skip to main content


The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 5:00 PM ET until 11:00 PM ET on Friday, June 21 due to maintenance. We apologize for the inconvenience.

Title: Review: How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults With Neuro-Musculoskeletal Conditions?
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.  more » « less
Award ID(s):
2031594 2037878
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Frontiers in Robotics and AI
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 
    more » « less
  2. null (Ed.)
    The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above. 
    more » « less
  3. Background

    COVID-19 has severely impacted health in vulnerable demographics. As communities transition back to in-person work, learning, and social activities, pediatric patients who are restricted to their homes due to medical conditions face unprecedented isolation. Prior to the pandemic, it was estimated that each year, over 2.5 million US children remained at home due to medical conditions. Confronting gaps in health and technical resources is central to addressing the challenges faced by children who remain at home. Having children use mobile telemedicine units (telerobots) to interact with their outside environment (eg, school and play, etc) is increasingly recognized for its potential to support children’s development. Additionally, social telerobots are emerging as a novel form of telehealth. A social telerobot is a tele-operated unit with a mobile base, 2-way audio/video capabilities, and some semiautonomous features.


    In this paper, we aimed to provide a critical review of studies focused on the use of social telerobots for pediatric populations.


    To examine the evidence on telerobots as a telehealth intervention, we conducted electronic and full-text searches of private and public databases in June 2010. We included studies with the pediatric personal use of interactive telehealth technologies and telerobot studies that explored effects on child development. We excluded telehealth and telerobot studies with adult (aged >18 years) participants.


    In addition to telehealth and telerobot advantages, evidence from the literature suggests 3 promising robot-mediated supports that contribute to optimal child development—belonging, competence, and autonomy. These robot-mediated supports may be leveraged for improved pediatric patient socioemotional development, well-being, and quality-of-life activities that transfer traditional developmental and behavioral experiences from organic local environments to the remote child.


    This review contributes to the creation of the first pediatric telehealth taxonomy of care that includes the personal use of telehealth technologies as a compelling form of telehealth care.

    more » « less
  4. Background

    Rural and remote communities were especially vulnerable to the COVID-19 pandemic due to the availability and capacity of rural health services. Research has found that key issues surrounded (1) the lack of staff, (2) the need for coordinated health services, and (3) operational and facility issues. Similarly, research also confirms that irrespective of hospital capacity issues existing during crisis, compared to urban communities, rural communities typically face poorer access to health services. Telehealth programs have long held promise for addressing health disparities perpetuated by inadequate health care access. In response to the current COVID-19 pandemic, Adventist Health Saint Helena Hospital, a rural hospital in northern California, urgently worked to expand telehealth services. However, as Adventist Health Saint Helena Hospital is the longest-serving rural hospital in the state of California, administrators were also able to draw on experiences from the pandemic of 1918/1919. Understanding their historically rural and heavily Latino populations, their telehealth approach was coupled with cultural approaches for prioritizing socially responsive and equitable access to health services.


    This study aimed to present one rural community’s holistic sociotechnical response to COVID-19 in redesigning their health care delivery approach. Redesign efforts included the expansion of digital health services coupled with county-wide collaborations for nondigital mobile health centers, testing, and vaccination clinics to meet the needs of those with limited digital access and language barriers.


    We present data on telehealth services for maintaining critical care services and a framework on the feasibility of private-public partnerships to address COVID-19 challenges.


    In this paper, we provide a critical review of how a rural hospital adapted its health care approach to incorporate telehealth services and distance services to meet the needs of a diverse population.


    This paper contributes empirical data on how rural communities can use telehealth technologies and community partnerships for a holistic community approach to meet health needs during a natural disaster.

    Conflicts of Interest

    None declared.

    more » « less
  5. Abstract Introduction

    Utilization of telemedicine for health care delivery increased rapidly during the coronavirus disease 2019 (COVID‐19) pandemic. However, physical examination during telehealth visits remains limited. A novel telerehabilitation system—The Augmented Reality‐based Telerehabilitation System with Haptics (ARTESH)—shows promise for performing synchronous, remote musculoskeletal examination.


    To assess the potential of ARTESH in remotely examining upper extremity passive range of motion (PROM) and maximum isometric strength (MIS).


    In this cross‐sectional pilot study, we compared the in‐person (reference standard) and remote evaluations (ARTESH) of participants' upper extremity PROM and MIS in 10 shoulder and arm movements. The evaluators were blinded to each other's results.


    Participants underwent in‐person evaluations at a Veterans Affairs hospital's outpatient Physical Medicine and Rehabilitation (PM&R) clinic, and underwent remote examination using ARTESH with the evaluator located at a research lab 30 miles away, connected via a high‐speed network.


    Fifteen participants with upper extremity pain and/or weakness.


    Not applicable.

    Main Outcome Measures

    Inter‐rater agreement between in‐person and remote evaluations on 10 PROM and MIS movements and presence/absence of pain with movement was calculated.


    The highest inter‐rater agreements were noted in shoulder abduction and protraction PROM (kappa (κ) = 0.44, confidence interval (CI): −0.1 to 1.0), and in elbow flexion, shoulder abduction, and shoulder protraction MIS (κ = 0.63, CI: 0 to 1.0).


    This pilot study suggests that synchronous tele‐physical examination using the ARTESH system with augmented reality and haptics has the potential to provide enhanced value to existing telemedicine platforms. With the additional technological and procedural improvements and with an adequately powered study, the accuracy of ARTESH‐enabled remote tele‐physical examinations can be better evaluated.

    more » « less