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Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network–”StressNet”–features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval : a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual’s stress state (i.e. stress or no stress). A detailed evaluation demonstrates that StressNet achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842. 

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling ofq‐space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling ofq‐space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS‐DSI in post‐mortem or non‐human data. At present, the capacity for CS‐DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter‐scan reliability of 6 different CS‐DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS‐DSI images. This allowed us to compare the accuracy and inter‐scan reliability of derived measures of white matter structure (bundle segmentation, voxel‐wise scalar maps) produced by the CS‐DSI and the full DSI schemes. We found that CS‐DSI estimates of both bundle segmentations and voxel‐wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS‐DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS‐DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS‐DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.