Healthcare Robots Research Articles

Välkky's voyage on to a hospital ward: Expectations, explorations and emergent robocentric nursing care.

Drawing on ethnographic fieldwork in Finland, we report on the trial of a teleoperated care robot named Välkky introduced onto a fully operational hospital neurological ward. Our data revealed a narrative arc where participants' early expectations of the hospital-based trial altered as the project unfolded. Greeted with techno-excitement and experimental enthusiasm about the place of robotics in reshaping roles within clinical care, Välkky became the focus for collaborative in situ learning, adaptation and redesign amongst the roboticists, designers, nurses, patients, and managers. Välkky acted as an 'attractor' provoking thinking about, and a reimagining of, future arrangements of care. Our empirically informed insights seek to pave the way for real-world nuanced thinking that pushes beyond human/non-human and success/failure binaries. Building on debates in STS and feminist posthumanism, we propose a robocentric approach, which encourages us to 'queer' health care robots, and to understand them as fluid, hybrid, distributed and relational figures, rather than purely as inert, mechanical, non-human objects that might replace humans. Nursing care practices by and with robots will generate new meanings and practices of care that will emerge iteratively, as caring relations, relationships and practices develop within the context of operational ward environments. Robots may or may not be able support care, but they will invariably challenge what care is.

The Role of Robotics in Mental Health Interventions: Opportunities and Challenges

The integration of robotics into mental health interventions represents a transformative shift in healthcare, offering innovative solutions to longstanding challenges. Robots equipped with artificial intelligence and sensor technologies are increasingly being designed to support individuals with mental health conditions through therapeutic engagement, monitoring, and companionship. These systems have shown promise in delivering consistent, personalized care, particularly for populations with limited access to mental health professionals, such as the elderly, individuals with autism spectrum disorders, and those in remote areas, the adoption of robotics in this domain also presents significant challenges. Ethical considerations regarding privacy, consent, and the potential dehumanization of care must be addressed. Technical barriers, including the need for intuitive user interfaces, adaptive algorithms, and affordability, remain critical. Additionally, the efficacy of robotic interventions compared to traditional methods requires robust evidence from clinical trials. This paper explores the opportunities and challenges of using robotics in mental health care, focusing on their potential to enhance therapeutic outcomes, improve accessibility, and foster patient engagement. It emphasizes the need for interdisciplinary collaboration between engineers, psychologists, and ethicists to ensure that these technologies are both effective and ethically deployed. Robotics in mental health, though still nascent, holds significant promise for reshaping the future of psychological care.

Super-Elastic and Multifunctional Core-Sheath EGaIn Fibers for Wearable Motion Detection and Visual Electrophysiologic Monitoring

Fiber-based wearable electronics, distinguished by their remarkable flexibility and robust performance, have emerged as a focal point in healthcare and soft robotics. While significant strides have been made in advancing fiber-based wearable electronics, pursuing multifunctional integration, particularly in areas such as personal health monitoring continues to present significant challenges. In this work, we report the successful fabrication of ultra-elastic and multifunctional core-sheath EGaIn microfibers tailored for wearable motion detection and visual electrophysiologic monitoring. These highly conductive fibers are characterized by an intriguing core-sheath structure, achieved through the uniform coating of EGaIn liquid metal on wet-spun elastomeric microfibers, which are priorly magnetron sputtered with gold nanoparticles (Au NPs) for fast alloying with EGaIn. Prepared core-sheath EGaIn microfibers possess numerous micro-wrinkles on the surface via a simple pre-stretching treatment, thus demonstrating an exceptional sensitivity (GF=108.3), a fast response time of 82 ms, and remarkable stability. Their superior electromechanical stability and sensitivity under various strain conditions enable reliable and real-time detection of human motion signals, electrooculography (EOG) signals, and visual evoked potentials (VEP). This research offers a novel approach to the multifunctional integration of high-performance fiber-based electronics.

Compassionate Care with Autonomous AI Humanoid Robots in Future Healthcare Delivery: A Multisensory Simulation of Next-Generation Models.

The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as "caring", necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human-robot interactions (HRIs) surpassing Tetsuya Tanioka's TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers' theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of "dynamic communication", (relational) "entanglement", and (healthcare system and robot's function) "superpositioning" in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson's ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of "caring". Establishing "pluralistic agreements" through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a "posthumanist" framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by "technological knowing" in Rozzano Locsin's TCCN theory.

AI Robotics in Healthcare Between the EU Medical Device Regulation and the Artificial Intelligence Act

AI Robotics in Healthcare Between the EU Medical Device Regulation and the Artificial Intelligence Act

The Integration of Robotics in Proactive Healthcare: Embracing Proactive Medicine for Enhanced Patient Care and Ethical Considerations

The Integration of Robotics in Proactive Healthcare: Embracing Proactive Medicine for Enhanced Patient Care and Ethical Considerations

Ethical Reflections on the Application of Medical Robots in Healthcare

With the development and utilization of medical robots, more and more members of the public have begun to pay attention to the risks of practicing medical robots and have expressed concerns about their application. In order to deeply study the ethical risks of medical robots applied in health care, this study chooses the literature research method to reflect on the moral and ethical issues involved in medical robots. The study found that current medical robots have many application scenarios, such as: surgery, rehabilitation care, assisted work and telemedicine. These application scenarios of medical robots may bring ethical risks such as attribution of medical responsibility, doctor-patient relationship, and data algorithms. Intensively, the study points out that inadequate laws, immature values, and technological deficiencies are the most significant factors leading to the aforementioned risks. Thus, adequate attention should be paid to the medical career, the intelligent technology of robots should be improved, and the relevant laws and regulations should be actively improved. This study realistically reflects the current situation and risks of medical robot application, and through in-depth reflection, provides inspiration for robots to better serve the medical cause.

Special Issue on Robotics and Mechatronics Technology for Sports, Exercise, and Health Care (Part 2)

Robotics, mechatronics, and digital technologies are advancing significantly in sports, exercise, and healthcare, and offer innovative solutions that shape the future of these fields. In Part 2 of this special issue, we examine independent research and cutting-edge developments that address new challenges and provide unique contributions to these domains. Technological advancements in assistive devices and rehabilitation offer new solutions for improving the quality of life of individuals with physical challenges. Researchers of wearable assistive devices and prosthetics continue to devise methods to enhance mobility and support physical functions, thereby benefiting both users and healthcare providers. Sports and motion analysis remains a key focus area, where innovative technologies are employed to analyze and improve athletic performance. By capturing and evaluating detailed movement data, these technologies can help athletes refine their techniques and achieve greater precision in their sports, thus ultimately resulting in enhanced performance and injury prevention. As in Part 1, the studies presented in this issue demonstrate the practical applications of cutting-edge technologies and offer insights into their future potential. We hope that these contributions will inspire researchers and practitioners alike and provide a foundation for continued advancements in these exciting and impactful fields.

Patient Preferences Concerning Humanoid Features in Healthcare Robots

In this paper, I argue that patient preferences concerning human physical attributes associated with race, culture, and gender should be excluded from public healthcare robot design. On one hand, healthcare should be (objective, universal) needs oriented. On the other hand, patient well-being (the aim of healthcare) is, in concrete ways, tied to preferences, as is patient satisfaction (a core WHO value). The shift toward patient-centered healthcare places patient preferences into the spotlight. Accordingly, the design of healthcare technology cannot simply disregard patient preferences, even those which are potentially morally problematic. A method for handling these at the design level is thus imperative. By way of uncontroversial starting points, I argue that the priority of the public healthcare system is the fulfillment of patients’ therapeutic needs, among which certain potentially morally problematic preferences may be counted. There are further ethical considerations, however, which, taken together, suggest that the potential benefits of upholding these preferences are outweighed by the potential harms.

Satisfaction analysis of 5G remote ultrasound robot for diagnostics based on a structural equation model.

With the increasing application of 5G remote ultrasound robots in healthcare, robust methods are in critical demand to assess participant satisfaction and identify its influencing factors. At present, there is limited empirical research on multi-parametric and multidimensional satisfaction evaluation of participants with 5G remote ultrasound robot examination. Previous studies have demonstrated that structural equation modeling (SEM) effectively integrates various statistical techniques to examine the relationships among multiple variables. Therefore, this study aimed to evaluate the satisfaction of participants with 5G remote ultrasound robot examination and its influencing factors using SEM. Between April and June 2022, 213 participants from Wuhan Automobile Manufacturing Company underwent remote ultrasound examinations using the MGIUS-R3 remote ultrasound robot system. After these examinations, the participants evaluated the performance of the 5G remote ultrasound robot based on their personal experiences and emotional responses. They completed a satisfaction survey using a self-developed questionnaire, which included 19 items across five dimensions: examination efficiency, examination perception, communication perception, value perception, and examination willingness. A SEM was established to assess the satisfaction of participants with the 5G remote ultrasound robot examinations and the influencing factors. A total of 201 valid questionnaires were collected. The overall satisfaction of participants with the 5G remote ultrasound robot examination was 45.43 ± 11.60, with 169 participants (84%) expressing satisfaction. In the path hypothesis relationship test, the dimensions of examination efficiency, examination perception, communication perception, and value perception had positive effects on satisfaction, with standardized path coefficients of 0.168, 0.170, 0.175, and 0.191. Satisfaction had a direct positive effect on examination willingness, with a standardized path coefficient of 0.260. Significant differences were observed across different educational levels in the dimensions of examination perception, communication perception, value perception, and examination willingness. Participants with different body mass indices also showed significant differences in examination perception; all p-values were less than 0.05. In this study, value perception was identified as the most significant factor influencing satisfaction. It could be improved by enhancing participants' understanding of the accuracy and safety of 5G remote ultrasound robot examinations. This enhances satisfaction and the willingness to undergo examinations. Such improvements not only facilitate the widespread adoption of this technology but also promote the development of telemedicine services.

Octopus-Inspired Adhesives with Switchable Attachment to Challenging Underwater Surfaces.

Adhesives that excel in wet or underwater environments are critical for applications ranging from healthcare and underwater robotics to infrastructure repair. However, achieving strong attachment and controlled release on difficult substrates, such as those that are curved, rough, or located in diverse fluid environments, remains a major challenge. Here, an octopus-inspired adhesive with strong attachment and rapid release in challenging underwater environments is presented. Inspired by the octopus's infundibulum structure, a compliant, curved stalk, and an active deformable membrane for multi-surface adhesion are utilized. The stalk's curved shape enhances conformal contact on large-scale curvatures and increases contact stress for adaptability to small-scale roughness. These synergistic mechanisms improve contact across multiple length scales, resulting in switching ratios of over 1000 within ≈30 ms with consistent attachment strength of over 60 kPa on diverse surfaces and conditions. These adhesives are demonstrated through the robust attachment and precise manipulation of rough underwater objects.

Are nurses and patients willing to work with service robots in healthcare? A mixed-methods study

IntroductionScholars have become increasingly interested in incorporating robots into healthcare. While there is a growing body of research examining nurses’ and patients’ attitudes towards using robots in healthcare, no prior research has specifically explored their willingness to integrate service robots within the Egyptian healthcare context.AimThe aim of this study was twofold: (a) to explore the behavioral intentions of nurses to accept robots in their workplace, and (b) to examine the willingness of patients to use service robots in healthcare settings.MethodsA mixed-methods study was conducted. Quantitative data were collected from 301 nurses using the Behavioral Intention to Accept Robots in the Workplace Scale and from 467 patients using the Service Robot Integration Willingness Scale through convenience sampling at three tertiary public hospitals in Port Said, Egypt. Qualitative data were obtained through in-depth, semi-structured interviews with 16 nurses, focusing on their perspectives and concerns regarding robot integration. Descriptive analyses were used to analyze quantitative data, and thematic analysis was used to analyze qualitative data.ResultsQuantitative results indicated a moderate level of behavioral intention to use robots among nurses. Patients demonstrated low willingness to use service robots. In the qualitative analysis of the data obtained from the interviews with nurses, three categories (Concerns about Robots, Roles and Competencies, and Potential Benefits) and eight themes (interaction and emotions, maintenance and reliability, job insecurity, role clarity, competence in critical care, trustworthiness, reducing physical strain, and specialized applications) were identified.ConclusionThe results of this study indicate that nurses’ behavioral intention to accept service robots in healthcare settings is moderate and their acceptance is influenced by various factors related to their concerns about robots, roles and competencies, and potential benefits they could gain. Patients showed a low level of willingness to use service robots in healthcare settings.ImplicationProviding targeted educational programs to nurses and patients, assuring them with the provision of robust maintenance protocols, enhancing their confidence in the capabilities of robots, and defining clear roles for robots are crucial for the successful integration of robots into healthcare settings.

Next-Gen Surgery: AI Robots Leading the Way in Healthcare

As human generations evolve, medical science and engineering have developed, and robotics have become an integral part of medicine from administrative duties to surgical procedures. In medical procedures, robotic systems support and enhance the accuracy and agility of human surgeons. This method is known as robotic surgery or robotic-assisted surgery (RAS). In the public health field, RAS plays an essential role due to its ability to improve surgical precision, lessen patient discomfort following surgery, and shorten hospital stays. In spite of the systems not being entirely self-contained, skilled surgeons use a console and specialized instruments to perform procedures that are more accurate and less invasive. Robots can help provide healthcare services such as monitoring vital signs, administering medications, and providing emotional support. The use of AI with robotics in healthcare has shown a remarkable rise in the past few years, particularly in rural India with limited access to healthcare providers and limited health resources. Urban and rural hospitals across the country can use artificial intelligence to manage tasks like surgery assistance, streamlining hospital logistics, and organizing routine checkups. The following considerations are the main causes of a surge in robotic help in the healthcare sector: (a) robots are hardworking; (b) they can withstand stress; (c) their grasping abilities; (d) they can do precise surgeries; and (e) they are capable of performing repetitive jobs. The present discourse delves into a few medical robotics applications, discussing their advantages, disadvantages, and possible avenues for future research and development.

A Review on Human Robot Interaction in Healthcare

Robots are increasingly being used in healthcare delivery and their effectiveness can be determined through understanding their personalities. Nonetheless, healthcare human robot interaction (H-HRI) has not had a systematic or comprehensive grasp of personality. In the past Healthcare has presented benefits for humanoid robots. However, there is no systematic and comprehensive understanding of personality in health care human robot interaction (H-HRI). This paper examines some common scenarios in medicine and provides an exhaustive way to measure trust. Such a proposed paradigm highlights the importance of unambiguous communication among doctors as well as their expectations regarding robot intervention into a task that is centered on humans. Additionally, it shows how patients receive help from robots. Finally, suggestions are made that aim at improving patient’s confidence because the former contributes towards improved therapy efficiency levels. It Briefly Discusses Five Character Traits Also Personality Traits. These entail human-robot interaction factors.

Human-Robot Interaction and Social Robot: The Emerging Field of Healthcare Robotics and Current and Future Perspectives for Spinal Care.

Recent advances in robotics technology and artificial intelligence (AI) have sparked increased interest in humanoid robots that resemble humans and social robots capable of interacting socially. Alongside this trend, a new field of robot research called human-robot interaction (HRI) is gaining prominence. The aim of this review paper is to introduce the fundamental concepts of HRI and social robots, examine their current applications in the medical field, and discuss the current and future prospects of HRI and social robots in spinal care. HRI is an interdisciplinary field where robotics, AI, social sciences, design, and various disciplines collaborate organically to develop robots that successfully interact with humans as the ultimate goal. While social robots are not yet widely deployed in clinical environments, ongoing HRI research encompasses various areas such as nursing and caregiving support, social and emotional assistance, rehabilitation and cognitive enhancement for the elderly, medical information provision and education, as well as patient monitoring and data collection. Although still in its early stages, research related to spinal care includes studies on robotic support for rehabilitation exercises, assistance in gait training, and questionnaire-based assessments for spinal pain. Future applications of social robots in spinal care will require diverse HRI research efforts and active involvement from spinal specialists.

Revolutionizing Healthcare: A Prototype Serving Robot for Enhanced Medical Assistance

This paper delves into the transformative potential of service robots in healthcare, addressing the growing need for innovative solutions. We examine the collaborative efforts between academia and the healthcare industry to develop and deploy service robots. The focus is on preventing infection spread, reducing human error, and optimizing patient care. Additionally, the paper explores the contemporary field of Robotic Process Automation (RPA) and its applications in relieving mundane tasks within the healthcare industry. The interdisciplinary analysis provides valuable insights into reshaping healthcare practices through robotic innovations

How AI Systems Can Be Blameworthy

AI systems, like self-driving cars, healthcare robots, or Autonomous Weapon Systems, already play an increasingly important role in our lives and will do so to an even greater extent in the near future. This raises a fundamental philosophical question: who is morally responsible when such systems cause unjustified harm? In the paper, we argue for the admittedly surprising claim that some of these systems can themselves be morally responsible for their conduct in an important and everyday sense of the term—the attributability sense. More specifically, relying on work by Nomy Arpaly and Timothy Schroeder (In Praise of Desire, OUP 2014), we propose that the behavior of these systems can manifest their ‘quality of will’ and thus be regarded as something they can be blameworthy for. We develop this position in detail, justify some of its crucial presuppositions, and defend it against potential objections.

Special Issue on Robotics and Mechatronics Technology for Sports, Exercise, and Health Care (Part 1)

Robotics, mechatronics, and digital technologies are rapidly advancing in the fields of sports, exercise, and healthcare, and their applications are expanding daily. In part 1 of our special issue, we focus on the innovative contributions of cutting-edge technologies in various domains. Robotics technology is crucial in sports and exercise training as it monitors individual movements and forms, provides user feedback, and enables effective training. The development of robots to enhance the accuracy and reproducibility of sports techniques is a key element directly linked to performance improvements. Robotics and mechatronics provide new possibilities for treatment and rehabilitation in healthcare. Developing beneficial systems for medical professionals and patients contributes to increased precision in surgery and productive support in rehabilitation. Power assistance and motion augmentation are witnessing advancements that prolong individual capabilities and achieve better performance. These studies may reduce the physical burden of daily activities and specific tasks, thereby enhancing efficiency. Therefore, studies on haptics and sensory feedback are important. Efforts to improve user interfaces through virtual haptic experiences represent a promising new application of digital technology. The studies included in part 1 of this special issue present recent technological trends and practical examples of sports, exercise, and healthcare and aim to provide valuable information and inspire the readers. We hope that these studies will serve as significant steps toward future technological advancement and social implementation.

Multimodal Massage Localization Algorithm for Human Acupoints

As the aging population issue worsens, people are experiencing increasing life pressures, and the elderly are unable to receive sufficient care, intensifying the demand for home-based intelligent massage healthcare robots. Addressing the challenges faced by users in expressing preferences for specific areas like the back through gestures or voice during the use of massage robots, and the issue of elderly individuals expressing imprecisely, which hinders effective communication of the desired massage target location to the robot, this paper proposes a new human-machine interaction mode for massage robots. The main innovations include: achieving precise control of the massage area through the representation of a virtual human body on the screen; proposing a virtual-to-real mapping algorithm based on human acupoints to address the consistency issue in human virtual-to-real mapping; introducing a multimodal intent understanding algorithm based on dynamic information entropy to address the current limitations of a single interaction mode and low intent comprehension rates in massage robot interactions. Experimental results demonstrate that the proposed multimodal massage localization algorithm achieves good massage intent recognition effects. By combining natural human-machine interaction with intent understanding, it not only accurately captures user massage intentions and assists in completing massage tasks but also reduces user psychological and cognitive loads, leading to more desirable interaction outcomes.

Comparative analysis of semantic segmentation and multi-boosted faster R-CNN for robotic tool detection in diabetes patient care

Diabetes, a common chronic illness, need meticulous patient care and treatment, which frequently necessitates advanced robotic help. The identification of robotic tools is critical in guaranteeing precise and safe medical procedures. With the growing use of robots in healthcare, reliable identification of instruments inside a patient care setting is critical for allowing smooth and efficient medical procedures. We used a Multi-Boosted Faster-Region-based Convolutional Neural Network (MBF-RCNN) to estimate Robotic Tool Detection in Diabetes Patient Care in the present research. Semantic Segmentation is a machine learning methodology centered on classifying pictures pixel-by-pixel, whereas Multi-Boosted Faster R-CNN recognizes objects using a variety of boosting strategies. Decimal Scaling Normalization is used during the preprocessing step to optimize the input data for both procedures. The effectiveness of both approaches is improved by doing extraction of features using Linear Discriminant Analysis (LDA) to find key trends in the information that is provided.The Multi-Boosted Faster-Region-based Convolutional Neural Network (MBF-RCNN) is an improved variation of the Faster R-CNN design that uses the boosting approach to improve object identification performance.Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic (ROC) curve among the assessment metrics used. The findings show that the suggested approaches are effective in identifying and localizing robotic equipment, which is critical for effective treatments in controlling diabetes.This research promotes computer vision applications in healthcare, particularly in the care of diabetic patients. It assesses the efficiency and dependability of Semantic Segmentation and MBF-RCNN for robotic tool identification, opening the way for more efficient and dependable automation for healthcare contexts.