The COVID-19 pandemic has accelerated methods to facilitate contactless evaluation of patients in hospital settings. By minimizing in-person contact with individuals who may have COVID-19, healthcare workers can prevent disease transmission and conserve personal protective equipment. Obtaining vital signs is a ubiquitous task that is commonly done in person by healthcare workers. To eliminate the need for in-person contact for vital sign measurement in the hospital setting, we developed Dr. Spot, a mobile quadruped robotic system. The system includes IR and RGB cameras for vital sign monitoring and a tablet computer for face-to-face medical interviewing. Dr. Spot is teleoperated by trained clinical staff to simultaneously measure the skin temperature, respiratory rate, and heart rate while maintaining social distancing from patients and without removing their mask. To enable accurate, contactless measurements on a mobile system without a static black body as reference, we propose novel methods for skin temperature compensation and respiratory rate measurement at various distances between the subject and the cameras, up to 5 m. Without compensation, the skin temperature MAE is 1.3°C. Using the proposed compensation method, the skin temperature MAE is reduced to 0.3°C. The respiratory rate method can provide continuous monitoring with a MAE of 1.6 BPM in 30 s or rapid screening with a MAE of 2.1 BPM in 10 s. For the heart rate estimation, our system is able to achieve a MAE less than 8 BPM in 10 s measured in arbitrary indoor light conditions at any distance below 2 m.
The trade-off between spatial and temporal resolution remains a fundamental challenge in machine vision. A captured image often contains a significant amount of redundant information, and only a small region of interest (ROI) is necessary for object detection and tracking. In this paper, we first systematically characterize the effects of ROI on camera capturing, data transmission, and image processing. We then present the closed-loop ROI algorithm capable of high spatial and temporal resolution as well as wide scanning field of view (FOV) in single and multi-object detection and tracking via real-time wireless video streaming. With the feedback from real-time object tracking, the wireless camera is able to capture and transmit only the ROI which in turn enhances both the spatial and temporal resolution in object tracking. In addition, the proposed approach can still maintain a large FOV by processing regions outside of the ROI at lower spatial and temporal resolutions. When applied to a high spatial resolution wireless stream (5 MegaPixels), the closed-loop ROI algorithm improves the temporal resolution by up to 10x (from 2.4 FPS to 22.5 FPS). Specifically, camera processing is improved by up to 4.7x, data transmission is improved by up to 160x, and PC processing is improved by up to 2.5x. In a person tracking experiment, the closed-loop ROI algorithm enables a wide-angle camera to outperform both a normal wide-angle camera-which suffers from poor temporal resolution and motion blur-and a pan & tilt camera-which cannot automatically refresh tracking after the tracking is lost.
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