|
 
 |
| REVIEW ARTICLE |
|
| Year : 2022 | Volume
: 8
| Issue : 1 | Page : 30 |
|
Telepresence robots to support telehealth during pandemics
Chongdan Pan1, Mingzhong Wang2, Pradeep K Ray1
1 Centre For Entrepreneurship, University of Michigan Joint Institute, Shanghai Jiao Tong University, Shanghai, China
2 School of Science, Technology and Engineering, University of the Sunshine Coast, Maroochydore, Australia
| Date of Submission | 03-Apr-2022 |
| Date of Decision | 20-Aug-2022 |
| Date of Acceptance | 22-Aug-2022 |
| Date of Web Publication | 07-Dec-2022 |
Correspondence Address:
Mingzhong Wang
School of Science, Technology and Engineering - ML12, University of the Sunshine Coast, Locked Bag 4, Maroochydore, QLD 4558
Australia
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/digm.digm_15_22
As the world becomes older, sustainable healthy aging becomes an important goal of social development. Robotic technologies have been widely considered an effective solution to reduce the labor demand and cost in aged care, thus providing satisfiable services to the elderly while keeping the cost low. The global outbreak of corona virus disease 2019 (COVID-19) has strengthened this trend when it impacted the elderly most because (1) the elderly was generally the most vulnerable population group to pandemics, and (2) the resources available to the elderly significantly declined due to lockdown and quarantines. The observations and experience from COVID-19 inspired us to consider the impact of pandemics on sustainable healthy aging, which was largely missing in existing work, leading to the study of the use of robots in general and telepresence robots in specific to aid sustainable healthy aging. The methodology of systematic review is applied to retrieve and analyze the articles published in nine databases between 2010 and 2020. Based on the review, the paper classifies the applications of robots in pandemics into four main categories, including healthcare, social support, education, and manufacturing. Further analysis of these applications revealed the missing features and challenges in applying them to healthy aging. The discoveries and findings in this paper provide practical guidelines for the future design and development of (telepresence) robots.
Keywords: Aged care, Pandemic, Telehealth, Telepresence robot
How to cite this article:
Pan C, Wang M, Ray PK. Telepresence robots to support telehealth during pandemics. Digit Med 2022;8:30 |
| Introduction | |  |
The world population keeps on growing older rapidly due to continuous falling fertility rates along with rising expectancy. The United Nations forecasted that one in six people in the world will be over the age of 65 by 2050, up from one in eleven in 2019.[1] Moreover, the population aging will be unbalanced with four regions, Northern Africa and Western Asia, Central and Southern Asia, Latin America and the Caribbean, and Eastern and Southeastern Asia, to have their share of the aged population more than double between 2019 and 2050. The increasing ratio of the aging population leads to the inevitable pressure on the sustainability of the welfare and healthcare accessible to them. Therefore, there is a growing demand for machine-assisted technologies in caring for aged people, including falls detection systems, remote health monitoring, smart home technology, and video surveillance.[2],[3],[4]
Various researches and studies have been carried out to explore the solutions, including the use of wearable devices and telepresence robots,[5] to tame the challenges in healthy aging. However, most existing work has an implicit assumption that the care of the aged population happens in the context of normal societal conditions, without considering the impact of drastic events, such as corona virus disease 2019 (COVID-19), on healthy aging.
As we have experienced in 2020, global pandemics, such as COVID-19, can dramatically change the landscape of healthy aging as societies are put into lockdown and the medical supplies are quickly drained up. On December 30, 2020, the WHO reported that COVID-19 has infected about 80.8 million people in 222 countries, areas, or territories and caused about 1.8 million deaths globally. The world has seen similar virus outbreaks in SARS around 2003 on a much smaller scale. Substantial resources are required to be invested to care for the infected people in hospitals and outside and to prevent healthy people from being infected. Besides, extreme measures, including quarantines and lockdowns, are required to contain such an outbreak, leading to severe damage to the health, economics, psychology, and education systems of the societies and communities worldwide due to the closure of factories, international and domestic trade, businesses, schools, colleges, and universities for significant periods.
Unfortunately, the aged population is generally the most vulnerable group in most pandemic outbreaks, such as COVID-19.[6] It was reported that the risk of dying from COVID-19 increases significantly with age, with the fatality ratio increasing from nearly zero for people under the age of 50 to 0.5% for people in their 50s and early 60s and then jumping to more than 11% for people more than mid-seventies. In the United States, more than 80% of death due to COVID-19 happens in the elderly (above 65 years). This population group is extremely significant for the future due to the aging population all over the world.
Therefore, this paper takes a different perspective from existing research by investigating the possible use of telepresence robots to aid the healthy aging in emergencies, such as pandemic outbreaks. This paper conducts a systematic literature review of current research on the applications of telepresence robots during the pandemics and explores their connections with healthy aging. The review helps find the gaps and trends in this area, thus providing the practitioners with information and insight for future studies.
The paper is organized as follows: Section 2 explains the research methodology. Section 3 presents the findings and analysis of the systematic review. Section 4 and 5 summarize the limitations and implications of this study. Finally, Section 6 concludes the paper.
| Research Method | | |
As the key component of this study is a systematic review, we customized the guidelines for systematic reviews developed by Kitchenham[7] which consists of four steps to carry out a literature review: identification of resources, selection of studies, data extraction and synthesis, and data analysis.
Identification of resources
The first step of this research was to systematically search for relevant publications. Nine electronic databases, including CINAHL, ERIC, EMBASE, IEEE Xplorer, MEDLINE, Proquest Central, PubMed, PsycINFO, and Web of Science, were selected for the study as they are the most authentic, recognized, and accepted platforms in relevant communities. The fields considered in the search include publication titles, keywords, abstracts, or full texts. The search was limited to the publication dates ranging from 2010 to 2020, both inclusive.
The following search phrase was used while searching each online database: “robot AND (COVID OR pandemic OR quarantine OR teleconference OR epidemic OR telemedicine OR telepresence)”.
A total of 3946 articles were initially retrieved from all databases. [Figure 1] shows the shares of articles by the databases. 2940 articles remained after 1006 duplicates with the same title were removed.
Selection of studies
The objective of this step was to filter the search results to get only relevant work for the applications of (telepresence) robots in pandemics. We first assessed the eligibility of articles by reviewing their titles, keywords, and abstracts. If an article met any of the following criteria, it was excluded from the study. As a result, 2828 articles were removed at the end of this stage.
- Did not talk about the application or functionality of robots.
- Had no potential use in pandemics.
- Was in languages other than English.
- Was not peer-reviewed.
- Was not available online.
We then reviewed the full texts of the remaining 112 articles to check their eligibility. As a result, 54 articles were selected as they provide adequate information to demonstrate the potential use of robots in COVID-19-like pandemics.
Thereafter, we repeated the previous process on the references of the selected articles to minimize the potential of missing any relevant work. As a result, seven additional articles were found and included in the study. Finally, 61 articles were selected for the systematic review. [Figure 2] illustrates the selection process in our study with the use of guidelines defined in Preferred Reporting Items for Systematic Reviews and Meta-Analyses.[8]
Data extraction and synthesis
In this step, the following information was extracted from the selected articles:
- Type of article.
- Continent and countries.
- Application scenarios during the pandemic.
- The challenge, drawback, and future improvement of the robots.
Data analysis
This step categorized, summarized, and analyzed the data collected from the selected articles.
| Results | | |
Article types
We categorized the selected articles into four types:
- Data-based study: The work in this category mainly reports the conclusions and findings by analyzing the usage data collected in the trials of robots.
- Interview-based study: This category is similar to the data-based study, but the data on focus are collected by interviewing users.
- Product report: The work in this category provides preliminary reports as case studies on the design, build, and potential use of robots in development.
- News report: This category mainly reports the robots' specific deployment and usage during the pandemics. These studies reveal the social acceptance and attitudes to robots.
As shown in [Figure 3], the number of articles in each category is close to each other, with product reports and database studies having 26% (16/61) each, followed by news reports of 25% (15/61) and interview-based studies of 23% (14/61).
Demographics
As shown in [Figure 4], 54% (33/61) articles were from North America, 31% (19/61) were from Asia, and 15% (9/61) were from Europe. Asia has the highest number of product reports, and North America has the most news reports because of its current COVID-19 outbreak.
Application types
Most applications of robots in the selected articles can be termed as some sorts of telepresence robots.[9],[10] They are generally 1–1.6 m high, equipped with a remote control system, a camera, a tablet, and a mobile system. The users of telepresence robots can be categorized as controllers and occupants. The controllers can be doctors, caregivers, or anyone who controls the robots, while the occupants include patients or the elderly, who are the targets of services. The remote control system enables controllers to operate robots remotely. The camera and tablet provide visual and audio channels for controllers to supervise the environment and communicate with occupants. With the mobile system, a telepresence robot can move and act as the avatar of a controller.
Generally, the robot and the controller are connected by the Internet. The controller can use computers or smartphones to issue instructions to the robot as well as watch videos recorded by the robot's camera. Meanwhile, the controller's image will also be shown to the occupant on the tablet of the robot.
Telepresence robots can provide various types of services, such as telecheckups, telemonitoring by recording physiological data, teleconsultation, and teletreatment.[11],[12],[13],[14],[15] Depending on the usage, the robots reported in the selected articles can be classified into four categories regarding the application scenarios in pandemics.
- Healthcare application: The robots are used in healthcare environments.
- Social support application: The robots are used in the daily life.
- Educational application: The robots are used for education-related activities.
- Industrial application: The robots are used in the manufacturing environment to aid the production of medical resources.
[Figure 5] shows the distribution of the application categories. Applications in healthcare have the highest share of 52% (32/61), which is followed by social support at 31% (19/61).
As this paper focuses on the applications of robots in healthcare, the applications in other categories are not included even though they can contribute indirectly to healthcare.
| Robots for Healthcare | | |
Applications
Robots for healthcare are built for different purposes, such as medical diagnoses, medical procedures, patient care, and drug stores. As the paper focuses on applying robots to support telehealth during pandemics, we focus on the applications that help humans fight against COVID-19 in this section. As majority of literature on robots in healthcare is not directly linked to the care of the elderly population, but this subsection only provides a general introduction to healthcare and leaves the discussions on the elderly people in the next subsection.
- Hospital robots: Many medical professionals were infected with COVID-19 due to close contact with patients. Telepresence robots can help with the delivery of medical services while maintaining social distances.[16] Tele-Robotic Intelligent Nursing Assistant (TRINA) has been trialed to fight against the 2014 Ebola outbreak and COVID-19.[17] TRINA allows doctors and nurses to virtually tour the rooms, aid telepresence consultations with patients, and monitor their vital signs of patients, thus avoiding unnecessary exposure to the virus. With robots such as TRINA, frontline medical workers could work more like system administrators.[18]
- Intensive care unit (ICU) robots: During COVID-19, life and death somewhat are tied with the availability of the ICU, which is unfortunately very scarce regarding the number of patients. Given the acknowledged shortage of intensive care specialists, the alternative is to have one intensive care physician supervise multiple bedside nurses with real-time and two-way communication support,[19] and ICU robots are helpful for this purpose. ICU robots, unlike old-fashioned telephonic communication, also enable care providers to remotely observe patients' vital signs and examine medical charts.[20] An ICU robot can also enable the remote collaboration of a multidisciplinary team without standing beside a patient's bedside.[21],[22] According to feedback from the clinic tests, more than 75% of patients and medical professionals agree that ICU robots can provide satisfying medical service.
- Care robots with the Internet of things (IoT): The pandemic prevents people, especially the elderly and disabled from receiving regular medical treatment and care due to fear, quarantine policies, or closure of clinics. In comparison with the medical robots in hospitals, care robots can be used at home. Care robots can be used to provide spiritual and physical care services, as well as telediagnosis, with various add-on equipment. A remote auscultation robot enables the doctor to remotely use the stethoscope with a diaphragm and a headset.[23] The robots can transmit the signals with high resolutions so that the doctor can examine the circulatory and respiratory systems as well as the gastrointestinal system. Some care robots can be connected to the IoT as an intermediate station to work together with sensors, cell phones, TV, etc.[24] When the occupants require telemedicine services, the doctor can use the robot to set up connections with other medical equipment in patients' homes for a more detailed diagnosis. Care robots also perform well in providing interpersonal communication for people in isolation.[25] Various care robots have already been deployed in many countries, and it is a good solution to give consolation to people isolated at home.[26],[27]
- Public use: In public areas, robots are used for various tasks, including bathing surfaces with radiation, sanitizing floors, scanning for fevers, spewing antimicrobial gas, and enforcing mask wearing. Xenex Disinfection Services LLC has developed the broad-spectrum, high-intensity ultraviolet light technology which enables one robot to disinfect a room in 2 min.[28] Besides disinfection, robots can help with the remote detection of suspected COVID-19 patients.[29] With a sophisticated camera and contactless temperature sensors, the robot can detect COVID-19 symptoms, including fever, cough, heart rate, temperature, and humidity. In comparison with human detectors, a robot can use its camera to detect multiple people simultaneously and capture the images and results.
Challenges
Although the robots have been widely tested and experimented with, there are still many challenges and barriers to be dealt with before they could be practically adopted in healthcare for the elderly, especially in extreme cases of pandemics.[9],[30],[31],[32] Therefore, this section analyzes and summarizes the articles of healthcare category selected in the systematic review process for the common issues and challenges as well as recommended solutions. [Table 1] provides an overall view of the problems and solutions, while the rest of the section explains the details of each problem. | Table 1: Common problems and solutions for applying robots in healthcare for the elderly.
Click here to view |
Battery life
Although battery life is a common issue for all mobile devices, it is more challenging for telepresence robots as they are equipped with both electrical and mechanical components, which drain more power supply. For example, Giraff is a telepresence robot that has been tested in homes of elderly people [Table 1].[45] However, experiments showed that Giraff can only work for 2 h and it needs another 2 h to be fully charged,[34] which was also the concern of many primary users in the experiments. Adding a backup battery to the robot may be a solution to extend the battery life.[12] Moreover, it is common that elder users forget to recharge robots manually and would be disappointed when they need to use them with low batteries. Many developers have adopted the idea of automatic charging, in which a robot can return to its docking station automatically for recharging when the battery is low and received positive feedback from users.[33]
Bulky size
In the case of elder users, they may touch, hold, or lean against robots during their use. Thus, in general, a telepresence robot needs a stable chassis, which is usually large, to prevent it from falling. However, the big size comes with inconvenience and safety concerns.[35],[36] It can increase the possibility that a robot hits an obstacle or causes an accident. In places such as wards or a small rooms, the robot may also have space conflict with other medical devices and healthcare providers. Therefore, a good design with the use scenarios considered is important for telepresence robots.
Poor night performance
A study has shown that physicians may favor robots over the telephone during night-time shifts because of the convenience of face-to-face communication with both aged-care residents and nurses.[37] However, robots, in general, cannot achieve the same performance as in the daytime. The main reason is the lack of light, which makes it inconvenient for medical professionals to view the surroundings with the robots' camera. The solutions include adding a flashlight or night vision function to robots. However, the light may disturb others and the night vision function requires more sophisticated driving skills for nurses.
Unstable signal transmission
The stability and efficiency of network transmission are extremely critical for the applications of telepresence robots in healthcare.[38],[39] The instability may cause delays or unavailability in remote control and video call, resulting in serious accidents. For example, a robot may keep rushing forward and hit someone because it does not receive a stop instruction. Therefore, developers must make the control system as stable as possible and provide emergency control functions. Some studies have proposed different architectures for stable signal communication, but they still need time to prove their practicability in long-range control.
Limited functionality
The functionality of healthcare robots remains very limited as they highly rely on the instructions from the controller but less autonomous or intelligent. Most robots have the high computing power and large storage capacity as they are equipped with new models of tablets or processing units, but they are rarely used to collect, record, and analyze the aged users health data with state-of-the-art solutions in machine learning and artificial intelligence.[46] A smart diagnosis architecture has been proposed with four stages to integrate the computing power together with remote control[40]:
- The robot approaches the users and takes measurements through the comprehensive medical sensors consisting of physiological, pathological, and in-body imaging.
- All data collected are fed to a local anomaly detection module that derives critical information mostly related to abnormalities of the measurements.
- The job of this cognitive engine is to relate this set of anomalies to possible disease and send the report to doctors/hospitals for further analysis.
- The robot can automatically search the Internet for related information for future treatment.
Transportation function
The high mobility of robots has not been fully exploited yet. The powerful chassis of robots can help transport elderly user, such as a wheelchair, when they need such support.[33],[38] Robots should be able to monitor the patients' vital signals and provide essential life support during transportation.
Lack of voice control
The feedback from an experiment about telepresence robots shows that many users believe that voice control can improve the user experience, especially for care robots.[41] As primary occupants of remote care robots are usually in bed or not very familiar with using technical equipment, voice control enables them to enjoy robots' services without making physical contact or dealing with the dazzling user interface. For busy medical professionals, the function allows them to give instruction to the robot and do their work simultaneously. Voice control has been proved useful in mobile phones; it can also make robots more user-friendly.
Lack of trust
The challenge of trust comes in two folds. On the one hand, it is about the disbelief of users that a robot can play a functional role in their lives. A trial of robots in the ICU reported that all users (100%) felt more confident caring for the patient with the supervising physician observing the visit via the ICU robot, compared to only 10% of the nonusers.[20] Similar findings also happened in the aged-care settings.[43] On the other hand, it is about the belief of nonevil actions by operators or companies which have access to ambient data about users. The study shows that elderly users commonly have concerns about what data is collected, where is it stored, and who has access to it.[44] If they feel they have no control of their own data, they are more likely to hold off. In addition, the study also suggests that people are less confident with the controller when the controller is unseen.[42] Therefore, how to attract, educate, and encourage both caregivers and elderly people to use robots remains a priority.
In summary, the applications of robots in healthcare and aged care are still in its infancy, with most applications as trials and pilots. The robots designed for general healthcare does not equal the needs of the elderly people. It is recommended to form a standard organization for “robots for aged people”, caring the voices of all stakeholders, including users, practitioners, academia, and industries.
| Discussion | | |
As the world becomes older, sustainable healthy aging becomes an important goal of social development. The global outbreak of COVID-19 inspired us to consider the impact of pandemics on sustainable healthy aging, leading to the study of the use of robots in general and telepresence robots in specific to aid sustainable healthy aging. The systematic review in this paper helped categorize the applications of robots in pandemics into four main types, including healthcare, social support, education, and manufacturing. Further analysis of these applications revealed the missing features and challenges in applying them to healthy aging, thus providing practical guidelines for future design and development of (telepresence) robots.
Implications to practice
The review helps readers gain a comprehensive view of robotic applications in fighting against COVID-19. Besides, we analyzed the feedback and summarized the missing features of these applications. The feedback and analysis were from different perspectives, including user experience, social acceptance, and design issues. The suggestions provided in this paper will help practitioners in the field to design and build more practical telepresence robots for the aged population in the future.
Limitations
The study is limited to the keywords chosen in the literature search. Therefore, there are other robots in use but not included in this review. Moreover, the full-text content of many articles was not available online, thus being ignored.
The study only covers the publications between 2010 to September 2020. As COVID-19 has occurred in less than a year, many valuable applications and work were not published, thus not being covered.
Furthermore, among all articles covered, only a few provide comprehensive introductions and analyses of specific robots which have been already put into use. As a result, the review can only summarize universal ideas about them, which were based on fragmented information from many articles.
| Conclusion | | |
This paper mainly consists of a systematic review on the use of robots in general and telepresence robots in specific in the fight against pandemics such as COVID-19. The aim is to find how telepresence robots can help the elderly in case of pandemics and what are the gaps between existing products and users' demands, thus guiding the future design of telepresence robots and boosting the welfare of the elderly for healthy aging. We classified these robots into four categories based on their applications and usage. The review highlights the robots' use scenarios during pandemics and summarizes the features on demand.
Financial support and sponsorship
Nil.
Conflicts of interest
Pradeep K. Ray is an Editorial Board Member of the journal. The article was subject to the journal's standard procedures, with peer review handled independently of this editor and his research groups.
| References | | |
| 1. | |
| 2. | Miskelly FG. Assistive technology in elderly care. Age Ageing 2001;30:455-8.  |
| 3. | Sapci AH, Sapci HA. Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: Systematic review. JMIR Aging 2019;2:e15429. |
| 4. | Marston HR, Samuels J. A review of age friendly virtual assistive technologies and their effect on daily living for carers and dependent adults. Healthcare (Basel) 2019;7:49. |
| 5. | Ray PK, Nakashima N, Ahmed A, Ro S, Soshino. Mobile Technologies for Delivering Healthcare in Remote, Rural or Developing Regions. Herts, United Kingdom: IET; 2020. 291-304. |
| 6. | Mallapaty S. The coronavirus is most deadly if you are older and male – New data reveal the risks. Nature 2020;585:16-7. |
| 7. | Kitchenham B. Procedures for performing systematic reviews. Kelee, UK: Department of Computer Science, Keele University; 2004. p. 1-26. |
| 8. | Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Med 2009;6:e1000097. |
| 9. | Kerr D, Serrano J, Ray P. The role of a disruptive digital technology for home-based healthcare of the elderly: Telepresence robot. Digit Med 2018;4:173. [Full text] |
| 10. | Tsui KM, Desai M, Yanco HA, Uhlik C. Exploring Use Cases for Telepresence Robots. 2011 6 th ed. ACM/IEEE Int Conf Hum-Robot Interact HRI. 2011. p. 11-18. |
| 11. | Burke RV, Berg BM, Vee P, Morton I, Nager A, Neches R, et al. Using robotic telecommunications to triage pediatric disaster victims. J Pediatr Surg 2012;47:221-4. |
| 12. | Bugtai NT, Ong AP, Angeles PB, Cervera JK, Ganzon RA, Villanueva CA, et al. Development of a telepresence robot for medical consultation. In: Whulanza Y, Supriadi S, Sahlan M, editors. Biomed Eng Recent Prog Biomater Drugs Dev Med Devices. New York: AIP Conference Proceedings; 2017. |
| 13. | Lepage P, Létourneau D, Hamel M, Brière S, Corriveau H, Tousignant M, et al. Telehomecare telecommunication framework – From remote patient monitoring to video visits and robot telepresence. 2016 38 th Annu Int Conf IEEE Eng Med Biol Soc EMBC. 2016. p. 3269-72. |
| 14. | Aggarwal S, Gupta D, Saini S. A Literature Survey on Robotics in Healthcare. 2019 4 th Int Conf Inf Syst Comput Netw ISCON. 2019. p. 55-8. |
| 15. | Carranza KA, Day NJ, Lin LM, Ponce AR, Reyes WR, Abad AC, et al. Akibot: A Telepresence Robot for Medical Teleconsultation. 2018 IEEE 10 th Int Conf Humanoid Nanotechnol Inf Technol Control Environ Manag HNICEM. 2018. p. 1-4. |
| 16. | Jitheesh P, Fasal K, Mohammed Ameen T, Athira PC, Madeena S, Arunvinodh C. Telepresence Robot Doctor. 2016 Online Int Conf Green Eng Technol IC-GET. 2016. p. 1-4. |
| 17. | |
| 18. | |
| 19. | Vespa P. Robotic telepresence in the intensive care unit. Crit Care 2005;9:319-20. |
| 20. | Becevic M, Clarke MA, Alnijoumi MM, Sohal HS, Boren SA, Kim MS, et al. Robotic telepresence in a medical intensive care unit – Clinicians' perceptions. Perspect Health Inf Manag 2015;12:1c. |
| 21. | Sucher JF, Todd SR, Jones SL, Throckmorton T, Turner KL, Moore FA. Robotic telepresence: a helpful adjunct that is viewed favorably by critically ill surgical patients. Am J Surg 2011;202:843-7. |
| 22. | Reynolds EM, Grujovski A, Wright T, Foster M, Reynolds HN. Utilization of robotic “remote presence” technology within North American intensive care units. Telemed J E Health 2012;18:507-15. |
| 23. | Falleni S, Filippeschi A, Ruffaldi E, Avizzano CA. Teleoperated multimodal robotic interface for telemedicine: A case study on remote auscultation. 2017 26 th IEEE Int Symp Robot Hum Interact Commun RO-MAN. 2017. p. 476-82. |
| 24. | Hai ND, Nam LH, Thinh NT. Remote Healthcare for the Elderly, Patients by Tele-Presence Robot. 2019 Int Conf Syst Sci Eng ICSSE. 2019. p. 506-10. |
| 25. | Brière S, Boissy P, Michaud F. In-home telehealth clinical interaction using a robot. 2009 4 th ACM/IEEE Int Conf Hum-Robot Interact HRI. 2009. p. 225-6. |
| 26. | Zhou B, Wu K, Lv P, Wang J, Chen G, Ji B, et al. A new remote health-care system based on moving robot intended for the elderly at home. J Healthc Eng 2018;2018:4949863. |
| 27. | Su C, Samani H, Yang C, Fernando ON. Doctor Robot with Physical Examination for Skin Disease Diagnosis and Telemedicine Application. 2018 Int Conf Syst Sci Eng ICSSE. 2018. p. 1-6. |
| 28. | |
| 29. | |
| 30. | Shishehgar M, Kerr D, Blake J. A systematic review of research into how robotic technology can help older people. Smart Health 2018;7:1-18. |
| 31. | Bogue R. Robots in a contagious world. Ind Robot Int J Robot Res Appl 2020;47:673-42. |
| 32. | Smith C, Gregorio M, Hung L. Facilitators and barriers to using telepresence robots in aged care settings: A scoping review protocol. BMJ Open 2021;11:e051769. |
| 33. | Laniel S, Letourneau D, Labbe M, Grondin F, Polgar J, Michaud F. Adding navigation, artificial audition and vital sign monitoring capabilities to a telepresence mobile robot for remote home care applications. IEEE Int Conf Rehabil Robot 2017;2017:809-11. |
| 34. | Cesta A, Cortellessa G, Orlandini A, Tiberio L. Addressing the Long-Term Evaluation of a Telepresence Robot for the Elderly. Portugal:4th International Conference on Agents and Artificial Intelligence. 2012. |
| 35. | Lee H, Kim S, Kim J, Lee J, Byun A, Ryu H, et al. Assessment of User Needs for the Teleconsultation Robot and the Bedside Robot Using Simulation. 2017 Int Conf Intell Inform Biomed Sci ICIIBMS. 2017. p. 126-8. |
| 36. | Gawron O, Keller L, Huffstadt K, Müller NH. Effect of Height in Telepresence Robots on the Users' Spatial Awareness. In: Zaphiris P, Ioannou A, editors. Learn Collab Technol Games Virtual Environ Learn. Cham: Springer International Publishing; 2021. p. 268-77. |
| 37. | Bettinelli M, Lei Y, Beane M, Mackey C, Liesching TN. Does robotic telerounding enhance nurse-physician collaboration satisfaction about care decisions? Telemed J E Health 2015;21:637-43. |
| 38. | |
| 39. | Koceski S, Koceska N. Evaluation of an assistive telepresence robot for elderly healthcare. J Med Syst 2016;40:121. |
| 40. | Sinharay A, Pal A, Banerjee S, Banerjee R, Bandyopadhyay S, Deshpande P, et al. A Novel Approach to Unify Robotics, Sensors, and Cloud Computing Through IoT for a Smarter Healthcare Solution for Routine Checks and Fighting Epidemics. In: Mandler B, MarquezBarja J, Campista ME, Caganova D, Chaouchi H, Zeadally S, et al., editors. Internet Things Iot Infrastruct Pt I. 2016. p. 536-42. |
| 41. | Tsui KM, Flynn K, McHugh A, Yanco HA, Kontak D. Designing speech-based interfaces for telepresence robots for people with disabilities. IEEE Int Conf Rehabil Robot 2013;2013:6650399. |
| 42. | Kraft K, Smart WD. Seeing is comforting: Effects of teleoperator visibility in robot-mediated health care. 2016 11 th ACM/IEEE Int Conf Hum-Robot Interact HRI. 2016. p. 11-8. |
| 43. | Cortellessa G, Fracasso F, Sorrentino A, Orlandini A, Bernardi G, Coraci L, et al. ROBIN, a telepresence robot to support older users monitoring and social inclusion: Development and evaluation. Telemed J E Health 2018;24:145-54. |
| 44. | Stahl BC, Coeckelbergh M. Ethics of healthcare robotics: Towards responsible research and innovation. Robot Auton Syst 2016;86:152-61. |
| 45. | Gonzalez-Jimenez J, Galindo C, Gutierrez-Castaneda C. Evaluation of a Telepresence Robot for the Elderly: A Spanish Experience. 2013. |
| 46. | Alers S, Bloembergen D, Claes D, Fossel J, Hennes D, Tuyls K. Telepresence Robots as a Research Platform for AI. AAAI Spring Symp – Tech Rep. AAAI; 20130905. p. 2-3. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1]
|
Search Pubmed for