Pilotstudy finds computer vision technology effective at determining proper mask wearing in a hospitalsetting brighamwomens BMJ_Open
mean true positive, false positive and false negative respectively. A face is classified as a true positive if the predicted bounding box and the ground truth bounding box do overlap by at least 50% in our case and the predicted class corresponds to the ground truth. A false positive is given, when the predicted bounding box and face bounding box do not overlap with at least 50%, but the predicted class is correct.
We also evaluated this method on a subset of the CCTV footage to show that this tracking method is applicable to our setup. We adapted this method slightly, namely instead of taking the intersection over union as an evaluation metric, we calculated the Euclidean distances of the centroids of the bounding boxes over time.A new identification number is assigned, if a new bounding box is detected.
Therefore, we recorded 3 min of one of the five videos and manually counted the number of people and compared it to the total number of assigned IDs. We would like to note that the mentioned video also contains images from the training set for the YOLOv4 since these images were selected randomly. This should not play a major role because we verified the YOLOv4 mask detector on a separate test set.
We conducted preliminary testing of our CV mask detection system among the study team. To provide live user feedback, we decided to annotate live image with a person bounding box, face bounding box, the mask detection result, mask detection confidence and a counter for past mask detection results. We used a colour scheme of red, yellow and green to delineate the presence or absence face masks on individuals using the CV system.
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Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staffObjectives Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital. Design Single-site, observational cohort study. Setting An urban, academic hospital in Boston, Massachusetts, USA. Participants We enrolled adult hospital staff entering the hospital at a key ingress point. Interventions Consenting participants entering the hospital were invited to experience the computer vision mask detection system. Key aspects of the detection algorithm and feedback were described to participants, who then completed a quantitative assessment to understand their perceptions and acceptance of interacting with the system to detect their mask adherence. Outcome measures Primary outcomes were willingness to interact with the mask system, and the degree of comfort participants felt in interacting with a public facing computer vision mask algorithm. Results One hundred and eleven participants with mean age 40 (SD15.5) were enrolled in the study. Males (47.7%) and females (52.3%) were equally represented, and the majority identified as white (N=54, 49%). Most participants (N=97, 87.3%) reported acceptance of the system and most participants (N=84, 75.7%) were accepting of deployment of the system to reinforce mask adherence in public places. One third of participants (N=36) felt that a public facing computer vision system would be an intrusion into personal privacy. Public-facing computer vision software to detect and provide feedback around mask adherence may be acceptable in the hospital setting. Similar systems may be considered for deployment in locations where mask adherence is important. Data are available in a public, open access repository.
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