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As the population of older adults increases, there is an anticipated rise in the utilization of hospice and palliative care. Many significant advancements in technology have been used to address the unique needs of this demographic; however, an unexplored area of research is the use of robotic animals as part of end-of-life care. The purpose of this scoping review was to examine the state of the literature on robotic animal use among older adults enrolled in palliative or hospice care and to offer a framework for future research. Following a guide for scoping reviews, we identified relevant studies and then charted, collated, summarized, and reported the data. Two articles were selected for final review. The results found that decreased medication use, behavior change, and emotional benefits were potential outcomes of robotic animal use in hospice and palliative care. Perceptions of the robot and ethical considerations were also discussed. Overall, the study findings point toward the potential uses of robotic animals as part of end-of-life care, however, more empirical research is critically needed. [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]

As the population of older adults increases, there is an anticipated rise in the utilization of hospice and palliative care. Many significant advancements in technology have been used to address the unique needs of this demographic; however, an unexplored area of research is the use of robotic animals as part of end-of-life care. The purpose of this scoping review was to examine the state of the literature on robotic animal use among older adults enrolled in palliative or hospice care and to offer a framework for future research. Following a guide for scoping reviews, we identified relevant studies and then charted, collated, summarized, and reported the data. Two articles were selected for final review. The results found that decreased medication use, behavior change, and emotional benefits were potential outcomes of robotic animal use in hospice and palliative care. Perceptions of the robot and ethical considerations were also discussed. Overall, the study findings point toward the potential uses of robotic animals as part of end-of-life care, however, more empirical research is critically needed.

For older adults, regular exercises can provide both physical and mental benefits, increase their independence, and reduce the risks of diseases associated with aging. However, only a small portion of older adults regularly engage in physical activity. Therefore, it is important to promote exercise among older adults to help maintain overall health. In this paper, we present the first exploratory long-term human–robot interaction (HRI) study conducted at a local long-term care facility to investigate the benefits of one-on-one and group exercise interactions with an autonomous socially assistive robot and older adults. To provide targeted facilitation, our robot utilizes a unique emotion model that can adapt its assistive behaviors to users' affect and track their progress towards exercise goals through repeated sessions using the Goal Attainment Scale (GAS), while also monitoring heart rate to prevent overexertion. Results of the study show that users had positive valence and high engagement towards the robot and were able to maintain their exercise performance throughout the study. Questionnaire results showed high robot acceptance for both types of interactions. However, users in the one-on-one sessions perceived the robot as more sociable and intelligent, and had more positive perception of the robot's appearance and movements. [ABSTRACT FROM AUTHOR]

Over decades, social robotics has evolved as a concept that presently covers different areas of application, and interacts with different domains in technology, education, medicine and others. Today, it is possible to envision social robots in tasks that were not expected years ago, and that is not only due to the evolution of social robots, but also to the evolution of the vision humans have for them. This survey addresses recent advances in social robotics from different perspectives. Different contexts and areas of application of social robots are addressed, as well as modalities of interaction with humans. Different robotic platforms used in social contexts are shown and discussed. Relationships of social robotics with advances in other technological areas are surveyed, and methods and metrics used for the human evaluation of the interaction with robots are presented. The future of social robotics is also envisioned based on surveyed works and from different points of view. [ABSTRACT FROM AUTHOR]

For older adults, regular exercises can provide both physical and mental benefits, increase their independence, and reduce the risks of diseases associated with aging. However, only a small portion of older adults regularly engage in physical activity. Therefore, it is important to promote exercise among older adults to help maintain overall health. In this paper, we present the first exploratory long-term human–robot interaction (HRI) study conducted at a local long-term care facility to investigate the benefits of one-on-one and group exercise interactions with an autonomous socially assistive robot and older adults. To provide targeted facilitation, our robot utilizes a unique emotion model that can adapt its assistive behaviors to users’ affect and track their progress towards exercise goals through repeated sessions using the Goal Attainment Scale (GAS), while also monitoring heart rate to prevent overexertion. Results of the study show that users had positive valence and high engagement towards the robot and were able to maintain their exercise performance throughout the study. Questionnaire results showed high robot acceptance for both types of interactions. However, users in the one-on-one sessions perceived the robot as more sociable and intelligent, and had more positive perception of the robot’s appearance and movements.

Over decades, social robotics has evolved as a concept that presently covers different areas of application, and interacts with different domains in technology, education, medicine and others. Today, it is possible to envision social robots in tasks that were not expected years ago, and that is not only due to the evolution of social robots, but also to the evolution of the vision humans have for them. This survey addresses recent advances in social robotics from different perspectives. Different contexts and areas of application of social robots are addressed, as well as modalities of interaction with humans. Different robotic platforms used in social contexts are shown and discussed. Relationships of social robotics with advances in other technological areas are surveyed, and methods and metrics used for the human evaluation of the interaction with robots are presented. The future of social robotics is also envisioned based on surveyed works and from different points of view.

Personalization and localization are important when developing social robots for different sectors, including education, industry, healthcare or restaurants. This allows for an adjustment of robot behaviors according to the needs, preferences or personality of an individual when referring to personalization or to the social conventions or the culture of a country when referring to localization. However, there are different models that enable personalization and localization presented in the current literature, each with their advantages and drawbacks. This work aims to help researchers in the field of social robotics by reviewing and analyzing different papers in this domain. We specifically focus our review by exploring different robots that employ distinct models for the adaptation of the robot to its environment. Additionally, we study an array of methods used to adapt the nonverbal and verbal skills of social robots, including state-of-the-art techniques in artificial intelligence.

Autism Spectrum Disorder is a developmental disorder that affects children from a very young age and is characterized by persistent deficits in social, communicational, and behavioral abilities. Since there is no cure for autism, domain experts focus on aiding these children through specific intervention plans that are aimed towards the development of the deficient areas. Using socially assistive robots that interact in a social manner with children in autism interventions, efforts are being made towards alleviating the autistic behavior of children and enhancing their social behavior. However, implementing robots in autism interventions could lead to harmful situations concerning safety. In this paper, an architecture for safe child–robot interactions in autism interventions is proposed. First, a taxonomy of child–robot interactions in autism interventions is presented, explaining its complete framework. Next, the interaction is modelled according to this taxonomy where an interaction case is employed in order for the structure of the interaction to be defined. Based on that, the safety architecture is proposed that will be integrated into the robot’s controller. Focus is placed on detecting possible distracting elements that could influence the performance of the child, affecting their psychological or physical safety. Lastly, the interaction between child and robot is created in a simulated environment through dialogue inputs and outputs, and the code of the architecture is tested, where a virtual robot performs the appropriate actions.

As societies move towards integration of robots, it is important to study how robots can use their cognition in order to choose effectively their actions in a human environment, and possibly adapt to new contexts. When modelling these contextual data, it is common in social robotics to work with data extracted from human sciences such as sociology, anatomy, or anthropology. These heterogeneous data need to be efficiently used in order to make the robot adapt quickly its actions. In this paper we describe a methodology for the use of heterogeneous and incomplete knowledge, through an algorithm based on naive Bayes classifier. The model was successfully applied to two different experiments of human-robot interaction.

Personalization and localization are important when developing social robots for different sectors, including education, industry, healthcare or restaurants. This allows for an adjustment of robot behaviors according to the needs, preferences or personality of an individual when referring to personalization or to the social conventions or the culture of a country when referring to localization. However, there are different models that enable personalization and localization presented in the current literature, each with their advantages and drawbacks. This work aims to help researchers in the field of social robotics by reviewing and analyzing different papers in this domain. We specifically focus our review by exploring different robots that employ distinct models for the adaptation of the robot to its environment. Additionally, we study an array of methods used to adapt the nonverbal and verbal skills of social robots, including state-of-the-art techniques in artificial intelligence. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder is a developmental disorder that affects children from a very young age and is characterized by persistent deficits in social, communicational, and behavioral abilities. Since there is no cure for autism, domain experts focus on aiding these children through specific intervention plans that are aimed towards the development of the deficient areas. Using socially assistive robots that interact in a social manner with children in autism interventions, efforts are being made towards alleviating the autistic behavior of children and enhancing their social behavior. However, implementing robots in autism interventions could lead to harmful situations concerning safety. In this paper, an architecture for safe child–robot interactions in autism interventions is proposed. First, a taxonomy of child–robot interactions in autism interventions is presented, explaining its complete framework. Next, the interaction is modelled according to this taxonomy where an interaction case is employed in order for the structure of the interaction to be defined. Based on that, the safety architecture is proposed that will be integrated into the robot's controller. Focus is placed on detecting possible distracting elements that could influence the performance of the child, affecting their psychological or physical safety. Lastly, the interaction between child and robot is created in a simulated environment through dialogue inputs and outputs, and the code of the architecture is tested, where a virtual robot performs the appropriate actions. [ABSTRACT FROM AUTHOR]