respiratory rate estimation To explore contact-free heart rate and respiratory rate detection through measuring infrared light modulation emitted near superficial blood vessels or a nasal area. Pioneering work Motivations Ground truth: ADI PowerLab 4/30 • A novel contact-free vital sign measurement method. • Low risk of harm & convenience for quick deployment. • Potential applications: airport heath screening, long-term elder care, workplace preventive care, etc. • N. Sun, M.Garbey, A. Merla, I. Pavlidis. Imaging the cardiovascular pulse. CVPR 2005. (S) • S.Y. Chekmenev, A.A. Farag, E.A. Essock. Multiresolution approach for non-contact measurements of arterial pulse using thermal imaging. CVPR 2006 Workshop. Overview of our approach Automatic ROI segmentation and alignment Signal enhancement and outlier removal Robust harmonic analysis • The initial ROI segmentation results • Region-of-interests segmentation by thresholding the isotherms and alignment by contour tracking. • Signal enhancement using a non-linear filter, and outlier removal by pixels-of-interests clustering. • Robust harmonic analysis by dominant frequency voting. • Perform N-point (N=1024/2048/4096) FFT of all temperature signals of all pixels using a sliding window: • Non-linear filtering by taking the point-by-point minimum of a rectangle window Wr(t) and a Hamming window Wh(t) • Cluster H(xj, f ) in the band of interest (40-100 bpm for heart rates, and 6-30 bpm for respiratory rates) using K-means, then select the largest cluster to estimate. • Segment the initial ROI by selecting the isotherm with the sharpest gradient. • Align the ROI by tracking the contour • Extract the temporal signals for individual pixels inside the ROI and denote by Conclusions • Insensitive to initialization and robust to gentle subject movement and facial expressions. • More stable estimation results compared with the state-of-the-art methods.

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