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 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 Vital Sign Estimation from Passive Thermal Video www.phwiki.com

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 Vital Sign Estimation from Passive Thermal Video

Davis, George, News Director has reference to this Academic Journal, PHwiki organized this Journal Infrared camera: Mid wave: 3.0-5.0 microns Resolution: 640512 pixels with 14bits Frame rate: 30/60/115 fps Sensitivity: about 25mK To explore contact-free heart rate in addition to respiratory rate detection through measuring infrared light modulation emitted near superficial blood vessels or a nasal area. 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 Vital Sign Estimation from Passive Thermal Video Experiments Overview of our approach Ground truth: ADI PowerLab 4/30 Test dataset: Age 20-60, F:8 in addition to M:12 20 subjects as long as heart rate estimation 7 subjects as long as respiratory rate estimation Accurate subject alignment as long as 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. A novel contact-free vital sign measurement method. Low risk of harm & convenience as long as 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 as long as non-contact measurements of arterial pulse using thermal imaging. CVPR 2006 Workshop. Goal Motivations Challenges Pioneering work Automatic ROI segmentation in addition to alignment Signal enhancement in addition to outlier removal Robust harmonic analysis Region-of-interests segmentation by thresholding the isotherms in addition to alignment by contour tracking. Signal enhancement using a non-linear filter, in addition to outlier removal by pixels-of-interests clustering. Robust harmonic analysis by dominant frequency voting. Per as long as m 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) in addition to a Hamming window Wh(t) Cluster H(xj, f ) in the b in addition to of interest (40-100 bpm as long as heart rates, in addition to 6-30 bpm as long as 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 as long as individual pixels inside the ROI in addition to denote by Respiratory rate estimation results Heart rate estimation results Point-by-point comparisons The initial ROI segmentation results Insensitive to initialization in addition to robust to gentle subject movement in addition to facial expressions. More stable estimation results compared with the state-of-the-art methods. Conclusions

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