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1. Automated Detection of Rest Disruptions in Critically Ill Patients

   https://doi.org/10.1109/EMBC44109.2020.9175252  

   Iyengar, Vasundhra; Bihorac, Azra; Rashidi, Parisa (July 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   null (Ed.)

   Sleep has been shown to be an indispensable and important component of patients' recovery process. Nonetheless, the sleep quality of patients in the Intensive Care Unit (ICU) is often low, due to factors such as noise, pain, and frequent nursing care activities. Frequent sleep disruptions by the medical staff and/or visitors at certain times might lead to disruption of the patient's sleep-wake cycle and can also impact the severity of pain. Examining the association between sleep quality and frequent visitation has been difficult, due to the lack of automated methods for visitation detection. In this study, we recruited 38 patients to automatically assess visitation frequency from captured video frames. We used the DensePose R-CNN (ResNet-101) model to calculate the number of people in the room in a video frame. We examined when patients are interrupted the most, and we examined the association between frequent disruptions and patient outcomes on pain and length of stay. 

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2. Pain Action Unit Detection in Critically Ill Patients

   https://doi.org/10.1109/compsac51774.2021.00094  

   Nerella, Subhash; Cupka, Julie; Ruppert, Matthew; Tighe, Patrick; Bihorac, Azra; Rashidi, Parisa (July 2021, IEEE Computer Society Computers, Software, and Applications Conference (COMPSAC))

   Existing pain assessment methods in the intensive care unit rely on patient self-report or visual observation by nurses. Patient self-report is subjective and can suffer from poor recall. In the case of non-verbal patients, behavioral pain assessment methods provide limited granularity, are subjective, and put additional burden on already overworked staff. Previous studies have shown the feasibility of autonomous pain expression assessment by detecting Facial Action Units (AUs). However, previous approaches for detecting facial pain AUs are historically limited to controlled environments. In this study, for the first time, we collected and annotated a pain-related AU dataset, Pain-ICU, containing 55,085 images from critically ill adult patients. We evaluated the performance of OpenFace, an open-source facial behavior analysis tool, and the trained AU R-CNN model on our Pain-ICU dataset. Variables such as assisted breathing devices, environmental lighting, and patient orientation with respect to the camera make AU detection harder than with controlled settings. Although OpenFace has shown state-of-the-art results in general purpose AU detection tasks, it could not accurately detect AUs in our Pain-ICU dataset (F1-score 0.42). To address this problem, we trained the AU R-CNN model on our Pain-ICU dataset, resulting in a satisfactory average F1-score 0.77. In this study, we show the feasibility of detecting facial pain AUs in uncontrolled ICU settings. 

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3. Face description using anisotropic gradient: thermal infrared to visible face recognition

   https://doi.org/10.1117/12.2304898  

   Wan, Qianwen; Rao, Shishir Paramathma; Panetta, Karen; Agaian, Sos S.; Kaszowska, Aleksandra; Taylor, Holly; Voronina, V. (May 2018, Mobile Multimedia/Image Processing, Security, and Applications 2018)

   Agaian, Sos S.; Jassim, Sabah A. (Ed.)

   Face recognition technologies have been in high demand in the past few decades due to the increase in human-computer interactions. It is also one of the essential components in interpreting human emotions, intentions, facial expressions for smart environments. This non-intrusive biometric authentication system relies on identifying unique facial features and pairing alike structures for identification and recognition. Application areas of facial recognition systems include homeland and border security, identification for law enforcement, access control to secure networks, authentication for online banking and video surveillance. While it is easy for humans to recognize faces under varying illumination conditions, it is still a challenging task in computer vision. Non-uniform illumination and uncontrolled operating environments can impair the performance of visual-spectrum based recognition systems. To address these difficulties, a novel Anisotropic Gradient Facial Recognition (AGFR) system that is capable of autonomous thermal infrared to visible face recognition is proposed. The main contribution of this paper includes a framework for thermal/fused-thermal-visible to visible face recognition system and a novel human-visual-system inspired thermal-visible image fusion technique. Extensive computer simulations using CARL, IRIS, AT&T, Yale and Yale-B databases demonstrate the efficiency, accuracy, and robustness of the AGFR system. Keywords: Infrared thermal to visible facial recognition, anisotropic gradient, visible-to-visible face recognition, nonuniform illumination face recognition, thermal and visible face fusion method 

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4. Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

   https://doi.org/10.3389/fdgth.2021.640685  

   Shickel, Benjamin; Davoudi, Anis; Ozrazgat-Baslanti, Tezcan; Ruppert, Matthew; Bihorac, Azra; Rashidi, Parisa (February 2021, Frontiers in Digital Health)

   Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients. 

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5. Dynamics of emotional facial expression recognition in individuals with social anxiety

   https://doi.org/10.1145/3204493.3204533  

   Krejtz, Krzysztof; Wisiecka, Katarzyna; Krejtz, Izabela; Holas, Paweł; Olszanowski, Michał; Duchowski, Andrew T. (June 2018, Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA '18))

   This paper demonstrates the utility of ambient-focal attention and pupil dilation dynamics to describe visual processing of emotional facial expressions. Pupil dilation and focal eye movements reflect deeper cognitive processing and thus shed more light on the dy- namics of emotional expression recognition. Socially anxious in- dividuals (N = 24) and non-anxious controls (N = 24) were asked to recognize emotional facial expressions that gradually morphed from a neutral expression to one of happiness, sadness, or anger in 10-sec animations. Anxious cohorts exhibited more ambient face scanning than their non-anxious counterparts. We observed a positive relationship between focal fixations and pupil dilation, indi- cating deeper processing of viewed faces, but only by non-anxious participants, and only during the last phase of emotion recognition. Group differences in the dynamics of ambient-focal attention sup- port the hypothesis of vigilance to emotional expression processing by socially anxious individuals. We discuss the results by referring to current literature on cognitive psychopathology. 

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