Socially Assistive Robots Research Articles

Therapeutic educational robot enhancing social interactions in the management of obesity.

Obesity is a chronic multifactorial pathology determined by many factors, including incorrect eating habits and a low level of physical activity. There is an urgent need to promote a persistent change in lifestyle in obese subjects, but very few individuals maintain long-term results achieved after diet therapies. Therapeutic Education (TE) has taken over an important role as a multidisciplinary intervention aimed at improving lifestyle and at acquiring new skills for the management of the disease. However, only a small portion of patients can maintain participation in such programs and fully benefit from them. Assistive technologies, and in particular assistive social robots, are powerful tools to boost independence and improve participation in educational activities. The aim of the research work described in this article is to evaluate the effect of employing a social robot as a therapeutic educational robot helping the expert therapist in the education activity. This article describes the implementation, deployment, and evaluation of a social educational robot used as a TE assistant. Although we cannot provide statistically significant results due to the limited number of people involved in the experimental protocol, all experimental results show a positive trend, indicating that the robot can enhance the social interactions between the patients and the therapist and among the patients, thus bringing to better overall results of the TE sessions, measured with standard tests for obesity management.

Socially-Assistive Robots to Support Learning in Students on the Autism Spectrum: Investigating Educator Perspectives and a Pilot Trial of a Mobile Platform to Remove Barriers to Implementation.

Technology offers educators tools that can tailor learning to students’ learning styles and interests. Research into the use of socially-assistive robots as a learning support for children on the autism spectrum are showing promising results. However, to date, few schools have introduced these robots to support learning in students on the autism spectrum. This paper reports on a research project that investigated the barriers to implementing socially-assistive robot supported learning, and the expectations, perceived benefits and concerns of school teachers and therapists of students on the autism spectrum and adults on the autism spectrum. First, three focus groups were conducted with six adults on the autism spectrum, and 13 teachers and therapists of students from two autism-specific schools. During the focus groups, there was cautious optimism from participants about the value of socially-assistive robots for teaching support. While the data showed that participants were in favour of trialling socially-assistive robots in the classroom, they also raised several concerns and potential barriers to implementation, including the need for teacher training. In response to their concerns, the second part of the project focussed on developing a software platform and mobile application (app) to support the introduction of robots into autism-specific classrooms. The software platform and app were then trialled in two schools (n = 7 teachers and therapists). Results from focus groups indicated that participants believe socially-assistive robots could be useful for learning support, as the mobile app provides an easy to use tool to support preparing and conducting lessons that would motivate them to trial robots in the classroom.

Development of an Interactive Total Body Robot Enhanced Imitation Therapy for ASD children.

Autism is a neurodevelopmental disorder in which the available therapies target the improvement of social skills, in order to ensure a high quality of life for the child. The use of Social Assistive Robots offers new therapeutic possibilities in which robots can act as therapy enhancers. IOGIOCO project emerges in this framework: it aims at the development of a Robot- Assisted Therapy protocol for the treatment of Autism Spectrum Disorder, through gesture training. The definition of these gestures and their recognition by the robot are parameters that directly affect the engagement of the children. However, the design of a protocol becomes harder in a highly unconstrained environment. Therefore, the current work aims at expanding the gesture set and improving the gesture recognition algorithm available in the IOGIOCO platform. More specifically, total body gestures have been added to the available upper limbs movements, and a custom Activity Detection method has been developed, which allows the identification of the time window in which a gesture is performed. The insertion of this method on a recognition algorithm based on a ResNet, a particular kind of Convolutional Neural Network, improved its F1-score from 57% obtained with the previously-available version, in a dataset of ASD children, to 76%, demonstrating the effectiveness of the Activity Detection method. Furthermore, the expansion of the interaction possibilities to total body movements was positively evaluated by the clinical staff, increasing the engagement of patients and the set of possible trained skills. Therefore, the results of the current work are encouraging. To reinforce the conclusions drawn, the proposed algorithm should be tested in real time on several autistic children within a complete Randomized Clinical Trial, also to study the effectiveness of this type of treatment. From the technical point of view, further improvements of the developed methodology should tackle the remained issues, such as further increasing the recognition capability, especially in the transitions from sitting to standing, that proved to be a hard task for the developed method.

Technology Acceptance in Socially Assistive Robots: Scoping Review of Models, Measurement, and Influencing Factors.

Objectives We summarized technology acceptance and the influencing factors of elderly people toward socially assistive robots (SARs). Methods A scoping review whereby a literature search was conducted in Embase, Cochrane, Scopus, PubMed, and Web of Science databases (2006–2021) to retrieve studies. No restrictions on study methodology were imposed. Results Out of the 1187 retrieved papers, 35 studies were finally included in the study. The articles covered various aspects, including general attitudes towards using SARs, technology acceptance theory models, and factors associated with technology acceptance. Twelve studies reported a positive attitude towards SARs. Three explicit theoretical frameworks were reported. Studies involving the elderly reported three themes that influence attitudes towards SARs: individual characteristics, concerns/problems regarding robots, and social factors. Conclusions This review elucidates on the suitability of theory-based framework as applied to acceptance of SARs. We found that research on technology acceptance with regard to SARs is still in the developmental stages, and further studies of assessment tools for SARs are required. It is also essential to consider the factors that influence the acceptance of SARs by older people to ensure that they meet the end goal requirements of the user.

Socially assistive robots on the market : Experiences from inpatient care and potentials for care at home.

The article addresses commercial socially assistive robots (SAR). There is evidence of the impact of SAR on older persons in institutional settings. Family care at home has not yet been the subject of systematic research; however, especially at home SAR could help to prevent loneliness, provide emotional support, and foster communication. Inspired by evidence on the potential impact of SAR, the article analyzes their potential for supporting family care at home. Aparticipatory research and development (R&D) project stands as an example of how SAR can improve communication and relations at home. Apart from the results of selected randomized controlled trials (RCT) in institutional settings, the article provides results from tests that were run in the R&D project. SAR can increase the quality of life of users at home. They are capable of improving relations within family care. They inspire communication, change routines, and provide relief to family caregivers; however, it takes trained human companions to support the use of SAR at home and address ethical aspects. If SAR are supposed to deliver their potential for care at home, older persons and their caregivers must be actively involved in research and development. These processes must be systematically evaluated.

Design and development of a social assistive robot for music and game activities: a case study in a residential facility for disabled people.

Cognitive disability strongly reduces people's autonomy in performing desired as well as daily activities. The use of Social Assistive Robots (SARs) for cognitive rehabilitation therapy for disabled people could be a valuable gateway for the residential facility of the future. In this work, we design and develop a SAR that can be used for cognitive therapy proposing music and game activities. The results confirm that participants were positively engaged during the proposed activities and satisfied by the robot, despite the low perception of its usability. Professional caregivers noticed and confirmed the high level of engagement and the positive acceptance of the robot within the session, suggesting future tasks for SAR. Clinical Relevance- The results suggest the potential use of SAR also with disabled people proposing cognitive games as a part of the cognitive rehabilitation program.

User Profiling to Enhance Clinical Assessment and Human–Robot Interaction: A Feasibility Study

Socially Assistive Robots (SARs) are designed to support us in our daily life as a companion, and assistance but also to support the caregivers’ work. SARs should show personalized and human-like behavior to improve their acceptance and, consequently, their use. Additionally, they should be trustworthy by caregivers and professionals to be used as support for their work (e.g. objective assessment, decision support tools). In this context the aim of the paper is dual. Firstly, this paper aims to present and discuss the robot behavioral model based on sensing, perception, decision support, and interaction modules. The novel idea behind the proposed model is to extract and use the same multimodal features set for two purposes: (i) to profile the user, so to be used by the caregiver as a decision support tool for the assessment and monitoring of the patient; (ii) to fine-tune the human–robot interaction if they can be correlated to the social cues. Secondly, this paper aims to test in a real environment the proposed model using a SAR robot, namely ASTRO. Particularly, it measures the body posture, the gait cycle, and the handgrip strength during the walking support task. Those collected data were analyzed to assess the clinical profile and to fine-tune the physical interaction. Ten older people (65.2 ± 15.6 years) were enrolled for this study and were asked to walk with ASTRO at their normal speed for 10 m. The obtained results underline a good estimation (p < 0.05) of gait parameters, handgrip strength, and angular excursion of the torso with respect to most used instruments. Additionally, the sensory outputs were combined in the perceptual model to profile the user using non-classical and unsupervised techniques for dimensionality reduction namely T-distributed Stochastic Neighbor Embedding (t-SNE) and non-classic multidimensional scaling (nMDS). Indeed, these methods can group the participants according to their residual walking abilities.

Design and Development of a Scale for Evaluating the Acceptance of Social Robotics for Older People: The Robot Era Inventory.

IntroductionNowadays, several robots have been developed to provide not only companionship to older adults, but also to cooperate with them during health and lifestyle activities. Despite the undeniable wealth of socially assistive robots (SARs), there is an increasing need to customize the tools used for measuring their acceptance in real-life applications.MethodsWithin the Robot-Era project, a scale was developed to understand the degree of acceptance of the robotic platform. A preliminary test with 21 participants was performed to assess the statistical validity of the Robot-Era Inventory (REI) scales.ResultsBased on the criteria observed in the literature, 41 items were developed and grouped in different scales (perceived robot personality, human–robot interaction, perceived benefit, ease of use, and perceived usefulness). The reliability of the Robot-Era Inventory scale was analyzed with Cronbach's alpha, with a mean value of 0.79 (range = 0.61–0.91). Furthermore, the preliminary validity of this scale has been tested by using the correlation analysis with a gold standard, the Unified Theory of Acceptance and Use of Technology (UTAUT) model.DiscussionThe Robot-Era Inventory represents a useful tool that can be easily personalized and included in the assessment of any SARs that cooperate with older people in real environment applications.

The Effect of Cognitive Function Health Care Using Artificial Intelligence Robots for Older Adults: Systematic Review and Meta-analysis.

BackgroundWith rapidly aging populations in most parts of the world, it is only natural that the need for caregivers for older adults is going to increase in the near future. Therefore, most technologically proficient countries are in the process of using artificial intelligence (AI) to build socially assistive robots (SAR) to play the role of caregivers in enhancing interaction and social participation among older adults.ObjectiveThis study aimed to examine the effect of intervention through AI SAR on the cognitive function of older adults through a systematic literature review.MethodsWe conducted a meta-analysis of the various existing studies on the effect of AI SAR on the cognitive function of older adults to standardize the results and clarify the effect of each method and indicator. Cochrane collaboration and the systematic literature review flow of PRISMA (Preferred Reporting Item Systematic Reviews and Meta-Analyses) were used on original, peer-reviewed studies published from January 2010 to March 2022. The search words were derived by combining keywords including Population, Intervention, and Outcome—according to the Population, Intervention, Comparison, Outcome, Time, Setting, and Study Design principle—for the question “What is the effect of AI SAR on the cognitive function of older adults in comparison with a control group?” (Population: adults aged ≥65 years; Intervention: AI SAR; Comparison: comparison group; Outcome: popular function; and Study Design: prospective study). For any study, if one condition among subjects, intervention, comparison, or study design was different from those indicated, the study was excluded from the literature review.ResultsIn total, 9 studies were selected (6 randomized controlled trials and 3 quasi-experimental design studies) for the meta-analysis. Publication bias was examined using the contour-enhanced funnel plot method to confirm the reliability and validity of the 9 studies. The meta-analysis revealed that the average effect size of AI SAR was shown to be Hedges g=0.43 (95% CI –0.04 to 0.90), indicating that AI SAR are effective in reducing the Mini Mental State Examination scale, which reflects cognitive function.ConclusionsThe 9 studies that were analyzed used SAR in the form of animals, robots, and humans. Among them, AI SAR in anthropomorphic form were able to improve cognitive function more effectively. The development and expansion of AI SAR programs to various functions including health notification, play therapy, counseling service, conversation, and dementia prevention programs are expected to improve the quality of care for older adults and prevent the overload of caregivers. AI SAR can be considered a representative, digital, and social prescription program and a nonpharmacological intervention program that communicates with older adults 24 hours a day. Despite its effectiveness, ethical issues, the digital literacy needs of older adults, social awareness and reliability, and technological advancement pose challenges in implementing AI SAR. Future research should include bigger sample sizes, pre-post studies, as well as studies using an older adult control group.

Evolving Fuzzy logic Systems for creative personalized Socially Assistive Robots

Socially Assistive Robots (SARs) are increasingly used in dementia and elderly care. In order to provide effective assistance, SARs need to be personalized to individual patients and account for stimulating their divergent thinking in creative ways. Rule-based fuzzy logic systems provide effective methods for automated decision-making of SARs. However, expanding and modifying the rules of fuzzy logic systems to account for the evolving needs, preferences, and medical conditions of patients can be tedious and costly. In this paper, we introduce EFS4SAR, a novel Evolving Fuzzy logic System for Socially Assistive Robots that supports autonomous evolution of the fuzzy rules that steer the behavior of the SAR. EFS4SAR combines traditional rule-based fuzzy logic systems with evolutionary algorithms, which model the process of evolution in nature and have shown to result in creative behaviors. We evaluate EFS4SAR via computer simulations on both synthetic and real-world data. The results show that the fuzzy rules evolved over time are not only personalized with respect to the personal preferences and therapeutic needs of the patients, but they also meet the following criteria for creativity of SARs: originality and effectiveness of the therapeutic tasks proposed to the patients. Compared to existing evolving fuzzy systems, EFS4SAR achieves similar effectiveness with higher degree of originality.

Personalized home-care support for the elderly: a field experience with a social robot at home

Socially assistive robotics (SAR) is getting a lot of attention for its potential in assisting elderly users. However, for robotic assistive applications to be effective, they need to satisfy the particular needs of each user and be well perceived. For this purpose, a personalization based on user’s characteristics such as personality and cognitive profile, and their dynamic changes is a crucial factor. Moreover, most of the existing solutions rely on the availability of specific technological infrastructures, generally requiring high economic investment, and that cannot be easily placed in different environments. Personalization and adaptation of assistive robotics applications to different user’s characteristics and needs, and even to different technological environments, are still not fully addressed in real environments. In the present work, the results of the UPA4SAR project are presented. The project aimed at providing a social robotic system to deliver assistive tasks for home care of patients with mild cognitive impairment in a personalized and adaptive way. We introduce the general architecture of the system and the developed robotic behaviors. Personalization and dynamic adaptation of assistive tasks are realized using a service-oriented approach by taking into account both user’s characteristics and environmental dynamic conditions. Field experimentation of the project was carried out with 7 patients, using the robotic system autonomously running in their homes for a total of 118 days. Results showed a reliable functioning of the proposed robotic system, a generally positive reaction, and a good acceptability rate from patients.

The adoption of socially assistive robots for long-term care: During COVID-19 and in a post-pandemic society.

The rapid spread of COVID-19 has prompted a surge in the adoption of technology, highlighting a number of potential applications for Socially Assistive Robots (SARs). Our entire healthcare system has been under unprecedented strain, and going forward, we must consider how robotic technology could help improve the quality of care and day-to-day functionality of our care facilities. Herein, we present our human-robot interaction study in a local long-term care centre during the pandemic and the lessons learned from deploying a SAR to screen staff members. We investigate staff acceptance and the influence of demographics on perceptions of the SAR. Results show that overall, staff were positive about the screening robot, and that autonomous screening with a social robot is a potential application in long-term care homes. We further detail the challenges and future opportunities to develop SARs, including recommendations to successfully implement and adopt these robots in a post-pandemic society.

User feedback and remote supervision for assisted living with mobile robots: A field study in long-term autonomy

In an ageing society, the at-home use of Socially Assistive Robots (SARs) could provide remote monitoring of their users’ well-being, together with physical and psychological support. However, private home environments are particularly challenging for SARs, due to their unstructured and dynamic nature which often contributes to robots’ failures. For this reason, even though several prototypes of SARs for elderly care have been developed, their commercialisation and wide-spread at-home use are yet to be effective. In this paper, we analyse how including the end users’ feedback impacts the SARs reliability and acceptance. To do so, we introduce a Monitoring and Logging System (MLS) for remote supervision, which increases the explainability of SAR-based systems deployed in older adults’ apartments, while also allowing the exchange of feedback between caregivers, technicians, and older adults. We then present an extensive field study showing how long-term deployment of autonomous SARs can be accomplished by relying on such a feedback loop to address any potential issue. To this end, we provide the results obtained in a 130-week long study where autonomous SARs were deployed in the apartments of 10 older adults, with the aim of possibly serving and assisting future practitioners, with the knowledge collected from this extensive experimental campaign, to fill the gap that currently exists for the widespread adoption of SARs.

Ethical Considerations from Child-Robot Interactions in Under-Resourced Communities.

Recent advancements in socially assistive robotics (SAR) have shown a significant potential of using social robotics to achieve increasing cognitive and affective outcomes in education. However, the deployments of SAR technologies also bring ethical challenges in tandem, to the fore, especially in under-resourced contexts. While previous research has highlighted various ethical challenges that arise in SAR deployment in real-world settings, most of the research has been centered in resource-rich contexts, mainly in developed countries in the ‘Global North,’ and the work specifically in the educational setting is limited. This research aims to evaluate and reflect upon the potential ethical and pedagogical challenges of deploying a social robot in an under-resourced context. We base our findings on a 5-week in-the-wild user study conducted with 12 kindergarten students at an under-resourced community school in New Delhi, India. We used interaction analysis with the context of learning, education, and ethics to analyze the user study through video recordings. Our findings highlighted four primary ethical considerations that should be taken into account while deploying social robotics technologies in educational settings; (1) language and accent as barriers in pedagogy, (2) effect of malfunctioning, (un)intended harms, (3) trust and deception, and (4) ecological viability of innovation. Overall, our paper argues for assessing the ethical and pedagogical constraints and bridging the gap between non-existent literature from such a context to evaluate better the potential use of such technologies in under-resourced contexts.

The perspectives of older adults with mild cognitive impairment and their caregivers on the use of socially assistive robots in healthcare: exploring factors that influence attitude in a pre-implementation stage

Background Due to increasing age and an increasing prevalence rate of neurocognitive disorders such as Mild Cognitive Impairment (MCI) and dementia, independent living may become challenging. The use of socially assistive robots (SARs) is one solution that can enable older adults with cognitive impairment to remain independent. However, at present, there is a lack of knowledge about the attitudes of older adults with MCI and their caregivers towards SARs. Methods This study relies on a constructivist grounded theory approach. Semi-structured interviews were conducted to gain a deeper insight into attitudes of two different stakeholder groups; older adults with MCI and their (in)formal caregivers. Results Forty individual semi-structured interviews were conducted with older adults with MCI (N = 30) and (in)formal caregivers (N = 10). Data revealed different perspectives on SARs in healthcare for the involved stakeholders. Two main topics could be derived: (1) perspectives on robot assistance, discussing different viewpoints on the potential value of robots as helpers, and (2) perspectives on implementation, revealing different factors that could affect implementation. Both topics may explain a positive, impartial or negative attitude towards SARs. Conclusions This study identified different factors that should be taken into account when implementing a SAR in the home environment of older adults. Despite the fact that the benefits of SARs are often recognized, many older participants currently seem not ready yet to commit to the use of a SAR. IMPLICATIONS FOR REHABILITATION This study explores the attitudes towards a SAR, developed to stimulate and support older adults with Mild Cognitive Impairment (MCI) on a physical, cognitive and social level. The results give a deeper insight into different factors contributing to a (non-) successful implementation of SARs in the home environment of older adults with MCI.

A longitudinal study on the effects of COVID-19 pandemic on non-motor symptoms in Parkinson’s disease

IntroductionThe COVID-19 pandemic led to psychological consequences on people’s mental health, representing a condition of increased vulnerability for the weakest sections of population, including elderly patients with Parkinson’s disease (PD).This longitudinal study aimed at exploring the impact of the most frequent non-motor symptoms and their contribute on health-related quality of life of PD patients after the COVID-19 outbreak, in comparison with the pre-pandemic status.MethodsForty-two non-demented PD patients underwent a first assessment between December 2018 and January 2020 (T0). Then, between March and May 2021 (T1), they were contacted again and asked to complete the second assessment. Levels of global functioning, several non-motor symptoms (i.e. depression, apathy, anxiety, anhedonia) and health-related quality of life were investigated.ResultsResults of the the paired Wilcoxon signed-rank test showed that at T1, PD patients scored lower on the emotional subscale of the DAS, Z = − 2.49; p = 0.013; Cohen dz = 0.691. Higher scores of the TEPS total score, Z = − 2.38; p = 0.025; Cohen dz = 0.621, and LEDD, Z = − 2.63; p = 0.008; Cohen dz = 0.731, were also reported at T1.ConclusionThe present study suggested that self-isolation at home might lead to a reduction of apathy and anhedonia in PD patients due to the increase in social support provided by families during COVID-19 restrictions. This evidence brings out the need of a consistent and persistent social support which might be represented by caregivers or/and social assistive robotics.

Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project

The integration of Ambient Assisted Living (AAL) frameworks with Socially Assistive Robots (SARs) has proven useful for monitoring and assisting older adults in their own home. However, the difficulties associated with long-term deployments in real-world complex environments are still highly under-explored. In this work, we first present the MoveCare system, an unobtrusive platform that, through the integration of a SAR into an AAL framework, aimed to monitor, assist and provide social, cognitive, and physical stimulation in the own houses of elders living alone and at risk of falling into frailty. We then focus on the evaluation and analysis of a long-term pilot campaign of more than 300 weeks of usages. We evaluated the system’s acceptability and feasibility through various questionnaires and empirically assessed the impact of the presence of an assistive robot by deploying the system with and without it. Our results provide strong empirical evidence that Socially Assistive Robots integrated with monitoring and stimulation platforms can be successfully used for long-term support to older adults. We describe how the robot’s presence significantly incentivised the use of the system, but slightly lowered the system’s overall acceptability. Finally, we emphasise that real-world long-term deployment of SARs introduces a significant technical, organisational, and logistical overhead that should not be neglected nor underestimated in the pursuit of long-term robust systems. We hope that the findings and lessons learned from our work can bring value towards future long-term real-world and widespread use of SARs.

HR1 Robot: An Assistant for Healthcare Applications.

According to the World Health Organization 1, 2 the percentage of healthcare dependent population, such as elderly and people with disabilities, among others, will increase over the next years. This trend will put a strain on the health and social systems of most countries. The adoption of robots could assist these health systems in responding to this increased demand, particularly in high intensity and repetitive tasks. In a previous work, we compared a Socially Assistive Robot (SAR) with a Virtual Agent (VA) during the execution of a rehabilitation task. The SAR consisted of a humanoid R1 robot, while the Virtual Agent represented its simulated counter-part. In both cases, the agents evaluated the participants’ motions and provided verbal feedback. Participants reported higher levels of engagement when training with the SAR. Given that the architecture has been proven to be successful for a rehabilitation task, other sets of repetitive tasks could also take advantage of the platform, such as clinical tests. A commonly performed clinical trial is the Timed Up and Go (TUG), where the patient has to stand up, walk 3 m to a goal line and back, and sit down. To handle this test, we extended the architecture to evaluate lower limbs’ motions, follow the participants while continuously interacting with them, and verify that the test is completed successfully. We implemented the scenario in Gazebo, by simulating both participants and the interaction with the robot 3 . A full interactive report is created when the test is over, providing the extracted information to the specialist. We validate the architecture in three different experiments, each with 1,000 trials, using the Gazebo simulation. These experiments evaluate the ability of this architecture to analyse the patient, verify if they are able to complete the TUG test, and the accuracy of the measurements obtained during the test. This work provides the foundations towards more thorough clinical experiments with a large number of participants with a physical platform in the future. The software is publicly available in the assistive-rehab repository 4 and fully documented.

A Socially Assistive Robot for Stroke Patients: Acceptance, Needs, and Concerns of Patients and Informal Caregivers.

Stroke patients often contend with long-term physical challenges that require treatment and support from both formal and informal caregivers. Socially Assistive Robots (SARs) can assist patients in their physical rehabilitation process and relieve some of the burden on the informal caregivers, such as spouses and family members. We collected and analyzed information from 23 participants (11 stroke patients and 12 informal caregivers) who participated in a total of six focus-group discussions. The participants responded to questions regarding using a SAR to promote physical exercises during the rehabilitation process: (a) the advantages and disadvantages of doing so; (b) specific needs that they wish a SAR would address; (c) patient-specific adaptations they would propose to include; and (d) concerns they had regarding the use of such technology in stroke rehabilitation. We found that the majority of the participants in both groups were interested in experiencing the use of a SAR for rehabilitation, in the clinic and at home. Both groups noted the advantage of having the constant presence of a motivating entity with whom they can practice their rehabilitative exercises. The patients noted how such a device can assist formal caregivers in managing their workload, while the informal caregivers indicated that such a system could ease their own workload and sense of burden. The main disadvantages that participants noted related to the robot not possessing human abilities, such as the ability to hold a conversation, to physically guide the patient's movements, and to express or understand emotions. We anticipate that the data collected in this study—input from the patients and their family members, including the similarities and differences between their points of view—will aid in improving the development of SARs for rehabilitation, so that they can better suit people who have had a stroke, and meet their individual needs.

Young and old users prefer immersive virtual reality over a social robot for short-term cognitive training

Socially Assistive Robots (SARs) and immersive Virtual Reality (iVR) are interactive platforms that promote user engagement, which can motivate users to adhere to therapeutic frameworks. SARs use social presence to create affective relationships with users, leveraging the human tendency to be driven by social interactions. iVR uses spatial presence to provide an intense multisensory experience that submerges users in a virtual world. We adapted two such platforms – a SAR and an iVR – to deliver cognitive training (CT), by integrating established cognitive tasks in gamified environments that convey a strong sense of presence. Sixty-four participants underwent CT with both platforms. We tested: (1) their perception of both platforms; (2) whether they preferred one over the other in the short term; (3) their projected preferences for long-term training; and (4) whether their preferences correlated with personal characteristics. They preferred the virtual experience in the short term across age and gender. For long-term CT, there was equal projected preference for both platforms. It may be that a combination of social and spatial presence might yield engagement in long-term training.