This article examines 152 reports the use of robots explicitly due to the COVID-19 pandemic reported in the science, trade, and press from 24 Jan 2021 to 23 Jan 2022 (Year 2) and compares with the previously published uses from 24 Jan 2020 to 23 Jan 2021 (Year 1). Of these 152 reports, 80 were new unique instances documented in 25 countries, bringing the total to 420 instances in 52 countries since 2020. The instances did not add new work domains or use cases, though they changed the relative ranking of three use cases. The most notable trend in Year was the shift from a) government or institutional use of robots to protect healthcare workers and the Public to b) personal and business use to enable the continuity of work and education. In Year 1, Public Safety, Clinical Care, and Continuity of Work and Education were the three highest work domains but in Year 2, Continuity of Work and Education had the highest number of instances.
more »
« less
S4 Features and Artificial Intelligence for Designing a Robot against COVID-19—Robocov
Since the COVID-19 Pandemic began, there have been several efforts to create new technology to mitigate the impact of the COVID-19 Pandemic around the world. One of those efforts is to design a new task force, robots, to deal with fundamental goals such as public safety, clinical care, and continuity of work. However, those characteristics need new products based on features that create them more innovatively and creatively. Those products could be designed using the S4 concept (sensing, smart, sustainable, and social features) presented as a concept able to create a new generation of products. This paper presents a low-cost robot, Robocov, designed as a rapid response against the COVID-19 Pandemic at Tecnologico de Monterrey, Mexico, with implementations of artificial intelligence and the S4 concept for the design. Robocov can achieve numerous tasks using the S4 concept that provides flexibility in hardware and software. Thus, Robocov can impact positivity public safety, clinical care, continuity of work, quality of life, laboratory and supply chain automation, and non-hospital care. The mechanical structure and software development allow Robocov to complete support tasks effectively so Robocov can be integrated as a technological tool for achieving the new normality’s required conditions according to government regulations. Besides, the reconfiguration of the robot for moving from one task (robot for disinfecting) to another one (robot for detecting face masks) is an easy endeavor that only one operator could do. Robocov is a teleoperated system that transmits information by cameras and an ultrasonic sensor to the operator. In addition, pre-recorded paths can be executed autonomously. In terms of communication channels, Robocov includes a speaker and microphone. Moreover, a machine learning algorithm for detecting face masks and social distance is incorporated using a pre-trained model for the classification process. One of the most important contributions of this paper is to show how a reconfigurable robot can be designed under the S3 concept and integrate AI methodologies. Besides, it is important that this paper does not show specific details about each subsystem in the robot.
more »
« less
- Award ID(s):
- 1828010
- PAR ID:
- 10344135
- Date Published:
- Journal Name:
- Future Internet
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 1999-5903
- Page Range / eLocation ID:
- 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The COVID-19 pandemic has infected millions of people around the world, spreading rapidly and causing a flood of patients that risk overwhelming clinical facilities. Whether in urban or rural areas, hospitals have limited resources and personnel to treat critical infections in intensive care units, which must be allocated effectively. To assist clinical staff in deciding which patients are in the greatest need of critical care, we develop a predictive model based on a publicly-available data set that is rich in clinical markers. We perform statistical analysis to determine which clinical markers strongly correlate with hospital admission, semi-intensive care, and intensive care for COVID-19 patients. We create a predictive model that will assist clinical personnel in determining COVID-19 patient prognosis. Additionally, we take a step towards a global framework for COVID-19 prognosis prediction by incorporating statistical data for geographically and ethnically diverse COVID--19 patient sets into our own model. To the best of our knowledge, this is the first model which does not exclusively utilize local data.more » « less
-
Background Telemedicine as a mode of health care work has grown dramatically during the COVID-19 pandemic; the impact of this transition on clinicians’ after-hours electronic health record (EHR)–based clinical and administrative work is unclear. Objective This study assesses the impact of the transition to telemedicine during the COVID-19 pandemic on physicians’ EHR-based after-hours workload (ie, “work outside work”) at a large academic medical center in New York City. Methods We conducted an EHR-based retrospective cohort study of ambulatory care physicians providing telemedicine services before the pandemic, during the acute pandemic, and after the acute pandemic, relating EHR-based after-hours work to telemedicine intensity (ie, percentage of care provided via telemedicine) and clinical load (ie, patient load per provider). Results A total of 2129 physicians were included in this study. During the acute pandemic, the volume of care provided via telemedicine significantly increased for all physicians, whereas patient volume decreased. When normalized by clinical load (ie, average appointments per day by average clinical days per week), telemedicine intensity was positively associated with work outside work across time periods. This association was strongest after the acute pandemic. Conclusions Taking physicians’ clinical load into account, physicians who devoted a higher proportion of their clinical time to telemedicine throughout various stages of the pandemic engaged in higher levels of EHR-based after-hours work compared to those who used telemedicine less intensively. This suggests that telemedicine, as currently delivered, may be less efficient than in-person–based care and may increase the after-hours work burden of physicians.more » « less
-
null (Ed.)The US CDC has recognized moist-heat as one of the most effective and accessible methods of decontaminating N95 masks for reuse in response to the persistent N95 mask shortages caused by the COVID-19 pandemic. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of smart technologies capable of ensuring proper decontamination conditions of hundreds of masks simultaneously. To tackle these challenges, we developed an open-source wireless sensor platform---VeriMask1 ---that facilitates per-mask verification of the moist-heat decontamination process. VeriMask is capable of monitoring hundreds of masks simultaneously in commercially available heating systems and provides a novel throughput-maximization functionality to help operators optimize the decontamination settings. We evaluate VeriMask in laboratory and real-scenario clinical settings and find that it effectively detects decontamination failures and operator errors in multiple settings and increases the mask decontamination throughput. Our easy-to-use, low-power, low-cost, scalable platform integrates with existing hospital protocols and equipment, and can be broadly deployed in under-resourced facilities to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks. We also memorialize the design challenges, guidelines, and lessons learned from developing and deploying VeriMask during the COVID-19 Pandemic. Our hope is that by reflecting and reporting on this design experience, technologists and front-line health workers will be better prepared to collaborate for future pandemics, regarding mask decontamination, but also other forms of crisis tech.more » « less
-
Cleaning work is a labor-intensive job that frequently exposes workers to substantial occupational hazards. Unfortunately, the outbreak of coronavirus disease 2019 (COVID-19) has increased the pressure on janitors and cleaners to meet the rising need for a safe and hygienic environment, particularly in grocery stores, where the majority of people get their daily necessities. To reduce the occupational hazards and fulfill the new challenges of COVID-19, autonomous cleaning robots, have been designed to complement human workers. However, a lack of understanding of the new generation of cleaning tools’ acceptance may raise safety concerns when they’re deployed. Therefore, a video-based survey was developed and distributed to 32 participants, aiming to assess human acceptance of the cleaning robot in grocery environments during the COVID-19 pandemic. Moreover, the effects of four factors (gender, work experience, knowledge, and pet) that may influence human acceptance of the cleaning robot were also examined. In general, our findings revealed a non-negative human acceptance of the cleaning robot, which is a positive sign of deploying cleaning robots in grocery stores to reduce the workload of employees and decrease COIVID-related anxiety and safety concerns of customers. Furthermore, prior knowledge of robotics was observed to have a significant effect on participants’ acceptance of the cleaning robot ( p = 0.039).more » « less
An official website of the United States government

