The world's expanding and aging population has created a demand for inexpensive, unobtrusive, automated healthcare solutions. Eulerian Video Magnification (EVM) aids in the development of these solutions by allowing for the extraction of physiological signals from video data. This paper examines the potential of thermal video in conjunction with EVM to extract physiological measures, particularly heart rate. This paper also proposes an adaptive EVM approach to amplify the desired signal, while avoiding noise amplification. A subject, wearing a textile sensor band collecting ECG, sat still while both a thermal camera and an iPad camera captured video. The iPad video was subjected to EVM, using a wide bandpass filter and low magnification factor. Mean intensity signals for five Regions of Interest (ROIs) were then calculated to extract a signal representing heart rate. The ECG signal was used to validate the ROI resulting in the mean intensity signal best representing heart rate. The thermal video was then subjected to EVM using the same wide bandpass filter and the identified ideal ROI mean intensity post-processing. This signal was compared to the enhanced iPad video mean intensity signal to verify the correct signal was extracted. The original thermal video was subjected again to EVM processing and ROI mean intensity post-processing, this time using an adapted, targeted narrow bandpass filter. Results indicated that thermal video, in conjunction with the proposed adapted EVM method and ROI post-processing can reveal physiological signals like heart rate and limit the potential of revealing an amplified noise signal.