ABSTRACT
COVID-19 has forced the government to close educational institutes to reduce the spread of the virus. As a result of this decision, students lose contact with teachers and a communication gap also arises. This survey attempts to bridge the gap between students and teachers. Through this survey, we sought to understand where the students are lacking and what are the different steps that can be taken by the teacher to improve the performance of the student and whether this concept should be reviewed or not. We found that most of the researchers who have published papers that we have read did the same mistake in their research, therefore we realized that the concept of AI should be studied again, and we should try not to repeat the same mistake in our research.The main aim of our project is to build 'Teacher facing dashboard' which can help the teacher to summarize,visualize and analyze the data of the education field(academics) and also understanding the students performance using Machine Learning(ML) and Deep Learning (DL). © 2023 IEEE.
ABSTRACT
Introduction: Activity monitoring is important in the ICU where delirium, sedation, and critical illness are associated with both inactivity and agitation. Staff monitoring of motion and sleep is intermittent and resource intense. Wearable actigraphic devices are poorly tolerated and limited to limb motion. Here we demonstrate continuous AI video monitoring in the ICU to provide alwayson, unobtrusive patient activity monitoring. Methods: We conducted a pilot study of AI video monitoring in the Duke University Hospital Medical Intensive Care Unit. Video carts continuously recorded data on encrypted hard drives. Second-by-second AI analysis generated binary motion “counts” that were summed to generate our patient motion metric: counts per minute (CPM). Scene intelligence from AI object and people detectors provided room environment information. These data streams along with de-identified (blurred) video data were used to generate prototype graphical and visual summaries of patient activity patterns and the hospital room environment. Results: We enrolled 22 patients and collected 2155 hours (116 days) of video. Representative time-series data streams are shown in the Figure (top left). These data were acquired from a 76-year-old with liver failure and an escalating nasal cannula oxygen requirement who was endotracheally intubated on the subsequent day. Note 1) the declining patient activity as the patient deteriorates and 2) the significant bedside activity (high acuity) throughout the day. We developed a prototype “overnight report” that summarizes patient activity and room environment. The Figure (bottom left) shows the overnight report for a 54-year-old post-COVID-19 patient admitted to the MICU for respiratory failure with hypoactive delirium that resolved per CAM-ICU on day 5 of data collection. Notably, our report demonstrates significant overnight movement, possibly consistent with a mixed or hyperactive delirium. To visually summarize patient motion, we generated activity “heat maps” over 10-minute intervals. As a control, we showed that the intubated and sedated liver failure patient generated a still heat map (Figure upper right). Further, we visualized daytime hypoactivity/sleep in the delirious post-COVID patient (Figure lower right), suggesting disrupted circadian rhythm, giving additional context to the negative CAM assessment. Conclusions: We demonstrated the feasibility of AI to monitor patient activity in a quaternary-care MICU. Our method has advantages compared to wearable actigraphic methods for monitoring patient activity, including being unobtrusive and being able to visualize and summarize wholebody motion. The data presented here suggest that such monitoring may be able to provide clinically actionable insights in delirium care and sedation weaning.