Telepresence Mobile Robots Design and Control for Social Interaction.
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ABSTRACT:
SUBMITTER: Tuli TB
PROVIDER: S-EPMC7358301 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We present a novel functional magnetic resonance imaging paradigm for second-person neuroscience. The paradigm compares a human social interaction (human-human interaction, HHI) to an interaction with a conversational robot (human-robot interaction, HRI). The social interaction consists of 1 min blocks of live bidirectional discussion between the scanned participant and the human or robot agent. A final sample of 21 participants is included in the corpus comprising physiological (blood oxygen level-dependent, respiration and peripheral blood flow) and behavioural (recorded speech from all interlocutors, eye tracking from the scanned participant, face recording of the human and robot agents) data. Here, we present the first analysis of this corpus, contrasting neural activity between HHI and HRI. We hypothesized that independently of differences in behaviour between interactions with the human and robot agent, neural markers of mentalizing (temporoparietal junction (TPJ) and medial prefrontal cortex) and social motivation (hypothalamus and amygdala) would only be active in HHI. Results confirmed significantly increased response associated with HHI in the TPJ, hypothalamus and amygdala, but not in the medial prefrontal cortex. Future analysis of this corpus will include fine-grained characterization of verbal and non-verbal behaviours recorded during the interaction to investigate their neural correlates. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.
| S-EPMC6452252 | biostudies-literature
Project description:In previous experiments, a back-off movement was introduced as a motion strategy of robots to facilitate the order of passage at bottlenecks in human-robot spatial interaction. In this article we take a closer look at the appropriate application of motion parameters that make the backward movement legible. Related works in distance perception, size-speed illusions, and viewpoint-based legibility considerations suggest a relationship between the size of the robot and the observer's perspective on the expected execution of this movement. We performed a participant experiment (N = 50) in a virtual reality environment where participants adjusted the minimum required back-off length and preferred back-off speed as a result of the robot size, and the viewpoint of the back-off movement. We target a model-based approach on how appropriate back-off design translates to different sized robots and observer's viewpoints. Thus, we allow the application of back-off in a variety of autonomous moving systems. The results show a significant correlation between the increasingly expected back-off lengths with increasing robot size, but only weak effects of the viewpoint on the requirements of this movement. An exploratory analysis suggests that execution time might be a promising parameter to consider for the design of legible motion.
| S-EPMC7993606 | biostudies-literature
Project description:Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.
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Project description:IntroductionSocial isolation is a significant issue in aged care settings (eg, long-term care (LTC) and hospital) and is associated with adverse outcomes such as reduced well-being and loneliness. Loneliness is linked with depression, anxiety, cognitive decline, weakened immune system, poor physical health, poor quality of life and mortality. The use of robotic assistance may help mitigate social isolation and loneliness. Although telepresence robots have been used in healthcare settings, a comprehensive review of studies focusing on their use in aged care for reducing social isolation requires further investigation. This scoping review will focus on the use of telepresence robots to support social connection of older people in care settings.Methods and analysisThis scoping review will follow Joanna Briggs Institute scoping review methodology. The review team consists of patient partners and family partners, a nurse researcher and a group of students. In the scoping review, we will search the following databases: MEDLINE (Ovid), CINAHL, PsycINFO (EBSCO), Web of Science and ProQuest Dissertations & Theses Global. Google and Google Scholar will be used to search for additional literature. A handsearch will be conducted using the reference lists of included studies to identify additional relevant articles. The scoping review will consider studies of using a telepresence robotic technology with older adults in care settings (ie, LTC and hospital), published in English.Ethics and disseminationSince the methodology of the study consists of collecting data from publicly available articles, it does not require ethics approval. By examining the current state of using telepresence to support older people in care settings, this scoping review can offer useful insight into users' needs (eg, patients' and care providers' needs) and inform future research and practice. We will share the scoping review results through conference presentations and an open access publication in a peer-reviewed journal.
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Project description:Background: Adolescent cancer patients experience considerable absence from their education, contributing to poorer academic attainment and isolation from peers, and impacting wellbeing. Telepresence robots have been used to support the educational and social needs of young people with chronic illness. This article presents the results of the development and pilot-testing of a telepresence robot service in schools for adolescent cancer patients - the TRECA (Telepresence Robots to Engage CAncer patients in education) service. Methods: Phase I used semi-structured interviews (n = 25) to assess the views of patients, parents, schools and clinicians on the benefits, acceptability, barriers, and enablers of utilizing robots in schools for adolescent cancer patients. Results from Phase I informed the development of the TRECA service. Phase II used semi-structured interviews (n = 22) to assess the implementation experiences of adolescent cancer patients, and their families, schools, and keyworkers who pilot-tested the TRECA service. Results: Phase I demonstrated the need for telepresence technology in connecting adolescent cancer patients to school. Given the variable support during treatment, a telepresence robot service was considered an acceptable method of facilitating a school-patient connection. The recommendations provided in Phase I, such as the need for provision of ongoing education, training, and support to the patient and school, informed the development of the TRECA service. In Phase II, the themes of The necessity of stakeholder buy-in, A facilitator of meaningful connection, and One size does not fit all were generated. The TRECA service's flexibility in meeting the needs of its users helped facilitate meaningful connections. Participants reported that these connections provided patients an enhanced sense of agency and wellbeing. The importance of stakeholder buy-in and taking an individualized approach to service delivery were also highlighted. Stakeholder miscommunication and lack of knowledge were key aspects of implementation needing improvement as the service is rolled out on a larger scale. Conclusion: Using telepresence robots to connect adolescents to school during cancer treatment was regarded as highly acceptable, facilitating peer and academic connection. By making stakeholder-recommended improvements to the TRECA service's existing processes, the service will continue to grow in effectiveness and capacity.
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Project description:The ability to recognize the surface type is crucial for both indoor and outdoor mobile robots. Knowing the surface type can help indoor mobile robots move more safely and adjust their movement accordingly. However, recognizing surface characteristics is challenging since similar planes can appear substantially different; for instance, carpets come in various types and colors. To address this inherent uncertainty in vision-based surface classification, this study first generates a new, unique data set composed of 2,081 surface images (carpet, tiles, and wood) captured in different indoor environments. Secondly, the pre-trained state-of-the-art deep learning models, namely InceptionV3, VGG16, VGG19, ResNet50, Xception, InceptionResNetV2, and MobileNetV2, were utilized to recognize the surface type. Additionally, a lightweight MobileNetV2-modified model was proposed for surface classification. The proposed model has approximately four times fewer total parameters than the original MobileNetV2 model, reducing the size of the trained model weights from 42 MB to 11 MB. Thus, the proposed model can be used in robotic systems with limited computational capacity and embedded systems. Lastly, several optimizers, such as SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, and Nadam, are applied to distinguish the most efficient network. Experimental results demonstrate that the proposed model outperforms all other applied methods and existing approaches in the literature by achieving 99.52% accuracy and an average score of 99.66% in precision, recall, and F1-score. In addition to this, the proposed lightweight model was tested in real-time on a mobile robot in 11 scenarios consisting of various indoor environments such as offices, hallways, and homes, resulting in an accuracy of 99.25%. Finally, each model was evaluated in terms of model loading time and processing time. The proposed model requires less loading and processing time than the other models.
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Project description:COVID-19 intensified interest in telemedicine, yet no study has evaluated the use of a telepresence robot on unselected urological patients. Therefore, we performed a survey study of patients, bedside caregivers and urologists, investigating the satisfaction and applicability of a telepresence robot (Beam Pro, Suitable Technologies, USA) at the urology ward and emergency department. The primary outcome was the number of patient encounters solved without the urologist's physical presence. Between March 2021 and May 2021, patients, caregivers, and urologists filled in 42, 35, and 54 questionnaires, respectively. Most patients were male (79%), with a mean age of 64 (SD ± 17). Two of the department's ten urologists participated. The urologists responded that physical examination was required in 7 (13%) encounters. The caregivers would have preferred the urologist physically present in 11 (31%) cases. Most patients (71%) "agreed" or "strongly agreed" that they were willing to be attended by a telepresence robot at future evaluations and generally, patients gave high satisfaction scores. Though implementation among the department's urologists was a major challenge, participating urologists reported that physical presence could be avoided in 87% of the patient encounters. Studies of patient-reported outcome measures comparing telemedical and physical patient encounters are needed.
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Project description: Not available
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