The Covid-19 Pandemic has renewed interest in contactless vital signs monitoring using state-of-the-art computer vision, which can efficiently screen for symptoms while reducing the risk of disease transmission. Despite the promising perfor- mance, the use of static camera setups requires subjects to remain static inside a field of view (FoV) for a pre-specified duration. Due to inconsistent ambient environmental conditions, the transit of individuals through the FoV, and the time it may take to triage individuals, the widespread adoption of static camera systems to continuously monitor vital signs has had suboptimal uptake. Robotic systems enable autonomous and continuous monitoring, but these require expensive cameras, computers, and robotic platforms, limiting widespread deployment. In response, we propose a cost-effective and scalable robotic solution consisting of a suite of commercial, off-the-shelf wireless cameras for capturing photoplethysmography (PPG) on ambulatory subjects linked to a single computer that supervises the cameras to compute the vital signs of subjects. Throughout a set of careful investigations of each individual step of the wireless machine vision camera and computer, bottlenecks constraining wireless live-streaming of high-quality PPG information are identified and those are addressed by a hybrid centralized/decentralized wireless machine vision protocol. Our results demonstrate that the proposed cost-effective wireless camera achieves equivalent remote-PPG accuracy to its costly, USB3 counterparts (mean error: 5.0 BMP vs. 4.7 BPM) by means of the hybrid camera protocol which boosts the overall frame rate to 17 FPS. In contrast, using the standard method that captures the PPG with the same spatial resolution can only achieve 1 FPS. In addition, this work also elucidates how varying the distance, image pixel density, frame rate, image compression, image downsampling, and color depth affect the rPPG performance. For each of the effects, we also discuss potential solutions for the cost-effective setup.

Recent advances in integrated circuits and micromachining have enabled the integration of battery-powered micro-actuators in miniaturized drug delivery systems. However, the power/energy management system that treats current overloading remains sub-optimal. Overloading not only deteriorates the actuators’ long-term performance but also attenuates battery capacity. In this work, we are proposing a simple yet powerful solution to manage current overloading to maximize battery-powered system life-time. The proposed solution consists of a digitally programmable soft starter and DC-DC converter that can dynamically balance the trade-off between inrush current amplitude and motor starting speed as well as maximally minimize the continuous current draw from a battery. Our experimental results show that the proposed soft starter alone can enhance the battery capacity by 18% and together with the DC-DC converter, they can increase the drug delivery cycles by 33% without sacrificing the system’s output performance.

The COVID-19 pandemic has accelerated methods to facilitate contactless evaluation of patients in hospital settings. By minimizing unnecessary in-person contact with individuals who may have COVID-19 disease, healthcare workers (HCW) can prevent disease transmission, and conserve personal protective equipment. Obtaining vital signs is a ubiquitous task that is commonly done in-person. To eliminate the need for in-person contact for vital signs measurement in the hospital setting, we developed Dr. Spot, an agile quadruped robotic system that comprises a set of contactless monitoring systems for measuring vital signs and a tablet computer to enable face-to-face medical interviewing. Dr. Spot is teleoperated by trained clinical staff to facilitate enhanced telemedicine. Specifically, it has the potential to simultaneously measure skin temperature, respiratory rate, heart rate, and blood oxygen saturation simultaneously while maintaining social distancing from the patients. This is important because fluctuations in vital sign parameters are commonly used in algorithmic decisions to admit or discharge individuals with COVID-19 disease. Here, we deployed Dr. Spot in a hospital setting with the ability to measure the vital signs from healthy volunteers from which the measurements of elevated skin temperature screening, respiratory rate, heart rate, and SpO2 were carefully verified with ground-truth sensors.