Using Facially Expressive Robots to Calibrate Clinical Pain Perception

Robotics began roughly 30 years ago in medical applications, but it is still relatively young for biological applications. Because of the precision, accuracy and reproducibility of robotic technology, robotic interventions in medical fields, such as robotic surgery, can enable doctors to work inside the human body, which is either non-invasive or minimally invasive, with improved surgical results. The importance of medical robots in the medical sector is intended to deliver good outcomes to assist people in doing complex tasks that need a significant amount of time, accuracy, concentration, and other routines that cannot be accomplished solely through human capability. Due to advancements in AI and IoT and their convergence to AIoT, the potential of medical robots has tremendously increased in the healthcare industry. The chapter outlines the various applications of robotics in the healthcare sector, including surgical, rehabilitation, telemedicine, and diagnostic. The advantages of robotics in Healthcare are highlighted, along with the discussion on the current and future challenges in their deployment and adoption. The role of AIoT in enhancing these healthcare robots' cognitive and other capabilities is also discussed. Finally, the future of robotics in Healthcare is explored, including emerging trends and technologies, their impact on the healthcare industry, and the potential for innovation and growth.

The aim of this scoping review was to gather, summarize, and map the knowledge of peoples’ experiences on humanoid robots, capable of assisting people with activities of daily living. The review was guided by the framework of Joanna Briggs Institute and PRISMA-ScR. We found 44 papers describing nine robots that could assist with a variety of tasks listed in the International Classification of Functioning, Disability, and Health. The mixed population experienced little or no anxiety toward the robots, and most accepted the robots’ ability to monitor for safety reasons. Some participants disliked the robots’ large size and slow movements. Most of the participants found the robots easy to use. They wanted improvements in the robots’ functionality and the ability to personalize services. Several of the participants found the services interesting and useful, but not for themselves. The experiences of humanoid robotic assistance showed an insufficient level of technical readiness for assisting in physical assistance, a lack of personalization and readiness for use in home settings. The practical relevance of these findings lies in guiding future research and development toward a more individualized approach focusing on user needs and experiences to enhance the efficacy and integration of humanoid robots in health-care.

The increasing average human age and a growing number of old age population over the globe have emerged the need for automation in the health care domain. Eventually, health care robots will contribute to manage the workload of the care providers in the future health care domain. Elderly people face a major challenge to identify and pronounce names of daily use objects and different family and friends due to dementia-related issues. One of the many tasks by AI-Enabled elderly care humanoid robots will be communication with such patients. They will aid them to remember objects and persons they find difficult to identify and recognize due to dementia. This study has investigated the potential of the JD humanoid robot for object identification in such a scenario. The study has presented a prototype AI-Enabled elderly care robot that can aid elder persons to identify different objects. The robot is trained on ImageNet data set for object identification. Further, a personalized data set comprising of faces of different persons labeled by names of family and friends are also used to train robots for family recognition aid. The JD Humanoid robot captures an image of an object/person and also tracks it as the object/face moves. It identifies input images and outputs in audio format the name of the object/person to guide the elderly person. The third feature of the robot is the notifications of daily routine tasks for elder people like reminders for prayer, exercise walk, and medicine intake. Testing results have shown an object and person identification with 95% accuracy.

In this work, an online survey was used to understand the acceptability of humanoid robots and users’ needs in using these robots to assist with care among people with Alzheimer’s disease and related dementias (ADRD), their family caregivers, health care professionals, and the general public. From November 1, 2020 to March 13, 2021, a total of 631 complete responses were collected, including 80 responses from people with mild cognitive impairment or ADRD, 245 responses from caregivers and health care professionals, and 306 responses from the general public. We carried out detailed caparisons between people with ADRD, caregivers, and general public for their opinions about robot acceptance, robotic functionality, usability, and ethical issues. Overall, people with ADRD, caregivers, and the general public showed positive attitudes towards using the robot to assist with care for people with ADRD. The top three functions of robots required by the group of people with ADRD were reminders to take medicine, emergency call service, and helping contact medical services. Additionally, we included a discussion of the comments, suggestions, and concerns from the caregivers and the general public. We recognized common concerns raised by the participants, including the cost of the robot, the machine-like voice of the robot, and reduced acceptability of the robot by people with ADRD due to cognitive deficit. The results of this article are of significant relevance for the applications of social robotics in dementia care and for biomedical interventions related to AI and robotics in healthcare. Moreover, the discussions on potentials and limitations identified in this article will shed light for future design, development, and evaluation of socially assistive robots for people living with dementia.

Socially active humanoid robots (SAHRs) are designed to communicate and interact with humans in humancentric environment using speech, movements, gestures, or facial expressions to communicate with their users following some set of social behavior while providing their assistance. Just like humans interact in an adaptive manner with others by changing their speech, tone, and body language intuitively, such type of adaptive behavior can be developed in SAHRs to get a human-like rich interaction capabilities. Therefore, a lot of research work and studies are going on to replicate various behavioral aspects of humans into SAHRs, so that human-robot interaction can be improved further. Besides interacting with humans, humanoid robot should be able to perform the assigned tasks remotely and also in real time with better accuracy. Thus, these social robots designed can be used in a diversified field of applications like education, healthcare, entertainment, communication, constructions, medical, collaborations, hazard management systems, etc.

In recent years, robotics has emerged as a transformative force in the field of healthcare. As technology continues to advance, robots are playing an increasingly vital role in patient care, diagnosis, surgery, and rehabilitation. This study explores the various applications of robotics in healthcare, highlighting its potential to revolutionize the way we approach medical treatment and improve patient outcomes.

We are witnessing research expansion and application of robotics in almost all segments of human's activity, from industry to space exploration. Similar happen with humanoid robotics, but it has to be pointed out that its applications areas are mostly situated in environment close to humans. This imposes some specificities that have to be encountered. Two of them are, in our opinion, of essential importance. First one is that human environment is either loosely structured or completely unstructured (as a consequence, walking realization by programming it in advance is not possible and on-line generation of walk is inevitable), while second one is that robot's physical contact with surrounding objects, humans or other robots almost can not be avoided. If unexpected (and unwanted) such contact can be considered as disturbance and can jeopardize humanoid's dynamic balance, whose preservation is prerequisite for any biped activity. In both situations new approaches are needed. We have proposed new method for on-line walking generation based on set of predefined primitive movements that can be easily combined into more complex ones. Thus, basic leg's motion is always same (combined of primitives), and just parameters of primitives (step length, height of foot trajectory, leg bending intensity, walking velocity, …) have to be adjusted on-line during walking. The second issue, closely related to walking, is preservation of dynamic balance, either in static posture or during walking. Because existence of dynamic balance is assumed for any biped performing gait, its preservation is of primary importance. However, compensation strategies are not same for disturbances of various intensities. For real application this is extremely important and complex issue, particularly in case of large disturbances. Compensation action must simultaneously ensure two, often contradictory, requirements (to prevent immediate biped overturn and to bring system into state which enables previous activity to be continued when compensation action is over) and we propose compensation to be composed as synergetic action of more DOFs simultaneously.

In this paper, we present a method to perform tasks identification on a humanoid robot. The observed motion is compared to a set of candidate controllers that the robot might be executing. The more relevant candidate controllers are selected, and can be used as a description of the motion, or as a basis to replicate a similar motion on another robot. The analysis of the movements is based on the task-function approach and is applied in the context of humanoid robot motions. A pool of tasks is defined in order to cover the range of possible motion of the robot and the demonstration movement is projected in each candidate task of that pool. The reduced trajectory is then compared with the characteristic task trajectory using a numerical optimization process. The movement is then projected into the null space of the best task candidate. As a consequence, the motion due to the task candidate is subtracted from the original movement. This process is iterated until the result of a projection becomes null. The approach is applied to recognize the tasks performed by a HRP-2 robot in simulation, in order to disambiguate very similar-looking motions. Preliminary directions are given for possible application for human motion recognition.

This paper presents a novel approach for acquiring dynamic whole-body movements on humanoid robots focused on learning a control policy for the center of mass (CoM). In our approach, we combine both a model-based CoM controller and a model-free reinforcement learning (RL) method to acquire dynamic whole-body movements in humanoid robots. (i) To cope with high dimensionality, we use a model-based CoM controller as a basic controller that derives joint angular velocities from the desired CoM velocity. The balancing issue can also be considered in the controller. (ii) The RL method is used to acquire a controller that generates the desired CoM velocity based on the current state. To demonstrate the effectiveness of our approach, we apply it to a ball-punching task on a simulated humanoid robot model. The acquired whole-body punching movement was also demonstrated on Fujitsu's Hoap-2 humanoid robot.

Background: Robots are introduced in many health and social care settings.Objectives: To provide an overview of the existing evidence related to the views of nurses and other health and social care workers about the use of assistive humanoid and animal-like robots.Methods: Using the Joanna Briggs Institute guidelines we searched MEDLINE, PUBMED, CINHAL, EMBASE, PsycInfo, Web of Science, and IEEE Xplore digital library. Nineteen (19) articles met the criteria for inclusion.Results: Health care workers reported mixed views regarding the use of robots. They considered an array of tasks that robots could perform; they addressed the issue of patient safety and raised concerns about privacy.Conclusions: A limited number of studies have explored the views of health care workers about the use of robots. Considering the fast pace with which technology is advancing in the care field, it is critical to conduct more research in this area.Impact Statement: Robots will increasingly have a role to play in nursing, health and social care. The potential impact will be challenging for the healthcare workforce. It is therefore important for nurses and other health and social care workers to engage in discussion regarding the contribution of robots and their impact not only on nursing care but also on future roles of health and social care workers.

Integrating futuristic humanoids like Tesla Optimus into supply chain operations represents groundbreaking automation and workforce efficiency innovation. This study investigates the potential of humanoids to address critical supply chain challenges, such as labor shortages, rising operational costs, and the demand for sustainable practices. Considering its ability to handle worker-intensive, hazardous, and repetitive duties, humanoids could offer an alternative to business challenges like inefficient operations, health and safety concerns, and worker shortages. Intelligent robotics plays an essential role in improving productivity, supporting sustainability, and transforming workforce dynamics as supply chains become increasingly complex. The study examines the effects of humanoids on workforce reallocation, manufacturing sustainability, and supply chain productivity. The current research reviews the usefulness, advantages, and downsides of integrating humanoids into supply chains. This study uses a mixed-method approach, incorporating case studies, qualitative productivity data, and expert interviews. According to Tesla, Optimus could significantly enhance supply chain efficiency by reducing error rates, streamlining workflows, and enabling 24/7 operations. It could also help meet sustainability goals by lowering waste and energy consumption. The study limits Tesla’s experience, modern technologies, and inadequate information from various industrial and geographical contexts. However, this study will be eye-opening for industries requiring such humanoid robots for their operations. Additional studies need to deal with factors like high implementation expenses, potential job displacement, and flexibility in changing supply chain demands. While focused on Tesla, this study provides insights that can inform broader applications of humanoid robotics in supply chains across industries. This study presents an in-depth review of humanoid involvement in developing future supply chain models. It also offers helpful knowledge that will assist industries in considering adopting comparable robotic integration as a strategic decision.

Given the complexity and the high number of degrees of freedom humanoid robot motions often are generated off-line, stored in the robot memory and then reproduced. In the simplest cases motor commands are just replayed in open-loop (i.e. feed-forward), in more advanced implementations few parameters are changed on fly to adapt the motion to the external conditions. Indeed, several authors proposed a large variety of techniques to exploit the input from sensory feedback to modify a reference trajectory, in order to cope with environment changes and disturbances. In this paper, we propose a motion representation for humanoid robots that includes the sensory feedback information in the motion representation itself. This motion description allows stabilization against disturbances and environmental changes, but does not require any design or tuning of the relationships between sensory inputs and movement modification. We present experimental results on a simulated small humanoid robot equipped with motor encoders and touch sensors covering the whole body.

Towards a mechanisms-based approach to pain medicine.

Brain-computer interface (BCI) offers a direct communication and control channel between the human brain and external devices, presenting new pathways for individuals with physical disabilities to operate robotic arms for complex tasks. However, achieving multiobject grasping tasks under low signal-to-noise ratio (SNR) consumer-grade EEG signals is a significant challenge due to the lack of robust decoding algorithms and precise visual tracking methods. This article proposes, ArmBCIsys, an integrated robotic arm system that combines a novel dual-branch frequency-enhanced network (DBFENet) to robustly decode EEG signals under noisy conditions with the high-precision vision-guided grasping module. The proposed DBFENet designs the scaling temporal convolution block (STCB) to extract multiscale spatiotemporal features from the time domain, while the designed DropScale projected Transformer (DSPT) utilizes discrete cosine transform (DCT) to capture key frequency-domain features, significantly improving decoding robustness. We fine-tune the masked-attention mask Transformer (Mask2Former) model on the Jacquard dataset and incorporate the multiframe centroid-intersection over union (IoU) tracking algorithm to build visual grasp segmenter (VisGraspSeg), enabling reliable segmentation and dynamic tracking for diverse daily objects. Experimental validations on both self-built code-modulated visual evoked potential (c-VEP) dataset (1344 samples) and two public c-VEP datasets demonstrate that DBFENet achieves the state-of-the-art recognition performance, and the system integrates the DBFENet and proposed vision-guided module and ensures stable multiobject selecting and automatic object grasping in dynamic environments, extending promising applications in healthcare robotics, assistive technology, and industrial automation. The self-built dataset has been made publicly accessible at https://github.com/wtu1020/ ArmBCIsys-Self-built-cVEP-Dataset.

Intelligent robotic systems increasingly operate in complex, data-rich environments where real-time decision-making and human interpretability are critical. While robots generate vast volumes of sensory and operational data, the lack of effective visualization mechanisms often limits system transparency, debugging, and collaborative interaction. This research presents an enhanced framework that integrates advanced data visualization techniques with intelligent robotic systems to improve situational awareness, system interpretability, and operational efficiency. The proposed framework combines multi-modal data acquisition, intelligent data processing, adaptive visualization layers, and feedback-driven robotic control. By enabling real-time visual analytics of robotic perception, cognition, and action, the framework supports both autonomous decision-making and human-in-the-loop supervision. Experimental evaluation across simulated and real-world robotic scenarios demonstrates improved task performance, reduced error rates, and enhanced user understanding, highlighting the framework’s potential for applications in industrial automation, healthcare robotics, and smart environments.