Nurses' perception towards care robots and their work experience with socially assistive technology during COVID-19: A qualitative study.

This study aimed to explore nurses' perceptions towards care robots and their work experiences in caring for older adults who use socially assistive technology. This qualitative descriptive study included 18 nurses who cared for older adults with dementia or living alone at home. Interviews via Zoom were conducted, and the collected data were analyzed using inductive content analysis. The three themes were identified: (1) perceived benefits, (2) perceived challenges, and (3) improvements needed to enhance the quality of care. The participants perceived that the care robot and socially assistive technology were useful in caring for older adults during COVID-19. However, they noted that the limited capabilities of the technology and an increased workload negatively impacted the quality of care for older adults. The findings of this study indicated that socially assistive technology and care robots have potential benefits in assisting older adults with dementia or living alone.

Robot-assisted sacrohysteropexy vs robot-assisted sacrocolpopexy in women with primary advanced apical prolapse: A retrospective cohort study.

BACKGROUND: This study aimed to evaluate the anatomic and clinical outcomes of robot-assisted sacrohysteropexy (RASH) against robot-assisted sacrocolpopexy (RASC) for the treatment of primary advanced apical prolapse. METHODS: We conducted a retrospective cohort study of all robot-assisted pelvic organ prolapse surgeries for primary advanced apical prolapse (stage ≥II) between January 2011 and May 2021 at an academic tertiary hospital. Surgical outcomes and pelvic organ function were evaluated using the Pelvic Organ Prolapse Quantitative (POP-Q) stage and validated questionnaires (POPDI-6) during preoperative and postoperative 12-month follow-up evaluations. All data were obtained from electronic medical records. RESULTS: A total of 2368 women underwent surgery for apical prolapse repair, and 18 women underwent either RASH (n = 11) or RASC (n = 7). Compared to the RASC group, the RASH group was significantly younger, premenopausal, and less parous. Preoperative prolapse stage, operative time, estimated blood loss, and hospitalization length was comparable between the groups. No intraoperative complications were observed. All women had a median follow-up duration of 24 months (range: 12-108 months). During the 12-month follow-up period, women in the RASH group reported higher satisfaction with the surgery than those in the RASC group (100% vs. 71.4%, p = 0.137). The mesh exposure rate was significantly higher in the RASC group (3/7, 42.9%) than in the RASH group (0/11, 0%) ( p = 0.043), which was found at 12 to 36 months postoperatively and was successfully managed with vaginal estrogen cream. In the RASH group, one woman required reoperation with anterior colporrhaphy for recurrent anterior prolapse at 60 months postoperatively. The apical success rate was 100% at one year postoperatively, without apical recurrence in either group during the follow-up period. CONCLUSION: RASH represents an effective and feasible option for the surgical treatment of advanced primary apical prolapse in women who desire uterine preservation and have a significantly lower risk of mesh erosion than RASC.

Implementation of an Environmental Cleaning Protocol in Hospital Critical Areas Using a UV-C Disinfection Robot.

Improving the cleaning and disinfection of high-touch surfaces is one of the core components of reducing healthcare-associated infections. The effectiveness of an enhanced protocol applying UV-C irradiation for terminal room disinfection between two successive patients was evaluated. Twenty high-touch surfaces in different critical areas were sampled according to ISO 14698-1, both immediately pre- and post-cleaning and disinfection standard operating protocol (SOP) and after UV-C disinfection (160 sampling sites in each condition, 480 in total). Dosimeters were applied at the sites to assess the dose emitted. A total of 64.3% (103/160) of the sampling sites tested after SOP were positive, whereas only 17.5% (28/160) were positive after UV-C. According to the national hygienic standards for health-care setting, 9.3% (15/160) resulted in being non-compliant after SOP and only 1.2% (2/160) were non-compliant after UV-C disinfection. Operation theaters was the setting that resulted in being less compliant with the standard limit (≤15 colony-forming unit/24 cm2) after SOP (12%, 14/120 sampling sites) and where the UV-C treatment showed the highest effectiveness (1.6%, 2/120). The addition of UV-C disinfection to the standard cleaning and disinfection procedure had effective results in reducing hygiene failures.

Same day discharge for robot-assisted radical prostatectomy: a prospective cohort study documenting an Australian approach.

BACKGROUND: The introduction of robotic surgical systems has significantly impacted urological surgery, arguably more so than other surgical disciplines. The focus of our study was length of hospital stay - patients have traditionally been discharged day 1 post-robot-assisted radical prostatectomy (RARP), however, during the ongoing COVID-19 pandemic and consequential resource limitations, our centre has facilitated a cohort of same-day discharges with initial success. METHODS: We conducted a prospective tertiary single-centre cohort study of a series of all patients (n = 28) - undergoing RARP between January and April 2021. All patients were considered for a day zero discharge pathway which consisted of strict inclusion criteria. At follow-up, each patient's perspective on their experience was assessed using a validated post-operative satisfaction questionnaire. Data were reviewed retrospectively for all those undergoing RARP over the study period, with day zero patients compared to overnight patients. RESULTS: Overall, 28 patients 20 (71%) fulfilled the objective criteria for day zero discharge. Eleven patients (55%) agreed pre-operatively to day zero discharge and all were successfully discharged on the same day as their procedure. There was no statistically significant difference in age, BMI, ASA, Charlson score or disease volume. All patients indicated a high level of satisfaction with their procedure. Median time from completion of surgery to discharge was 426 min (7.1 h) in the day zero discharge cohort. CONCLUSION: Day zero discharge for RARP appears to deliver high satisfaction, oncological and safety outcomes. Therefore, our study demonstrates early success with unsupported same-day discharge in carefully selected and pre-counselled patients.

Study on the Applicability of Digital Twins for Home Remote Motor Rehabilitation.

The COVID-19 pandemic created the need for telerehabilitation development, while Industry 4.0 brought the key technology. As motor therapy often requires the physical support of a patient's motion, combining robot-aided workouts with remote control is a promising solution. This may be realised with the use of the device's digital twin, so as to give it an immersive operation. This paper presents an extensive overview of this technology's applications within the fields of industry and health. It is followed by the in-depth analysis of needs in rehabilitation based on questionnaire research and bibliography review. As a result of these sections, the original concept of controlling a rehabilitation exoskeleton via its digital twin in the virtual reality is presented. The idea is assessed in terms of benefits and significant challenges regarding its application in real life. The presented aspects prove that it may be potentially used for manual remote kinesiotherapy, combined with the safety systems predicting potentially harmful situations. The concept is universally applicable to rehabilitation robots.

Decision Method of Optimal Needle Insertion Angle for Dorsal Hand Intravenous Robot.

In the context of COVID-19, the research on various aspects of the venipuncture robot field has become increasingly hot, but there has been little research on robotic needle insertion angles, primarily performed at a rough angle. This will increase the rate of puncture failure. Furthermore, there is sometimes significant pain due to the patients' differences. This paper investigates the optimal needle entry angle decision for a dorsal hand intravenous injection robot. The dorsal plane of the hand was obtained by a linear structured light scan, which was used as a basis for calculating the needle entry angle. Simulation experiments were also designed to determine the optimal needle entry angle. Firstly, the linear structured optical system was calibrated and optimized, and the error function was constructed and solved iteratively by the optimization method to eliminate measurement error. Besides, the dorsal hand was scanned to obtain the spatial point clouds of the needle entry area, and the least squares method was used to fit it to obtain the dorsal hand plane. Then, the needle entry angle was calculated based on the needle entry area plane. Finally, the changes in the penetration force under different needle entry angles were analyzed to determine the optimal needle insertion angle. According to the experimental results, the average error of the optimized structured light plane position was about 0.1 mm, which meets the needs of the project, and a large angle should be properly selected for needle insertion during the intravenous injection.

JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution.

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered.

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.

The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

Ferrobotic swarms enable accessible and adaptable automated viral testing.

Expanding our global testing capacity is critical to preventing and containing pandemics^1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required^10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence^15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies^21-29. Here we use a swarm of millimetre-sized magnets as mobile robotic agents ('ferrobots') for precise and robust handling of magnetized sample droplets and high-fidelity delivery of flexible workflows based on nucleic acid amplification tests to overcome these limitations. Within a palm-sized printed circuit board-based programmable platform, we demonstrated the myriad of laboratory-equivalent operations involved in pooled testing. These operations were guided by an introduced square matrix pooled testing algorithm to identify the samples from infected patients, while maximizing the testing efficiency. We applied this automated technology for the loop-mediated isothermal amplification and detection of the SARS-CoV-2 virus in clinical samples, in which the test results completely matched those obtained off-chip. This technology is easily manufacturable and distributable, and its adoption for viral testing could lead to a 10-300-fold reduction in reagent costs (depending on the viral prevalence) and three orders of magnitude reduction in instrumentation cost. Therefore, it is a promising solution to expand our testing capacity for pandemic preparedness and to reimagine the automated clinical laboratory of the future.

Piton: Investigating the Controllability of a Wearable Telexistence Robot.

The COVID-19 pandemic impacted collaborative activities, travel, and physical contact, increasing the demand for real-time interactions with remote environments. However, the existing remote communication solutions provide limited interactions and do not convey a high sense of presence within a remote environment. Therefore, we propose a snake-shaped wearable telexistence robot, called Piton, that can be remotely used for a variety of collaborative applications. To the best of our knowledge, Piton is the first snake-shaped wearable telexistence robot. We explain the implementation of Piton, its control architecture, and discuss how Piton can be deployed in a variety of contexts. We implemented three control methods to control Piton: HM-using a head-mounted display (HMD), HH-using an HMD and hand-held tracker, and FM-using an HMD and a foot-mounted tracker. We conducted a user study to investigate the applicability of the proposed control methods for telexistence, focusing on body ownership (Alpha IVBO), mental and physical load (NASA-TLX), motion sickness (VRSQ), and a questionnaire to measure user impressions. The results show that both the HM and HH provide relevantly high levels of body ownership, had high perceived accuracy, and were highly favored, whereas the FM control method yielded the lowest body ownership effect and was least favored. We discuss the results and highlight the advantages and shortcomings of the control methods with respect to various potential application contexts. Based on our design and evaluation of Piton, we extracted a number of insights and future research directions to deepen our investigation and realization of wearable telexistence robots.

A study on the performance and cost-effectiveness of robots in replacing manual nucleic acid collection method: Experience from the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has led nucleic acid collection and detection became a measure to ensure normal life in China. Considering the huge detection demand, it has emerged that robots replace manual sample collection. However, the cost-effectiveness of nucleic acid collection by robots instead of humans remain unknown. METHODS: This study was approved by the Ethics Committee of the Shenzhen Luohu District People's Hospital, number 2021-LHQRMYY-KYLL-031a. All participants signed the written informed consent of this study. 273 volunteers were recruited on December 1st 2021 from Shenzhen and divided into six groups: one group to be sampled by robots and the others to be sampled manually with varying specifications for swab rotation and insertion time. Questionnaires were distributed to the robot group to ask them sampling feeling. The effectiveness and safety of sampling were evaluated through the sampling efficiency, adverse events and sampling feeling of different groups. The economics of the different methods were judged by comparing the sampling cost for each. RESULTS: The sampling efficiency of the robot group was 96.9%, and there was no statistically significant difference between the other five manually sampled groups (p = 0.586). There were no serious adverse events in any of the six groups, but nasal soreness and tearing did occur in all group. Of the volunteers who underwent robotic sampling, 85.94% reported that the experience was either no different or more comfortable than the manual sampling. In economic terms, a single robot used to replace medical staff for sample collection becomes economically advantageous when the working time is ≥ 455 days. If multiple robots are used to replace twice the number of manual collections, it becomes more economical at 137 days and remains so as long as the robot is used. CONCLUSIONS: It appears safe and effective for robots to replace manual sampling method. Implementation of robotic sampling is economical and feasible, and can significantly save costs when working over a long term.

COVID-19 and public support for autonomous technologies-Did the pandemic catalyze a world of robots?

By introducing a novel risk to human interaction, COVID-19 may have galvanized interest in uses of artificial intelligence (AI). But was the pandemic a large enough catalyst to change public attitudes about the costs and benefits of autonomous systems whose operations increasingly rely on AI? To answer this question, we use a preregistered research design that exploits variation across the 2018 and 2020 waves of the CCES/CES, a nationally representative survey of adults in the United States. We compare support for autonomous cars, autonomous surgeries, weapons, and cyber defense pre- and post-the beginning of the COVID-19 pandemic. We find that, despite the incentives created by COVID-19, the pandemic did not increase support for most of these technologies, except in the case of autonomous surgery among those who know someone who died of COVID-19. The results hold even when controlling for a variety of relevant political and demographic factors. The pandemic did little to push potential autonomous vehicle users to support adoption. Further, American concerns about autonomous weapons, including cyber defense, remain sticky and perhaps exacerbated over the last two years. These findings suggest that the relationship between the COVID-19 pandemic and the adoption of many of these systems is far more nuanced and complex than headlines may suggest.

A Meta-Analysis on Remote HRI and In-Person HRI: What Is a Socially Assistive Robot to Do?

Recently, due to the COVID-19 pandemic and the related social distancing measures, in-person activities have been significantly reduced to limit the spread of the virus, especially in healthcare settings. This has led to loneliness and social isolation for our most vulnerable populations. Socially assistive robots can play a crucial role in minimizing these negative affects. Namely, socially assistive robots can provide assistance with activities of daily living, and through cognitive and physical stimulation. The ongoing pandemic has also accelerated the exploration of remote presence ranging from workplaces to home and healthcare environments. Human-robot interaction (HRI) researchers have also explored the use of remote HRI to provide cognitive assistance in healthcare settings. Existing in-person and remote comparison studies have investigated the feasibility of these types of HRI on individual scenarios and tasks. However, no consensus on the specific differences between in-person HRI and remote HRI has been determined. Furthermore, to date, the exact outcomes for in-person HRI versus remote HRI both with a physical socially assistive robot have not been extensively compared and their influence on physical embodiment in remote conditions has not been addressed. In this paper, we investigate and compare in-person HRI versus remote HRI for robots that assist people with activities of daily living and cognitive interventions. We present the first comprehensive investigation and meta-analysis of these two types of robotic presence to determine how they influence HRI outcomes and impact user tasks. In particular, we address research questions regarding experience, perceptions and attitudes, and the efficacy of both humanoid and non-humanoid socially assistive robots with different populations and interaction modes. The use of remote HRI to provide assistance with daily activities and interventions is a promising emerging field for healthcare applications.

Methodologies Used to Study the Feasibility, Usability, Efficacy, and Effectiveness of Social Robots For Elderly Adults: Scoping Review.

BACKGROUND: New research fields to design social robots for older people are emerging. By providing support with communication and social interaction, these robots aim to increase quality of life. Because of the decline in functioning due to cognitive impairment in older people, social robots are regarded as promising, especially for people with dementia. Although study outcomes are hopeful, the quality of studies on the effectiveness of social robots for the elderly is still low due to many methodological limitations. OBJECTIVE: We aimed to review the methodologies used thus far in studies evaluating the feasibility, usability, efficacy, and effectiveness of social robots in clinical and social settings for elderly people, including persons with dementia. METHODS: Dedicated search strings were developed. Searches in MEDLINE (PubMed), Web of Science, PsycInfo, and CINAHL were performed on August 13, 2020. RESULTS: In the 33 included papers, 23 different social robots were investigated for their feasibility, usability, efficacy, and effectiveness. A total of 8 (24.2%) studies included elderly persons in the community, 9 (27.3%) included long-term care facility residents, and 16 (48.5%) included people with dementia. Most of the studies had a single aim, of which 7 (21.2%) focused on efficacy and 7 (21.2%) focused on effectiveness. Moreover, forms of randomized controlled trials were the most applied designs. Feasibility and usability were often studied together in mixed methods or experimental designs and were most often studied in individual interventions. Feasibility was often assessed with the Unified Theory of the Acceptance and Use of Technology model. Efficacy and effectiveness studies used a range of psychosocial and cognitive outcome measures. However, the included studies failed to find significant improvements in quality of life, depression, and cognition. CONCLUSIONS: This study identified several shortcomings in methodologies used to evaluate social robots, resulting in ambivalent study findings. To improve the quality of these types of studies, efficacy/effectiveness studies will benefit from appropriate randomized controlled trial designs with large sample sizes and individual intervention sessions. Experimental designs might work best for feasibility and usability studies. For each of the 3 goals (efficacy/effectiveness, feasibility, and usability) we also recommend a mixed method of data collection. Multiple interaction sessions running for at least 1 month might aid researchers in drawing significant results and prove the real long-term impact of social robots.

Developing an Upper Limb Kinematics Database of Activities of Daily Living.

Open-access databases can facilitate data sharing among researchers and provide normative data for objective clinical assessment development, robotic design, and biomechanical modeling. However, most existing databases focus on gait, balance, and hand gestures without providing elbow and shoulder kinematics that are required in activities of daily living. Furthermore, the few existing upper limb datasets include small sample sizes without consistent data collection protocols, which hinder robotic engineers' ability to design robotic devices that accommodate the general population. To address the literature gap, an open-access upper limb kinematic database was proposed. Due to the impact of COVID-19 on human research, only data from 16 participants were collected. Clinical Relevance-This provides baseline kinematics for developing objective clinical assessments and rehabilitation robots.

Teleoperated Probe Manipulator for Prone-Position Echocardiography Examination.

Echocardiography probe manipulation is a strenuous task. During a procedure, the operator must hold the probe, extend their arm, bend their elbow, and monitor the resulting image simultaneously, which causes strain and introduces variability to the measurement. We propose a teleoperated probe manipulation robot to reduce the burden of handling the probe and minimize the infection risk during the COVID pandemic. The proposed robot utilizes prone position scanning that could enlarge the cardiac windows for easier scanning and eliminate the risk of the robot pressing down on the patient. We derived the robot's requirements based on a real clinical scenario. Initial evaluation showed that the robot could achieve the required range of motion, force, and control. The robot's functionality was tested by a non-clinician, in which the tester could obtain heart images of a volunteer in under one minute.

Rapid disinfection of radiology treatment rooms using an autonomous ultraviolet germicidal irradiation robot.

BACKGROUND: Ultraviolet germicidal irradiation (UVGI) technologies have emerged as a promising adjunct to manual cleaning, however, their potential to shorten cleaning times remains unexplored. METHODS: A <10-minute disinfection procedure was developed using a robotic UVGI platform. The efficacy and time to perform the UVGI procedure in a CT scan treatment room was compared with current protocols involving manual disinfection using biocides. For each intervention, environmental samples were taken at 12 locations in the room before and after disinfection on seven distinct occasions. RESULTS: The mean UVC dose at each sample location was found to be 13.01 ± 4.36 mJ/cm2, which exceeded published UVC thresholds for achieving log reductions of many common pathogens. Significant reductions in microbial burden were measured after both UVGI (P≤.001) and manual cleaning (P≤.05) conditions, with the UVGI procedure revealing the largest effect size (r = 0.603). DISCUSSION: These results support the hypothesis that automated deployments of UVGI technology can lead to germicidal performance that is comparable with, and potentially better than, current manual cleaning practices. CONCLUSIONS: Our findings provide early evidence that the incorporation of automated UVGI procedures into cleaning workflow could reduce turnaround times in radiology, and potentially other hospital settings.