Herein, how robotic and autonomous systems and smart wearables complement and support healthcare delivery and the healthcare staff during the COVID‐19 pandemic are presented. For instance, robotic and telerobotic systems significantly reduce the risk of infectious disease transmission to frontline healthcare workers by making it possible to triage, evaluate, monitor, and treat patients from a safe distance. Various examples of where the medical, engineering, and science communities come together to aid the healthcare system, healthcare workers, and society during the current crisis are presented. The goal is to encourage an interdisciplinary dialog so that ethical, practical, and beneficial technological solutions are found to effectively tackle this and similar crises.

Worldwide, at the time this article was written, there are over 127 million cases of patients with a confirmed link to COVID-19 and about 2.78 million deaths reported. With limited access to vaccine or strong antiviral treatment for the novel coronavirus, actions in terms of prevention and containment of the virus transmission rely mostly on social distancing among susceptible and high-risk populations. Aside from the direct challenges posed by the novel coronavirus pandemic, there are serious and growing secondary consequences caused by the physical distancing and isolation guidelines, among vulnerable populations. Moreover, the healthcare system’s resources and capacity have been focused on addressing the COVID-19 pandemic, causing less urgent care, such as physical neurorehabilitation and assessment, to be paused, canceled, or delayed. Overall, this has left elderly adults, in particular those with neuromusculoskeletal (NMSK) conditions, without the required service support. However, in many cases, such as stroke, the available time window of recovery through rehabilitation is limited since neural plasticity decays quickly with time. Given that future waves of the outbreak are expected in the coming months worldwide, it is important to discuss the possibility of using available technologies to address this issue, as societies have a duty to protect the most vulnerable populations. In this perspective review article, we argue that intelligent robotics and wearable technologies can help with remote delivery of assessment, assistance, and rehabilitation services while physical distancing and isolation measures are in place to curtail the spread of the virus. By supporting patients and medical professionals during this pandemic, robots, and smart digital mechatronic systems can reduce the non-COVID-19 burden on healthcare systems. Digital health and cloud telehealth solutions that can complement remote delivery of assessment and physical rehabilitation services will be the subject of discussion in this article due to their potential in enabling more effective and safer NMSDK rehabilitation, assistance, and assessment service delivery. This article will hopefully lead to an interdisciplinary dialogue between the medical and engineering sectors, stake holders, and policy makers for a better delivery of care for those with NMSK conditions during a global health crisis including future pandemics.

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

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Herein, a semiautonomous robot control system for mandible reconstruction surgery is proposed. To reconstruct a segmental defect of the mandible caused by cancerous tissue, a piece of matched fibula bone is often segmented and used to replace the removed mandible section. Herein, to provide guidance to the surgeon during fibula segmentation according to the reconstruction surgical plan and improve the fibula bone cutting accuracy, an admittance‐controlled robotic assistant incorporating 3D augmented reality (AR) visualization and haptic virtual fixtures (VFs) is proposed. The admittance controller is used to reduce the surgeon's hand tremor. VF and AR are used to provide haptic and visual guidance to the surgeon, respectively. A feasibility study is conducted through a comparison of fibula osteotomies when performed with image‐guided surgery, AR‐guided surgery, VF‐guided robot‐assisted surgery, and AR‐ and VF‐guided robot‐assisted surgery. Experimental results show the effectiveness of the proposed admittance‐controlled robotic assistant with AR and VF compared with the other three methods. The proposed method is found to be able to increase precision of the osteotomized segments with a lower average linear variation of 1.04 ± 0.79 mm and a lower average angular variation of 1.83 ± 1.85° compared with the virtual preoperative plan.

Introduction A novel telerehabilitation service provides wayfinding and self-management advice to persons with neurological, musculoskeletal, or coronavirus disease 2019 related rehabilitation needs. Method We utilized multiple methods to evaluate the impact of the service. Surveys clarified health outcomes (quality of life, self-efficacy, social support) and patient experience (telehealth usability; general experience) 3-months post-call. We analysed associations between, and within, demographics and survey responses. Secondary analyses described health care utilization during the first 6 months. Results Sixty-eight callers completed the survey (42% response rate). Self-efficacy was significantly related to quality of life, interpersonal support and becoming productive quickly using the service. Becoming productive quickly was significantly related to quality of life. Education level was related to ethnicity. Survey respondents’ satisfaction and whether they followed the therapist's recommendations were not significantly associated with demographics. Administrative data indicated there were 124 callers who visited the emergency department before, on, or after their call. The average (SD) frequency of emergency department visits before was 1.298 times (1.799) compared to 0.863 times (1.428) after. Discussion This study offers insights into the potential impact of the telerehabilitation service amidst pandemic restrictions. Usability measurements showed that callers were satisfied, corroborating literature from pre-pandemic contexts. The satisfaction and acceptability of the service does not supplant preferences for in-person visits. The survey sample reported lower quality of life compared with the provincial population, conflicting with pre-pandemic research. Findings may be due to added stressors associated with the pandemic. Future research should include population-level comparators to better clarify impact.