BackgroundDementia poses a significant challenge in India. The rising incidence rates, limited resources, and restricted clinician access have contributed to a staggering 90% gap in diagnosis and care. Conversational technology provides a natural user interface with the potential to promote the independence, well-being, and safety of people living with dementia at home. However, the feasibility of implementing such technology to support people living with dementia across diverse cultural and economic settings remains underexplored.ObjectiveThis study aimed to assess the cultural feasibility of conversational robots for dementia care in India, a culturally underserved context in robotics and artificial intelligence (AI) for aging and dementia care.MethodsWe involved 29 stakeholders, including people living with dementia, caregivers, and dementia care professionals. We evaluated (1) the engagement of people living with dementia with 3 conversational robots with varying interactive modalities (a voice agent, a virtual affective robot, and an embodied robot), (2) robot acceptance, and (3) stakeholder perspectives on the benefits and challenges of deploying conversational AI in India.ResultsPeople living with dementia were willing to engage in verbal dialogue with conversational robots. Stakeholders perceived the technology as beneficial for supporting daily tasks at home, reducing loneliness, and enhancing cognitive function. We identified design adaptations to address feasibility challenges in India, including the need to (1) adapt interaction style to use a kind tone, appreciative language, and customizable facial expressions; (2) improve speech recognition for local accents interpretation and noisy settings; and (3) introduce prototypes in local clinics to promote familiarity.ConclusionsThis work offers novel insights into cultural acceptance, human-robot engagement, and perceived utility for dementia care, along with key design implications for integrating conversational AI into care settings in India.

Autism spectrum disorders (ASD) are complex neurodevelopmental conditions characterised by qualitative features in social interaction and communication. Physical therapy plays an important role in the comprehensive support of children with autism spectrum disorders (ASD), which is aimed at improving their motor skills, coordination, balance, strength and endurance, as well as correcting problems with posture and sensory perception. The aim of the study is to analyse and systematise innovative methods of physical therapy for children with autism spectrum disorders. To achieve the objectives of the study, the scientific literature was analysed and information was systematised using electronic databases such as PubMed, Google Scholar, PEDro and others. For children with autism spectrum disorders (ASD), it is highly advisable to use innovative approaches in physical therapy, such as virtual reality, exergaming, robotics, sensory integration, and ABA therapy. VR is a technology that creates interactive, computer-generated three-dimensional environments. Exergaming – is a combination of physical exercises and video game elements that creates interactive and entertaining activities that require physical activity to control the gameplay. The use of robotics involves the use of physical robots, especially social assistive robots, in physiotherapy to provide structured and predictable interaction during exercise. Sensory integration is a therapeutic approach aimed at improving the brain's ability to process and integrate sensory information. In physical therapy, SI can include specially designed activities that stimulate different sensory systems (vestibular, proprioceptive, tactile, etc.) in a controlled and safe environment. Integration of ABA principles into physical therapy can be especially useful for children with ASD who need clear instructions, visual support and positive reinforcement to successfully master motor skills and maintain engagement in therapy. Mohamed A. Abdel Ghafar and others have shown that the inclusion of virtual reality in rehabilitation helps to improve postural balance in children with autism spectrum disorder. Karin Karo's research shows that by using the FroggyBobby game complex, children reduced aimless limb movements and developed both simple and precise targeted limb movements. A study by Athanasius Kupup showed that robot-assisted interventions significantly improved the social functioning of respondents. Amel E. Abdel Karim and Amira H. Mohammed conducted a study on the effectiveness of a sensory integration programme in children with autism and showed significant improvements in gross and fine motor skills after a six-month programme. Henry Eco Prasetio and others conducted a study on the comparative effectiveness of ABA therapy and brain gymnastics on the development of gross motor skills in children with autism and proved a significant positive impact of ABA therapy on the development of gross motor skills, as opposed to brain gymnastics. Thus, the application of these innovative methods in the physical therapy of children with ASD opens up new opportunities for more effective and personalised interventions that take into account their unique needs and contribute to better functional outcomes and improved quality of life.

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Stroke remains one of the leading causes of disability and death worldwide, resulting in significant social and economic costs. Effective post-stroke rehabilitation, including physiotherapy, occupational therapy, speech therapy and psychological support, is crucial for improving motor and cognitive functions and quality of life in patients. In recent decades, there has been dynamic development of multimedia therapeutic tools, such as virtual reality (VR), robotics, mobile applications and telemedicine. These technologies allow for the personalisation of exercises, greater patient engagement and monitoring of therapy progress. The introduction of VR systems, computer games and motion controllers has revolutionised motor and cognitive rehabilitation, and the use of gamification elements increases motivation and the number of repetitions of exercises performed. A review of studies confirms the effectiveness of VR and other multimedia tools in improving upper limb function, gait, balance and cognitive function and reducing the symptoms of depression after a stroke, as well as strengthening motivation for rehabilitation. Modern technologies also facilitate performing exercises at home, which is particularly important for maintaining long-term therapeutic activity. Multimedia tools are a valuable complement to traditional methods, contributing to the intensity, effectiveness and attractiveness of the post-stroke rehabilitation process.

Background: Stroke is a major contributor to long-term disability worldwide, presenting significant obstacles to motor function, coordination, and independence. Rehabilitation approaches to those deficits include intensive motor learning strategies aimed at promoting neuroplasticity that will lead to functional recovery. This systematic review examined the effectiveness of different motor learning-based interventions in stroke rehabilitation, including, but not limited to, constraint-induced movement therapy (CIMT), task-specific training (TST), robotics-assisted therapy, virtual reality interventions, and Hand-Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE). Methods: A systematic search on PubMed, the Cochrane Library, and Google Scholar identified studies published in the last decade that examined intensive motor learning interventions with respect to stroke rehabilitation. The search yielded 4871 studies, leading to the final selection of ten studies that met strong inclusion criteria. To promote methodological rigor, the PRISMA framework was used. Primary outcome measures were motor function improvements, neuroplasticity changes, and functional independence levels. Results: The results show HABIT-ILE, TST and virtual reality-based interventions displayed significant and long-lasting improvements in motor function, coordination, and independence. CIMT needs more research and while it has promise, we do not know how it will compare long-term and the evidence is mixed regarding its effectiveness in reducing disability levels. Robotics-assisted therapy improves motor learning and strength, but there are still challenges in applying these improvements to activities of daily living (ADLs). Conclusion: It is essential to incorporate various intensive motor learning strategies instead of depending only on traditional therapy to maximize stroke rehabilitation. Interventions such as HABIT-ILE and task-specific training show great promise, and new technologies like virtual reality and robotics provide extra advantages. Nonetheless, more research is necessary to improve intervention protocols, create standardized outcome measures, and design personalized rehabilitation strategies to enhance motor recovery for stroke survivors. Categories: Physical Medicine & Rehabilitation, Therapeutics, Motor Learning, Neurorehabilitation

Cooperative relationships between humans and agents are becoming more important for the social coexistence of anthropomorphic agents, including virtual agents and robots. One way to improve the relationship between humans and agents is for humans to empathize with agents. Empathy can increase human acceptance. In this study, we focus on the IKEA effect in creating agents and examine empathy through interpersonal relationships. We conducted a robot assembly task in which participants either cooperatively built the same robot or individually assembled their own. The results showed that the IKEA effect promoted empathy toward the agent regardless of the relationship between participants. However, participants did not show a significant change in empathy levels from before to after the task. These results suggest that regardless of the relationship between participants, the IKEA effect can promote empathy toward the agent.

ABSTRACT This paper presents a new design and navigation framework for a legged quadruped robot. The different reactive behaviour-based approaches are built for quadruped robot to avoid obstacles in different unknown environments. The proposed navigational strategy is implemented by using a fuzzy controller integrated with the gap tracking technique. The fuzzy inference system is designed with 40 rules, with three input and two output variables. A light detection and ranging (LiDAR) sensor is used to locate the obstacles and gaps in the environment. The sensor-extracted data sets are provided as input to the fuzzy controller to determine the robot’s safe movement in the environments. The validity of the proposed navigation model is simulated in the virtual robot experimental platform (V-REP) platform before a test in a real-time environment. The final results are analysed in real-time by considering different experiments. The findings indicate that the robot moves in a gap using the proposed navigational method and avoids obstacles with satisfactory intelligent performance.

Non-contact surface processing does not involve direct contact between the tool and a worksurface. An industrial robot mostly uses preplanned toolpaths to perform non-contact surface processing. A preplanned toolpath may work well in repetitive conditions but may easily become inaccurate and unsafe if the tool needs to follow unknown worksurface variations. Many industrial processes, e.g., painting, coating, and sandblasting, typically involve worksurfaces with unknown variations. This study proposes an optimal toolpath planning method for an industrial robot equipped with end-of-arm distance sensors to automatically guide its tool motion along unknown worksurface variations. The distance sensors facilitate sparse sensing to acquire sparse data that is just enough for the quick and adequate perception of unknown worksurfaces by requiring fewer measurements and less computing. Optimization facilitates the optimality of multi-objective toolpath planning with a customizable value function, where the multiple objectives comprise adapting to unknown worksurface variations and traveling between known tool targets. To validate the proposed toolpath planning method, this study conducts a simulation experiment on a virtual robot with four end-of-arm distance sensors and a workpiece with unknown surface variations. The experimental results indicate that the proposed method is accurate and near-optimal even in the presence of sensor noises.

Abstract With the rapid development of intelligent manufacturing field, robot modeling and simulation technology plays a crucial role in improving production efficiency and quality. However, the existing simulation methods have poor modeling capability, poor scalability and openness when dealing with complex robot systems, and cannot meet the requirements of fast and accurate robot control. In this paper, a robot virtual modeling and trajectory planning simulation method is proposed, using the powerful computational ability of MATLAB and the advantages of Virtual Robot Experimentation Platform (V-REP) physical attribute simulation. It imports the model created in Solidworks into the V-REP 3D simulation environment, builds a virtual prototype of the Delta robot, and uses the Non-uniform rational B-spline (NURBS) spline curve method to accurately plan and simulate the robot’s trajectory in MATLAB software. The robot trajectory was accurately planned and simulated using the NURBS spline curve method in MATLAB software. The experimental results prove that the method can generate accurate parallel robot motion paths according to the robot motion parameters, and the method can effectively shorten the design cycle, improve the efficiency of research and development, and provide an efficient means for robot motion simulation, as well as provide a certain reference for similar simulations.

Deep reinforcement learning (DRL), as an important branch of machine learning, has shown strong potential for application in complex environmental decision-making problems in recent years. This article systematically reviews the current application status and development trends of DRL in fields such as gaming and virtual environments, robot control, resource management, and healthcare. Through a comprehensive analysis of existing literature, this paper has summarized the technical roadmap and solutions of DRL in addressing core challenges such as high-dimensional state spaces, sparse rewards, and partial observability, including hierarchical reinforcement learning frameworks, mixed reward designs, and memory based reinforcement learning methods. Meanwhile, this article delves into the opportunities and challenges faced by cutting-edge research directions such as multi-agent systems, security, and interpretability. Based on current research progress, possible paths for the future development of DRL have been proposed, including improving algorithm robustness, integrating interdisciplinary methods, and engineering considerations in practical deployment, and providing reference for the further development of deep reinforcement learning.

Objective: The aim of this study is to assess and analyze students' needs regarding the learning of virtual robotics. As virtual technologies gain prominence in education, especially in robotics and engineering fields, understanding learners’ expectations and barriers becomes essential for designing effective curricula. Method: A structured survey was conducted among students at Bukhara State University to evaluate their familiarity, interest, and challenges related to virtual robotics education. The survey included quantitative Likert-scale questions and qualitative open-ended responses. Collected data were statistically analyzed, and key indicators were visualized through bar charts and pie graphs to enhance interpretability. Results: The analysis showed that over 65% of students expressed a high level of interest in virtual robotics, with accessibility and interactivity being the most appreciated features. Motivation levels were found to increase after exposure to simulation-based learning modules. Graphical data illustrated a clear shift in student engagement—from moderate to high—before and after the virtual course. However, gaps in digital literacy and access to devices were noted as limiting factors for some learners. Novelty: This study is among the first to combine statistical and graphical analysis to assess the virtual robotics readiness of Uzbek university students. The integration of visual data enhances the clarity and credibility of the findings. The results offer practical implications for educators seeking to implement virtual learning platforms in STEM disciplines within developing regions.

Virtual Educational Robotics (VER) consists of using virtual platforms where students can design, build, program, and operate robots, serving as a replacement for traditional educational robotics that utilizes physical kits. This paper proposes the design and implementation of an educational strategy that utilizes this type of technology to determine its impact on the teaching and learning processes in selected mathematical topics within secondary education in Mexico. A quasi-experimental research study was conducted to assess the impact of virtual educational robotics on specific topics in Analytical Geometry in secondary education in Mexico. A pretest and post-test were administered to two groups (experimental and control), and it was found that the experimental group achieved better results in the specific topics of Analytical Geometry. Although the overall mean of the control group was slightly higher, it can be concluded that the use of VEXcode VR improved learning compared to traditional didactic strategies.

Accurately and reliably detecting and recognizing human body movements in real time, relaying appropriate commands to the machine, have substantial implications for virtual reality, remote control, and robotics applications. Nonetheless, most contemporary wearable analysis and control systems attain action recognition by setting sensor thresholds. In routine usage, the stringent trigger conditions facilitate inadvertent contact, resulting in a poorer user experience. Here, we have created a wearable intelligent gesture recognition control system utilizing a multilayer microstructure composite thin film piezoresistive sensing array and deep learning techniques. The system exhibits ultrahigh sensitivity (ranging from 0-6 kPa to 412.2 kPa-1) and rapid response times (loading at 40 ms, recovery at 30 ms). The detected gestures are classified and recognized via a convolutional neural network, achieving a recognition accuracy of 97.5%. Ultimately, the altitude control of an unmanned aerial vehicle is accomplished through wireless signal transmission and reception. To achieve the visualization of the complete gesture-controlled flight process, we developed an intuitive user interface for the real-time display of flight altitude and video surveillance. The implementation of this recognition system introduces a novel control mechanism for human-machine interaction, expands the applications of robotic technology, and offers innovative concepts and practical pathways for virtual reality.

The use of wearable robots has been widely adopted in rehabilitation training for patients with hand motor impairments. However, the uniqueness of patients' muscle loss is often overlooked. Leveraging reinforcement learning and a biologically accurate musculoskeletal model in simulation, we propose a customized wearable robotic controller that is able to address specific muscle deficits and to provide compensation for hand-object manipulation tasks. Video data of a same subject performing human grasping tasks is used to train a manipulation model through learning from demonstration. This manipulation model is subsequently fine-tuned to perform object-specific interaction tasks. The muscle forces in the musculoskeletal manipulation model are then weakened to simulate neurological motor impairments, which are later compensated by the actuation of a virtual wearable robotics glove. Results shows that integrating the virtual wearable robotic glove provides shared assistance to support the hand manipulator with weakened muscle forces. The learned exoglove controller achieved an average of 90.5 % of the original manipulation proficiency.

This study introduces the REHAB-PAL system, an interactive solution for home-based rehabilitation in children with cerebral palsy. To verify its functioning and usability, the system was tested with physical therapists and rehabilitation specialists simulating patient roles. The system was evaluated for its ability to track users' movements during rehabilitation exercises and assess the quality of their performance. A look-up table was developed to correlate kinematic movement characteristics with clinical scores that quantify exercise quality. To enhance exercise performance and promote adherence, the system integrates NAO social robot, programmed to provide verbal instructions, demonstrate exercises, and deliver personalized feedback based on users' movement quality. Feedback delivery was guided by a decisional strategy, prioritizing specific kinematic variables tracked by the system. Participants were asked to perform a rehabilitation session under three experimental conditions: 1) Interaction with the physical NAO robot, 2) Interaction with a virtual robot displayed on a computer screen, and 3) Receiving instructions without the use of a robot. The REHAB-PAL system accurately detected participants' kinematics and automatically generated correct movement scores. Questionnaires revealed participants' preference for the interaction with the physical robot, associated with highest levels of engagement and seen as the most effective means of delivering exercise instructions.

This study examines the development of oral communication skills in a second language (L2) context. This research employs an orally given text-based generative AI (GenAI) model as its methodology. The population in this study was 505 students at teaching and educational science faculties in universities. The demographics and sample for this study consisted of 25 undergraduate students who are majoring in Indonesian language education and 26 students who are from the Pancasila education and public health study program at Al Asyariah Mandar University, Indonesia in the 2022 academic year. They were selected via a purposive sampling method. The instruments utilized in this quantitative research are a questionnaire and observation. The research results indicated that the students' choice of interactions with virtual robots continued to improve their English communication skills, including vocabulary, intonation, gesture, and fluently dan volume. They also believe that AI can enhance their learning autonomy, critical thinking abilities, and confidence in practicing English effectively and quickly. This research contribution provides insight into the importance of using AI-robot technology so that participants achieve learning outcomes, making learning more enjoyable, the importance of soft skills for the cognitive process of language acquisition, and collaboratively foster communicative competencies in the 21st century.

A cyber–physical multi-robot system integrates robotic agents that share data over communication networks in real time to achieve common objectives by making decisions collectively based on the knowledge of their surroundings. This work introduces a formation control strategy for two groups of mobile robots placed in two separate workspaces connected by a communication network. The control technique generates two similar formations on each workspace using virtual agents that mirror the behavior of the corresponding physical robot in the opposite workspace. Control laws are derived for a single integrator and unicycle-type real and virtual robots that converge to the desired formation, even in the presence of communication delays. The numerical simulations performed show the convergence of the control strategy. A low-cost cyber–physical micro-robot platform was developed to run experiments with real robots. The setup uses a camera as a position and orientation sensor and the MQTT protocol for server communication and data exchange. Results obtained on this platform show the feasibility of the approach in an actual physical setting.

Revealing the evolved mechanisms that give rise to collective behavior is a central objective in the study of cellular and organismal systems. In addition, understanding the algorithmic basis of social interactions in a causal and quantitative way offers an important foundation for subsequently quantifying social deficits. Here, with virtual reality technology, we used virtual robot fish to reverse engineer the sensory-motor control of social response during schooling in a vertebrate model: juvenile zebrafish (Danio rerio). In addition to providing a highly controlled means to understand how zebrafish translate visual input into movement decisions, networking our systems allowed real fish to swim and interact together in the same virtual world. Thus, we were able to directly test models of social interactions in situ. A key feature of social response is shown to be single- and multitarget-oriented pursuit. This is based on an egocentric representation of the positional information of conspecifics and is highly robust to incomplete sensory input. We demonstrated, including with a Turing test and a scalability test for pursuit behavior, that all key features of this behavior are accounted for by individuals following a simple experimentally derived proportional derivative control law, which we termed "BioPD." Because target pursuit is key to effective control of autonomous vehicles, we evaluated-as a proof of principle-the potential use of this simple evolved control law for human-engineered systems. In doing so, we found close-to-optimal pursuit performance in autonomous vehicle (terrestrial, airborne, and watercraft) pursuit while requiring limited system-specific tuning or optimization.

Many people with stroke experience incomplete recoveries, leaving them with upper extremity (UE) deficits affecting their long-term independence. Interventions including virtual reality (VR) and robotics have been developed to foster neuroplasticity post stroke. Few of the many studies examining these interventions consider the impact of both timing and dosage. The primary aim of this randomized controlled trial was to investigate (1) dosage and (2) timing of UE VR/robotic training in the subacute period post stroke. 100 participants were consented 5–30 days after stroke. They were randomized to an Early (first month) or Delayed (second month) VR/robotic group (EVR/DVR), a dose matched usual care group (DMUC) or a usual care group (UC). Participants were evaluated using impairment, motor function, and quality of life measures immediately before, after, and 1 month after training, and 4 and 6 months post stroke. At 4 months post stroke the DVR group showed a higher rate of change from baseline on the Action Research Arm Test compared to the EVR group. This difference was not sustained; none of the training groups demonstrated significantly better scores on any outcome measure 6 months post stroke. Growth mixture modeling revealed three groups with patterns of recovery associated with early finger movement. At 6 months post stroke, the EuroQol was moderately correlated with impairment and activity.

The power of anaesthesia is a controlled hypnotic state that is generated to reduce pain during surgery and work with the surgeon. That's why many people die. Improper use of anaesthesia can lead to blindness, breathing arrest, brain damage and even death. The impact varies from patient to patient. Our project is based on integrated smart anaesthesia management integrated with virtual medical robots and is a cutting-edge approach in modern healthcare systems. They provide recommendations based on current guidelines, historical data and ongoing real-time patient reviews to support critical medical decisions in their operation. IoT sensors are continuously embedded in anaesthesia and care devices important information such as heart rate, blood pressure, oxygen saturation, and anaesthesia depth. This data is safely transferred to the virtual doctor system for analysis. Provides insight into optimal anaesthesia adjustment, predicts patient responses, and informs health service providers about important situations that require immediate attention. Finally, the integrated IoT anaesthesia management and virtual health systems represent a transformative approach to the delivery of healthcare that IoT uses to achieve safe anaesthesia practices, continuous patient monitoring, and accessible medical knowledge regardless of physical proximity.