InjectMeAI-Software Module of an Autonomous Injection Humanoid.

The recent pandemic outbreak proved social distancing effective in helping curb the spread of SARS-CoV-2 variants along with the wearing of masks and hand gloves in hospitals and assisted living environments. Health delivery personnel having undergone training regarding the handling of patients suffering from Corona infection have been stretched. Administering injections involves unavoidable person to person contact. In this circumstance, the spread of bodily fluids and consequently the Coronavirus become eminent, leading to an upsurge of infection rates among nurses and doctors. This makes enforced home office practices and telepresence through humanoid robots a viable alternative. In providing assistance to further reduce contact with patients during vaccinations, a software module has been designed, developed, and implemented on a Pepper robot that estimates the pose of a patient, identifies an injection spot, and raises an arm to deliver the vaccine dose on a bare shoulder. Implementation was done using the QiSDK in an android integrated development environment with a custom Python wrapper. Tests carried out yielded positive results in under 60 s with an 80% success rate, and exposed some ambient lighting discrepancies. These discrepancies can be solved in the near future, paving a new way for humans to get vaccinated.

Automatic system for high-throughput and high-sensitivity diagnosis of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had severe consequences for health and the global economy. To control the transmission, there is an urgent demand for early diagnosis and treatment in the general population. In the present study, an automatic system for SARS-CoV-2 diagnosis is designed and built to deliver high specification, high sensitivity, and high throughput with minimal workforce involvement. The system, set up with cross-priming amplification (CPA) rather than conventional reverse transcription-polymerase chain reaction (RT-PCR), was evaluated using more than 1000 real-world samples for direct comparison. This fully automated robotic system performed SARS-CoV-2 nucleic acid-based diagnosis with 192 samples in under 180 min at 100 copies per reaction in a "specimen in data out" manner. This throughput translates to a daily screening capacity of 800-1000 in an assembly-line manner with limited workforce involvement. The sensitivity of this device could be further improved using a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based assay, which opens the door to mixed samples, potentially include SARS-CoV-2 variants screening in extensively scaled testing for fighting COVID-19.

Robotic Applications in Orthodontics: Changing the Face of Contemporary Clinical Care.

The last decade (2010-2021) has witnessed the evolution of robotic applications in orthodontics. This review scopes and analyzes published orthodontic literature in eight different domains: (1) robotic dental assistants; (2) robotics in diagnosis and simulation of orthodontic problems; (3) robotics in orthodontic patient education, teaching, and training; (4) wire bending and customized appliance robotics; (5) nanorobots/microrobots for acceleration of tooth movement and for remote monitoring; (6) robotics in maxillofacial surgeries and implant placement; (7) automated aligner production robotics; and (8) TMD rehabilitative robotics. A total of 1,150 records were searched, of which 124 potentially relevant articles were retrieved in full. 87 studies met the selection criteria following screening and were included in the scoping review. The review found that studies pertaining to arch wire bending and customized appliance robots, simulative robots for diagnosis, and surgical robots have been important areas of research in the last decade (32%, 22%, and 16%). Rehabilitative robots and nanorobots are quite promising and have been considerably reported in the orthodontic literature (13%, 9%). On the other hand, assistive robots, automated aligner production robots, and patient robots need more scientific data to be gathered in the future (1%, 1%, and 6%). Technological readiness of different robotic applications in orthodontics was further assessed. The presented eight domains of robotic technologies were assigned to an estimated technological readiness level according to the information given in the publications. Wire bending robots, TMD robots, nanorobots, and aligner production robots have reached the highest levels of technological readiness: 9; diagnostic robots and patient robots reached level 7, whereas surgical robots and assistive robots reached lower levels of readiness: 4 and 3, respectively.

Progress in robotics for combating infectious diseases.

The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.

An Autotuning Cable-Driven Device for Home Rehabilitation.

Out of all the changes to our daily life brought by the COVID-19 pandemic, one of the most significant ones has been the limited access to health services that we used to take for granted. Thus, in order to prevent temporary injuries from having lingering or permanent effects, the need for home rehabilitation device is urgent. For this reason, this paper proposes a cable-driven device for limb rehabilitation, CUBE2, with a novel end-effector (EE) design and autotuning capabilities to enable autonomous use. The proposed design is presented as an evolution of the previous CUBE design. In this paper, the proposed device is modelled and analyzed with finite element analysis. Then, a novel vision-based control strategy is described. Furthermore, a prototype has been manufactured and validated experimentally. Preliminary test to estimate home position repeatability has been carried out.

Approaches to assessing the impact of robotics in geriatric mental health care: a scoping review.

The goals of this scoping literature review are to (1) aggregate the current research involving socially assistive robots in the setting of geriatric psychiatry and (2) examine the outcome measures used in these studies and determine where the gaps and needs are. In light of the global COVID-19 pandemic, the geriatric psychiatric population in particular is vulnerable to both the physical and mental toll COVID-19 may cause. Recently, socially assistive robots have gained attention for their ability to aid in the care of the geriatric psychiatry population and are being explored as a realistic way to deliver certain elements of psychiatric care that have the potential to be safe even in the setting of COVID-19. The results of this review indicate that robots are in the early stages of clinical applicability, they display usability for a range of psychiatric indications, and their impact on clinical care is notable. We project that in the next few years, robotic applications will be tailored to address clinical outcomes with a greater degree of precision and efficacy.

Can occupational therapy manpower be replaced with social robots in a singing group during COVID-19?

BACKGROUND: Prior to the COVID-19 global health emergency, reducing direct contacts between therapists and patients is an important issue, and could be achieved by using robots to perform certain caring activities. OBJECTIVE: This study compares therapeutic factors of singing group activities directed by social robots and by occupational therapists at elderly care centers during this COVID-19 outbreak. METHODS: This project has a quasi-experimental research design, based on a pilot study of 14 subjects aged above 65 years. They received eight sessions of singing group therapy given by a social robot or an occupational therapist. Completed copies of a therapeutic-factor questionnaire were then collected. RESULTS: At the 4th week, the scores for 8 therapeutic factors were higher in sessions with the occupational therapist than the robot-directed sessions, reaching a statistically significant level; at the 8th week, the scores for 3 therapeutic factors, including imparting of information, were higher in sessions with the occupational therapist than in sessions with the robot. The top scoring therapeutic factor in the robot sessions was group cohesiveness. CONCLUSIONS: Social robots may be good companion tools for elderly care during this COVID-19 outbreak, but group therapy sessions supervised by real-person therapists still have higher therapeutic factor scores than those conducted by robots. The number of subjects needs to be increased to enhance the validity of future study results.

Use of Telepresence Robots in Glaucoma Patient Education.

PRCIS: Telepresence robots (TR) present the versatility to effectively provide remote educational sessions for patients affected by glaucoma to improve disease knowledge. Given COVID-19's effect on clinical practice, TR can maintain social distancing when educating patients. PURPOSE: TR are devices that allow remote users to have a mobile presence anywhere. We compared the effect of an education session given by an in-person educator versus a TR on glaucoma knowledge and identified factors that impact patient education. METHODS: Eighty-five glaucoma patients were split into control, human, and TR groups. We measured glaucoma knowledge scores (KS) using the National Eye Institute's Eye-Q Test. Human and TR groups had the education session with a human or TR followed by the questionnaire. The control group was administered the questionnaire without an education session. Treatment regimen recall (RR) >90% was considered a success. We used linear regression and binary logistic regression to determine variables that affect KS and RR, respectively. RESULTS: Mean age was 58.3±2.8 years. 49% were female. Mean KS were 5.8±0.7 in the control group (n=31), 7.9±0.5 in the TR group (n=26), and 8.4±0.5 in the human group (n=28). Control participants had a lower mean KS than the human or TR groups (P<0.001). Having the education session (2.5, P<0.001), education greater than high school (0.8, P=0.016), and diabetes (-0.7, P=0.037) affected KS. Having diabetes (odds ratio=0.14, P=0.014) negatively affected RR. Having the education session may affect RR (odds ratio=5.47, P=0.07), warranting additional studies. CONCLUSIONS: Education sessions with a human and TR improved patients' glaucoma KS. TRs may serve as an alternative to in-person education sessions and allow educators to safely and effectively educate patients remotely to adhere to COVID-19 social distancing guidelines.

A Mathematical Model to Study the Effectiveness of Some of the Strategies Adopted in Curtailing the Spread of COVID-19.

In this paper, we developed a model that suggests the use of robots in identifying COVID-19-positive patients and which studied the effectiveness of the government policy of prohibiting migration of individuals into their countries especially from those countries that were known to have COVID-19 epidemic. Two compartmental models consisting of two equations each were constructed. The models studied the use of robots for the identification of COVID-19-positive patients. The effect of migration ban strategy was also studied. Four biologically meaningful equilibrium points were found. Their local stability analysis was also carried out. Numerical simulations were carried out, and the most effective strategy to curtail the spread of the disease was shown.

A Human Support Robot for the Cleaning and Maintenance of Door Handles Using a Deep-Learning Framework.

The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.