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Background: Socially assistive robots introduced in nursing care settings have multidimensional psychological impacts on care recipients and caregivers. This study aims to explore the longitudinal changes induced by socially assistive robots, focusing on a chain of human behaviors. Methods: In this qualitative study, nine participants from two nursing homes who had experience in manipulating socially assistive robots were interviewed in a semi-structured focus group using a topic guide to explore the changes in care recipients and caregivers. Following the framework analysis method, the transcripts were coded using the International Classification of Functioning, Disability, and Health (ICF). The identified codes were charted for each side—care recipients and caregivers sides— using a causal loop diagram, a tool used to visualize nonlinear dynamics in complex systems. Results: Three and seven kinds of codes in the domains of "body functions" and "activities and participation", respectively, were identified on the care recipients' side; whereas on the caregivers' side, one and five kinds of codes in the domains of "body functions" and "activities and participation", respectively, were found. The codes obtained from the facility with longer experience were represented graphically as a reinforcing feedback loop, in which favorable changes were amplified in a chain of events. Robot use directly changed the mental functions of care recipients, and the caregivers' perceptions of these positive changes led to their own emotional and behavioral changes, which would reduce the burden of care. Moreover, the findings suggest that sharing information regarding these changes and objectively recognizing the effectiveness of robots among staff members can be the key to continuous robot use in nursing care settings. Conversely, the figure obtained from the novice facility shows fragmented chain relationships of the codes, indicating that all the effects of robot use are recognizable and form a chain reaction after continuous robot use for more than several months. Conclusions: This study revealed important aspects regarding the longitudinal effects of socially assistive robots in nursing care settings. These implications should be broadly implemented for effective robot use and reduction of the burden of care in nursing care settings. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

The article discusses the importance of assistive robotic technologies for individuals with disabilities, focusing on the challenges related to gait and mobility. It highlights the need for accurate classification of walking terrains to enhance the control systems of exoskeletons and prostheses. The authors introduce L-AVATeD, a novel dataset that captures visual and LiDAR images of various walking terrains, providing valuable data for researchers and engineers working on gait analysis and robotic prostheses. The dataset includes detailed descriptions of terrain types, data collection methods, and post-processing techniques, aiming to improve the understanding and classification of walking terrains for diverse applications. [Extracted from the article]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing number of individuals with disabilities—over 61 million adults in the United States alone—underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and often require assistance from another person with activities of daily living (ADLs), such as eating, grooming, and dressing. Wheelchair-mounted assistive robotic arms offer a promising solution to enhance independence, but their complex control interfaces can be challenging for users. Automating control through deep learning-based object detection models presents a viable pathway to simplify operation, yet progress is impeded by the absence of specialized datasets tailored for ADL objects suitable for robotic manipulation in home environments. To bridge this gap, we present a novel ADL object dataset explicitly designed for training deep learning models in assistive robotic applications. We curated over 112,000 high-quality images from four major open-source datasets—COCO, Open Images, LVIS, and Roboflow Universe—focusing on objects pertinent to daily living tasks. Annotations were standardized to the YOLO Darknet format, and data quality was enhanced through a rigorous filtering process involving a pre-trained YOLOv5x model and manual validation. Our dataset provides a valuable resource that facilitates the development of more effective and user-friendly semi-autonomous control systems for assistive robots. By offering a focused collection of ADL-related objects, we aim to advance assistive technologies that empower individuals with mobility impairments, addressing a pressing societal need and laying the foundation for future innovations in human–robot interaction within home settings. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Socially assistive robots represent a promising tool in assistive contexts for improving people's quality of life and well-being through social, emotional, cognitive, and physical support. However, the effectiveness of interactions heavily relies on the robots' ability to adapt to the needs of the assisted individuals and to offer support proactively, before it is explicitly requested. Previous work has primarily focused on defining the actions the robot should perform, rather than considering when to act and how confident it should be in a given situation. To address this gap, this paper introduces a new data-driven framework that involves a learning pipeline, consisting of two phases, with the ultimate goal of training an algorithm based on Influence Diagrams. The proposed assistance scenario involves a sequential memory game, where the robot autonomously learns what assistance to provide when to intervene, and with what confidence to take control. The results from a user study showed that the proactive behaviour of the robot had a positive impact on the users' game performance. Users obtained higher scores, made fewer mistakes, and requested less assistance from the robot. The study also highlighted the robot's ability to provide assistance tailored to users' specific needs and anticipate their requests. [ABSTRACT FROM AUTHOR]

Introduction: Assistive robots and human-robot interaction have become integral parts of sports training. However, existing methods often fail to provide real-time and accurate feedback, and they often lack integration of comprehensive multi-modal data. Methods: To address these issues, we propose a groundbreaking and innovative approach: CAM-Vtrans--Cross-Attention Multi-modal Visual Transformer. By leveraging the strengths of state-of-the-art techniques such as Visual Transformers (ViT) andmodels like CLIP, along with cross-attentionmechanisms, CAM-Vtrans harnesses the power of visual and textual information to provide athletes with highly accurate and timely feedback. Through the utilization of multi-modal robot data, CAM-Vtrans offers valuable assistance, enabling athletes to optimize their performance while minimizing potential injury risks. This novel approach represents a significant advancement in the field, offering an innovative solution to overcome the limitations of existing methods and enhance the precision and efficiency of sports training programs. [ABSTRACT FROM AUTHOR]

With the progression of an aging society, the importance of walking assistance technology has been increasing. The research and development of robotic walkers for individuals requiring walking support are advancing. However, there was a problem that the conventional constant support amount did not satisfy the propulsion force required for the walking speed that users wanted. In this study, in order to solve this problem, we propose an algorithm for determining the support amount to maintain the walking speed when the average walking speed of each user is set as the target speed. A robotic walker was developed by attaching BLDC motors to an actual walker, along with a control algorithm for assistance based on sampling-type PID control. The effectiveness of the assistance determination algorithm and the usefulness of the parameters were demonstrated through experiments using weights loaded on the forearm support and target speeds. Subsequently, subject experiments were conducted to verify the ability to maintain target speeds, and a questionnaire survey confirmed that the assistance did not interfere with actual walking. The proposed algorithm for determining the assistance levels demonstrated the ability to maintain target speeds and allowed for adjustments in the necessary level of assistance. [ABSTRACT FROM AUTHOR]

Care ethics has been advanced as a suitable framework for evaluating the ethical significance of assistive robotics. One of the most prominent care ethical contributions to the ethical assessment of assistive robots comes through the work of Aimee Van Wynsberghe, who has developed the Care-Centred Value-Sensitive Design framework (CCVSD) in order to incorporate care values into the design of assistive robots. Building upon the care ethics work of Joan Tronto, CCVSD has been able to highlight a number of ways in which care practices can undergo significant ethical transformations upon the introduction of assistive robots. In this paper, we too build upon the work of Tronto in an effort to enrich the CCVSD framework. Combining insights from Tronto’s work with the sociological concept of emotional labor, we argue that CCVSD remains underdeveloped with respect to the impact robots may have on the emotional labor required by paid care workers. Emotional labor consists of the managing of emotions and of emotional bonding, both of which signify a demanding yet potentially fulfilling dimension of paid care work. Because of the conditions in which care labor is performed nowadays, emotional labor is also susceptible to exploitation. While CCVSD can acknowledge some manifestations of unrecognized emotional labor in care delivery, it remains limited in capturing the structural conditions that fuel this vulnerability to exploitation. We propose that the idea of privileged irresponsibility, coined by Tronto, helps to understand how the exploitation of emotional labor can be prone to happen in roboticized care practices. [ABSTRACT FROM AUTHOR]

Physical diseases have become an alarming social problem worldwide. Several conditions related to limb mobility are more frequent due to a high level of demand in the repetitive execution of activities. Assistive technologies have been proposed to improve the experience of physical rehabilitation. This paper presents a framework with the mechanical design criteria for an upper limb assistive robotic system. We define the design specifications and construction characteristics of an assistive system. As a result, a 7-degree-of-freedom system for upper limb physical rehabilitation is designed with a novel characteristic of arm-switching configuration for the treatment of both arms. [ABSTRACT FROM AUTHOR]

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times ( p < 0.001 ). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use ( p = 0.04 ). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements. [ABSTRACT FROM AUTHOR]

As the number of elderly population in the community grows, more efficient and precise recreation and exercise aids are needed to safeguard their quality of life. The study proposes a control model based on an improved dense trajectory algorithm to enhance the recognition and response capabilities of recreation and exercise assistance robots. The main method of the model is to improve the dense trajectory algorithm to enhance its recognition speed and accuracy for complex and small movements. Specifically, the study deeply refined the control process of a health and exercise assisted robot, and combined action capture to construct a health and exercise assisted robot model. The control model of the health and exercise assisted robot was optimized using an improved dense algorithm. The improved dense trajectory algorithm has feature embedding and attention mechanism, which can supplement the input data of the model, thus enabling more accurate action recognition. The results show that among the five samples, the recreation effectiveness score of the experimental group averaged 8.9, which was significantly higher than that of the control group, which was 7.3. The recognition accuracy has been improved by 2.7 % and 3.9 %, respectively, effectively suppressing the influence of camera motion. After using the improved dense trajectory algorithm, the fitness of the health training assistant robot reached 96.25 % under the same processing time, which is 8.93 % higher than the traditional model's fitness of 87.32 %. In summary, the control model of a community elderly health exercise assistance robot based on improved dense trajectory algorithm has achieved more accurate and faster recognition and response to the actions of the elderly, providing a more efficient technical means for health exercise and improving the health effect of the elderly.

Abstract Physical assistive robotics are oriented to support and improve functional capacities of people. In physical rehabilitation, robots are indeed useful for functional recovery of affected limb. However, there are still open questions related to technological aspects. This work presents a systematic review of upper limb rehabilitation robotics in order to analyze and establish technological challenges and future directions in this area. A bibliometric analysis was performed for the systematic literature review. Literature from the last six years, conducted between August 2020 and May 2021, was reviewed. The methodology for the literature search and a bibliometric analysis of the metadata are presented. After a preliminary search resulted in 820 articles, a total of 66 articles were included. A concurrency network and bibliographic analysis were provided. And an analysis of occurrences, taxonomy, and rehabilitation robotics reported in the literature is presented. This review aims to provide to the scientific community an overview of the state of the art in assistive robotics for upper limb physical rehabilitation. The literature analysis allows access to a gap of unexplored options to define the technological prospects applied to upper limb physical rehabilitation robotics.

Physical assistive robotics are oriented to support and improve functional capacities of people. In physical rehabilitation, robots are indeed useful for functional recovery of affected limb. However, there are still open questions related to technological aspects. This work presents a systematic review of upper limb rehabilitation robotics in order to analyze and establish technological challenges and future directions in this area. A bibliometric analysis was performed for the systematic literature review. Literature from the last six years, conducted between August 2020 and May 2021, was reviewed. The methodology for the literature search and a bibliometric analysis of the metadata are presented. After a preliminary search resulted in 820 articles, a total of 66 articles were included. A concurrency network and bibliographic analysis were provided. And an analysis of occurrences, taxonomy, and rehabilitation robotics reported in the literature is presented. This review aims to provide to the scientific community an overview of the state of the art in assistive robotics for upper limb physical rehabilitation. The literature analysis allows access to a gap of unexplored options to define the technological prospects applied to upper limb physical rehabilitation robotics. [ABSTRACT FROM AUTHOR]

Care-giving and assistive robotics, driven by advancements in AI, offer promising solutions to meet the growing demand for care, particularly in the context of increasing numbers of individuals requiring assistance. It creates a pressing need for efficient and safe assistive devices, particularly in light of heightened demand due to war-related injuries. While cost has been a barrier to accessibility, technological progress can democratize these solutions. Safety remains a paramount concern, especially given the intricate interactions between assistive robots and humans. This study explores the application of reinforcement learning (RL) and imitation learning in improving policy design for assistive robots. The proposed approach makes the risky policies safer without additional environmental interactions. The enhancement of the conventional RL approaches in tasks related to assistive robotics is demonstrated through experimentation using simulated environments. [ABSTRACT FROM AUTHOR]

In this chapter, we discuss the merits and challenges of organic communication within an inclusive, iterative research design through our research project 'Improving the quality of life of families with parents with intellectual disabilities (IDs) and their children (,12 years) by means of assistive robotics'. We will discuss the research process, its key steps and preliminary findings, as well as how inclusivity of participants was taken along throughout this process. The links between research design, designers and project participants are explored and reflected on. Our positions as researchers within the research process are also reflected upon. We will additionally address the implications of our research for the broader field of inclusive design for assistive robotics and the creative methodologies employed and tailored to the needs of families headed by parents with an ID and their children. In this, we keep a close eye on the difficulties such families face within the context of our research project. Lastly, we reflect on several key markers of collaboration within marginalised communities we encountered in our research. [ABSTRACT FROM AUTHOR]

The increasing number of individuals with disabilities—over 61 million adults in the United States alone—underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and often require assistance from another person with activities of daily living (ADLs), such as eating, grooming, and dressing. Wheelchair-mounted assistive robotic arms offer a promising solution to enhance independence, but their complex control interfaces can be challenging for users. Automating control through deep learning-based object detection models presents a viable pathway to simplify operation, yet progress is impeded by the absence of specialized datasets tailored for ADL objects suitable for robotic manipulation in home environments. To bridge this gap, we present a novel ADL object dataset explicitly designed for training deep learning models in assistive robotic applications. We curated over 112,000 high-quality images from four major open-source datasets—COCO, Open Images, LVIS, and Roboflow Universe—focusing on objects pertinent to daily living tasks. Annotations were standardized to the YOLO Darknet format, and data quality was enhanced through a rigorous filtering process involving a pre-trained YOLOv5x model and manual validation. Our dataset provides a valuable resource that facilitates the development of more effective and user-friendly semi-autonomous control systems for assistive robots. By offering a focused collection of ADL-related objects, we aim to advance assistive technologies that empower individuals with mobility impairments, addressing a pressing societal need and laying the foundation for future innovations in human–robot interaction within home settings.

During robot-assisted therapy, a robot typically needs to be partially or fully controlled by therapists, for instance using a Wizard-of-Oz protocol; this makes therapeutic sessions tedious to conduct, as therapists cannot fully focus on the interaction with the person under therapy. In this work, we develop a learning- based behaviour model that can be used to increase the autonomy of a robot's decision-making process. We investigate reinforcement learning as a model training technique and compare different reward functions that consider a user's engagement and activity performance. We also analyse various strategies that aim to make the learning process more tractable, namely i) behaviour model training with a learned user model, ii) policy transfer between user groups, and iii) policy learning from expert feedback. We demonstrate that policy transfer can significantly speed up the policy learning process, although the reward function has an important effect on the actions that a robot can choose. Although the main focus of this paper is the personalisation pipeline itself, we further evaluate the learned behaviour models in a small-scale real-world feasibility study in which six users participated in a sequence learning game with an assistive robot. The results of this study seem to suggest that learning from guidance may result in the most adequate policies in terms of increasing the engagement and game performance of users, but a large-scale user study is needed to verify the validity of that observation. [ABSTRACT FROM AUTHOR]

Developing an authentic understanding of potential users' needs and translating these into usable categories as an input to research and development is an open problem. It is generally accepted that genuine knowledge of user needs is essential for the creation of any new technology. For assistive robots, however, this knowledge is even more important for two key reasons. First, because the form and function of these technologies is still in the process of negotiation, and second, because assistive robots are ultimately intended for a vulnerable population. In this paper, we describe a number of existing strategies to address this challenge and discuss some of their shortcomings, including a loss of data richness and context, the stereotyping of users and a lack of transparency and traceability. The primary contribution of this paper is a novel Authentic Citations process for capturing needs which aims to address these shortcomings. This process involves a thematic analysis of complex qualitative data to derive robotics needs for older people, which emphasises the retention of the original situated description, or 'authentic citation', for ongoing sensitising and grounding at all stages of the research and development cycle, and by various stakeholders. The Authentic Citations process adds additional rigour to a process that can be tacit and opaque and can be used by robotics researchers to analyse and translate qualitative research into usable categories. An additional contribution of this paper is an initial outline of a taxonomy of assistive robotics needs for older people, which contributes to improving the understanding of the user as a situated and complex person and can be used as an input to design. [ABSTRACT FROM AUTHOR]

Hand exoskeletons are potential solutions for enhancing upper extremity function after stroke, yet achieving intuitive control remains challenging. We recently showed that isometric grip force tracking is preserved after stroke, providing a possible control source for a hand exoskeleton. In this study, we developed a hand exoskeleton with a soft compliant mechanism and novel force control strategy that leverages isometric grip force control of digits 3–5 to control an index–thumb pinch grip. We first present characterization of the compliant mechanisms output impedance (34.77 N/m), and output force (2.3 ± 0.57 N). We then present results of a study that assessed the intuitiveness of the strategy during a grip–lift–move task in ten unimpaired individuals. From four unimpaired individuals we also gathered user preferences on force sensitivity and operating mode, where in one mode flexion force from digits 3–5 caused index finger closing, while in the other mode it caused index finger opening. The strategy proved intuitive, improving movement frequency on the grip–lift–move task by 30%. Users preferred greater force sensitivity and using flexion force from digits 3–5 to drive index finger extension. The force control strategy incorporated into the exoskeleton shows promise warranting further investigation in neurologically impaired participants. [ABSTRACT FROM AUTHOR]

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible. [ABSTRACT FROM AUTHOR]

The landscape of neurorehabilitation is undergoing a profound transformation with the integration of artificial intelligence (AI)-driven robotics. This review addresses the pressing need for advancements in pediatric neurorehabilitation and underscores the pivotal role of AI-driven robotics in addressing existing gaps. By leveraging AI technologies, robotic systems can transcend the limitations of preprogrammed guidelines and adapt to individual patient needs, thereby fostering patient-centric care. This review explores recent strides in social and diagnostic robotics, physical therapy, assistive robotics, smart interfaces, and cognitive training within the context of pediatric neurorehabilitation. Furthermore, it examines the impact of emerging AI techniques, including artificial emotional intelligence, interactive reinforcement learning, and natural language processing, on enhancing cooperative neurorehabilitation outcomes. Importantly, the review underscores the imperative of responsible AI deployment and emphasizes the significance of unbiased, explainable, and interpretable models in fostering adaptability and effectiveness in pediatric neurorehabilitation settings. In conclusion, this review provides a comprehensive overview of the evolving landscape of AI-driven robotics in pediatric neurorehabilitation and offers valuable insights for clinicians, researchers, and policymakers. [ABSTRACT FROM AUTHOR]

One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential. [ABSTRACT FROM AUTHOR]

Immersive media, particularly Extended Reality (XR), is at the forefront of revolutionizing the healthcare industry. Healthcare provides XR with “silver bullet” use cases that add value and societal effect to the technology. Healthcare interventions frequently require imaging or visualization to be applied correctly, and the sensation of presence that XR can provide is crucial as a training aid for healthcare learners. From anatomy to surgical training, multimodal immersion in the reality of a medical situation increases the impact of an XR resource compared to the usual approach. Thus, healthcare has become a specialized focus for the immersive media sector, with a multitude of development and research underway. This research subject, which followed on from the previous one, yielded an eclectic group of works spanning the gamut of immersive media applications in healthcare. The underlying theme in these works remains a consistent focus on calibrating, validating, verifying, and standardizing procedures, instruments, and technologies in order to constantly rigorously streamline the means and materials that will integrate immersive technologies in healthcare. In that spirit, we share the findings from this research topic as a motivator for rigorous and evidence-based use of immersive media in digital and connected health.

Currently wearable assistive robot has been widely researched and applied in many fields. Aiming at the problems of difficult and time-consuming handling of goods, a wearable assistive robot using automatic dynamic analysis of mechanical systems is designed. The robot is assisted by robot operating system. The robot uses robot operating system to assist the automatic dynamic analysis of mechanical systems system, and the mechanical structure of the robot is analyzed and designed. Then the robot structure is simulated and analyzed by the software to explore the practical application of the robot. The results of the study indicated that the height of the robot’s air drive affected the effectiveness of the robot’s application. The angle of the robot could reach 36.2° at 0mm height, 53.6° at 50mm and 66.7° at 100mm. At the same time, the maximum force of the robot’s stand could reach 191.5N. The amount of robot’s assisting force varied in different handling goods. The actual force applied by the robot varied in different arm positions. However, the actual structural design of the robot is in line with the actual application effect. It can be concluded that the robot designed by the system conforms to the actual application structure and can realize the actual booster assistance effect. This is a good guide for the assistive control and structure analysis of the assistive robot.

Distinguishable vibrotactile feedback can convey information non-verbally and complete the sensory loop when using assistive devices. Feedback can increase acceptance of assistive devices but could require personalization as these devices need to adapt to user capabilities and preferences that may affect the location for inducing feedback. Developing personalized feedback for each user may be ideal, but impractical if demand for these devices increases. In this research, we evaluate the hypothesis that the ability to define, generate, and use personalized feedback is preferred and should be provided. The hypothesis is evaluated using a system capable of capturing and recognizing non-verbal inputs, providing vibrotactile feedback to verify the identified action, and executing the verified action. Evaluation focuses on determining the ability to initiate actions using the system architecture, and define, generate, and use personalized feedback. In addition to recording performance metrics (success or failure), the participants also completed the NASA TLX and post-evaluation questionnaires. Based on results, the actions can be initiated using the system architecture, and defining, generating, and using personalized feedback was found to be preferable with the success rate reaching 100% after only three successive repetitions, and could decrease mental demand and effort while interacting with the system.

Children worldwide are becoming increasingly inactive, leading to significant wellness challenges. Initial findings from our research team indicate that robots could potentially provide a more effective approach (compared to other age-appropriate toys) for encouraging physical activity in children. However, the basis of this past work relied on either interactions with groups of children (making it challenging to isolate specific factors that influenced activity levels) or a preliminary version of results of the present study (which centered on just a single more exploratory method for assessing child movement). This paper delves into more controlled interactions involving a single robot and a child participant, while also considering observations over an extended period to mitigate the influence of novelty on the study outcomes. We discuss the outcomes of a two-month-long deployment, during which $N=8$ participants engaged with our custom robot, GoBot, in weekly sessions. During each session, the children experienced three different conditions: a teleoperated robot mode, a semi-autonomous robot mode, and a control condition in which the robot was present but inactive. Compared to our past related work, the results expanded our findings by confirming with greater clout (based on multiple data streams, including one more robust measure compared to the past related work) that children tended to be more physically active when the robot was active, and interestingly, there were no significant differences between the teleoperated and semi-autonomous modes in terms of our study measures. These insights can inform future applications of assistive robots in child motor interventions, including the guiding of appropriate levels of autonomy for these systems. This study demonstrates that incorporating robotic systems into play environments can boost physical activity in young children, indicating potential implementation in settings crafted to enhance children’s physical movement.

Recognition of human intended motion is key to developing intelligent human-robot interaction (HRI) controllers in assistive devices. This study aims to develop a human motion recognition architecture tailored explicitly for real-time assistive robotics, such as exoskeletons and robot-assisted walking systems. We introduced a multi-modal lower limb modified transformer (LLMT), an architecture that bridges the gap in existing HRI technologies by defining a comprehensive set of relevant motions that generalize well for unseen subjects, ensuring adaptability and precision in diverse interaction scenarios. LLMT uses sparse multi-channel surface electromyography (sEMG) and Inertial Measurement Unit (IMU) signals to classify different motion patterns. The accuracy of the proposed method was compared with that of the classical machine learning (cML) models and a convolutional neural network (CNN). This comparison uses experimental data from seven human participants in two motion scenarios and a benchmark dataset. The validation methods included inter-subject, leave-one-subject-out, and intra-subject approaches. The proposed method demonstrated excellent accuracy, achieving $99.42 \pm 0.25\%$ , $99.07 \pm 0.32\%$ , and $97.08 \pm 1.16\%$ in inter-subject, leave-one-subject-out, and intra-subject validation methods on the collected and benchmark datasets, respectively. Additionally, it exhibited an average online prediction time of 84.09 ms within the recording loop.

With the progression of an aging society, the importance of walking assistance technology has been increasing. The research and development of robotic walkers for individuals requiring walking support are advancing. However, there was a problem that the conventional constant support amount did not satisfy the propulsion force required for the walking speed that users wanted. In this study, in order to solve this problem, we propose an algorithm for determining the support amount to maintain the walking speed when the average walking speed of each user is set as the target speed. A robotic walker was developed by attaching BLDC motors to an actual walker, along with a control algorithm for assistance based on sampling-type PID control. The effectiveness of the assistance determination algorithm and the usefulness of the parameters were demonstrated through experiments using weights loaded on the forearm support and target speeds. Subsequently, subject experiments were conducted to verify the ability to maintain target speeds, and a questionnaire survey confirmed that the assistance did not interfere with actual walking. The proposed algorithm for determining the assistance levels demonstrated the ability to maintain target speeds and allowed for adjustments in the necessary level of assistance.

Immersive media, particularly Extended Reality (XR), is at the forefront of revolutionizing the healthcare industry. Healthcare provides XR with "silver bullet" use cases that add value and societal effect to the technology. Healthcare interventions frequently require imaging or visualization to be applied correctly, and the sensation of presence that XR can provide is crucial as a training aid for healthcare learners. From anatomy to surgical training, multimodal immersion in the reality of a medical situation increases the impact of an XR resource compared to the usual approach. Thus, healthcare has become a specialized focus for the immersive media sector, with a multitude of development and research underway. This research subject, which followed on from the previous one, yielded an eclectic group of works spanning the gamut of immersive media applications in healthcare. The underlying theme in these works remains a consistent focus on calibrating, validating, verifying, and standardizing procedures, instruments, and technologies in order to constantly rigorously streamline the means and materials that will integrate immersive technologies in healthcare. In that spirit, we share the findings from this research topic as a motivator for rigorous and evidence-based use of immersive media in digital and connected health. [ABSTRACT FROM AUTHOR]

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

During robot-assisted therapy, a robot typically needs to be partially or fully controlled by therapists, for instance using a Wizard-of-Oz protocol; this makes therapeutic sessions tedious to conduct, as therapists cannot fully focus on the interaction with the person under therapy. In this work, we develop a learning-based behaviour model that can be used to increase the autonomy of a robot’s decision-making process. We investigate reinforcement learning as a model training technique and compare different reward functions that consider a user’s engagement and activity performance. We also analyse various strategies that aim to make the learning process more tractable, namely i) behaviour model training with a learned user model, ii) policy transfer between user groups, and iii) policy learning from expert feedback. We demonstrate that policy transfer can significantly speed up the policy learning process, although the reward function has an important effect on the actions that a robot can choose. Although the main focus of this paper is the personalisation pipeline itself, we further evaluate the learned behaviour models in a small-scale real-world feasibility study in which six users participated in a sequence learning game with an assistive robot. The results of this study seem to suggest that learning from guidance may result in the most adequate policies in terms of increasing the engagement and game performance of users, but a large-scale user study is needed to verify the validity of that observation.

Selecting the right actuator for a portable exoskeleton involves a comprehensive evaluation of various design characteristics. In this study, we introduce a methodology for actuator selection based on specific tasks, enhancing the practical adoption of portable exoskeletons. By examining a range of candidate actuators designed for lower limb exoskeletons, our objective is to engineer a system that is both lightweight and power-efficient. These candidate actuators, developed by integrating diverse motors and transmission systems, were rigorously tested against defined tasks. Our methodology, applied to an assistive exoskeleton catered to the elderly, showed its potential in tailoring an efficient system with matching capabilities. The obtained results indicated that the ideal configuration achieved reductions in weight and power requirements by 35% and 80%, respectively. The present research delineates a strategic approach for actuator selection in portable exoskeletons, contributing to the evolution of high-performing assistive devices.

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview.Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years.Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted.Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking.Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

One of the major problems of today's society is the rapid aging of its population. Life expectancy is increasing, but the quality of life is not. Faced with the growing number of people who require cognitive or physical assistance, new technological tools are emerging to help them. In this article, we present the ADAM robot, a new robot designed for domestic physical assistance. It mainly consists of a mobile base, two arms with grippers and vision systems. All this allows the performance of physical tasks that require navigation and manipulation of the environment. Among ADAM's features are its modularity, its adaptability to indoor environments and its versatility to function as an experimental platform and for service applications. In addition, it is designed to work respecting the user's personal space and is collaborative, so it can learn from experiences taught by them. We present the design of the robot as well as examples of use in domestic environments both alone and in collaboration with other domestic platforms, demonstrating its potential.

Assistive robots and technologies can play a key role in supporting the independence and social participation of older people, helping them living healthy lives and reducing the burden on caregivers. To support the effective development of assistive robots and technologies, it is important to develop a "living laboratory" to verify and adapt technology in real-life living spaces. The purpose of this study is to validate assistive robots using a living laboratory that simulates typical indoor and outdoor real-life situations. The rationale is to enable evaluation of daily living activities of older people in a simulated living space. To minimize the risk of trauma after falls, a ceiling suspension system was installed in the living laboratory. Six different commercially available mobility and transfer support robots were introduced and tested. We demonstrated that effective scenarios could be implemented using these assistive robots within the living laboratory. We implemented a 3D markerless motion capturing system in the outdoor space and showed that outdoor activities, including walking up and down a ramp, could be verified with sufficient accuracy in three cases: (i) normal use without a robot, (ii) use of the ceiling suspension system, and (iii) use of a mobility support robot on three healthy subjects. These results suggest that the proposed living laboratory can support testing and verification of assistive robots in simulated living environments. [ABSTRACT FROM AUTHOR]

Access to regular sports competitions is often precluded for disabled people. Chess, which has been recognized as a sport by the International Olympic Committee in 1999, is a rare exception. Nevertheless, to compete in official tournaments, people suffering from a high level of motor impairment must rely on the assistance of a person to move their pieces on the chessboard, under their indications. This can result in a reduction of the feeling of independence and self-esteem. In this work, a service robot is employed as an assistant for competitive chess players, moving pieces on a standard chessboard for competitions, and adhering to the rules of the international chess federation (e.g. not relying on a custom sensorized chess-set). The robot is controlled through an intuitive graphical user interface. The user interface can be navigated with easy-to-use devices, such as a mouse, a touchpad, or a commodity joystick for motion-impaired people (Ottobock calibratable Mini joystick). An effective framework for the opponent's move identification from RGB-D images is proposed and used to keep track of the live game situation. The application is implemented in ROS on a PAL Robotics TIAGo robot, a service robot with a 7 degrees-of-freedom arm, an extensible torso, and a re-orientable RGB-D camera. The robustness of the application is tested by reproducing six famous chess games several times on a standard wooden competition chessboard, making TIAGo play on behalf of the player with white or black pieces, alternatively. The application is properly working without the need of operator intervention in the 91.6 % of the performed moves. The proposed approach successfully opens the door to independent competitive chess playing for motor disabled people in official tournaments. [ABSTRACT FROM AUTHOR]

Socially assistive robots are increasingly being considered to help address the shortage of care workers in long-term care, which has been further exacerbated by the COVID-19 pandemic. In this paper, we present the first human-robot interaction study with care staff and an autonomous screening socially assistive robot in a long-term care facility. We assessed: (1) overall perceptions, experiences and attitudes of care staff prior to and after interacting with the robot, and (2) perceived workload and usability of the robot by administrators and management staff. Results show staff had overall high ratings of the robot, with a statistically significant increase identified for cognitive attitude towards the robot after interaction. Furthermore, we found that overall, perceived workload was moderately low as defined by the NASA Task Load Index while using the robot screener, and the usability rating of the robot was rated between OK and Good by the System Usability Scale. Personalization of the robot was found to be an important factor for usability. Staff enjoyed using the robot and had high willingness to frequently use it. In general, our robot study motivates the application of autonomous socially assistive robots from the staff perspective for repetitive tasks in long-term care homes. [ABSTRACT FROM AUTHOR]