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1. Vital Extraction from Microvascular Videos

   Erickson, E. (January 2021, AMSUS (Society of Federal Health Professionals) Annual Meeting)

   Microvascular videos are a method of monitoring patients undergoing surgery. These videos display red blood cell movement in the capillary bed beneath the tongue, analysis of which may indicate patient status and predict negative outcomes. A critical care experiment was conducted in which hemorrhage was induced in 16 healthy anesthetized pigs. Motion features were able to predict blood pressure-related vitals with R^2 between 0.1-0.02. 

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2. Evaluating videos for flipped instruction.

   https://doi.org/10.5951/MTLT.2019.0088  

   Otten, S. (January 2020, The mathematics teacher)
3. Detours for Navigating Instructional Videos

   Kumar Ashutosh, Zihui Xue (January 2024, arXivorg)
4. Analyzing Interactions in Paired Egocentric Videos

   https://doi.org/10.1109/FG57933.2023.10042654  

   Khatri, Ajeeta; Butler, Zachary; Nwogu, Ifeoma (January 2023, IEEE International Conference on Automatic Face and Gesture Recognition (FG))

   As wearable devices become more popular, ego-centric information recorded with these devices can be used to better understand the behaviors of the wearer and other people the wearer is interacting with. Data such as the voice, head movement, galvanic skin responses (GSR) to measure arousal levels, etc., obtained from such devices can provide a window into the underlying affect of both the wearer and his/her conversant. In this study, we examine the characteristics of two types of dyadic conversations. In one case, the interlocutors discuss a topic on which they agree, while the other situation involves interlocutors discussing a topic on which they disagree, even if they are friends. The range of topics is mostly politically motivated. The egocentric information is collected using a pair of wearable smart glasses for video data and a smart wristband for physiological data, including GSR. Using this data, various features are extracted including the facial expressions of the conversant and the 3D motion from the wearer's camera within the environment - this motion is termed as egomotion. The goal of this work is to investigate whether the nature of a discussion could be better determined either by evaluating the behavior of an individual in the conversation or by evaluating the pairing/coupling of the behaviors of the two people in the conversation. The pairing is accomplished using a modified formulation of the dynamic time warping (DTW) algorithm. A random forest classifier is implemented to evaluate the nature of the interaction (agreement versus disagreement) using individualistic and paired features separately. The study found that in the presence of the limited data used in this work, individual behaviors were slightly more indicative of the type of discussion (85.43% accuracy) than the paired behaviors (83.33% accuracy). 

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5. StressNet: Detecting Stress in Thermal Videos

   https://doi.org/10.1109/WACV48630.2021.00104  

   Kumar, Satish; Iftekhar, A S; Goebel, Michael; Bullock, Tom; MacLean, Mary H.; Miller, Michael B.; Santander, Tyler; Giesbrecht, Barry; Grafton, Scott T.; Manjunath, B.S. (January 2021, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV))

   null (Ed.)

   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. 

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