Editorial: Robotics, Autonomous Systems and AI for Nonurgent/Nonemergent Healthcare Delivery During and After the COVID-19 Pandemic
Bibliographic record
Abstract
Robotics, Autonomous Systems and AI for Nonurgent/Nonemergent Healthcare Delivery During and After the COVID-19 PandemicIn an emergency, all resources are dedicated to ensuring survival.This was also true of the COVID-19 pandemic, in which, in an effort to keep people safe, research was directed to understanding the virus better and ensuring vaccines were developed.However, the vulnerability of our health care system was also exposed when we saw nonurgent and nonemergent cases fall through the cracks while resources were directed at addressing the emerging health crisis.This special issue aimed to capture novel research directions and perspectives on technological advances that can be put in place to support health care: from novel techniques for diagnosing and treating COVID-19 and preventing spread, to tools and procedures that support other aspects of our health during and after the pandemic.A total of 33 articles by 180 leading authors were accepted from around the world, demonstrating a significant effort from the robotics and artificial intelligence (AI) communities to help reduce the impact of the pandemic on our overall health.The articles have been divided into general themes, as described below. PREVENTION OF COMMUNITY SPREADThe first line of defense in a pandemic is to prevent the spread of the virus within the community, and a large component of prevention is proper cleaning and disinfection.Towards this end, the paper by Nasirian et al. proposes an end-to-end coverage path planning (CPP) method using a novel graph representation of the environment that can generate a continuous and uninterrupted collision-free path for an autonomous mobile robot.The proposed method is able to generate an optimal path that can reduce the disinfection task completion time and cost through shorter travel distances and a smaller number of turns than other approaches.Complementary to disinfection are other ways to prevent the spread of the virus.The work by Michelin et al. aimed to prevent people from touching their faces.A convolutional neural network (CNN) algorithm using data from an inertial measurement unit (IMU) at the wrist was able to predict when a person was about to touch their face.The system then provided sensory feedback in various forms, with vibrotactile feedback providing the fastest response, the best success rate, and the best user experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.028 | 0.026 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".