Search

BackgroundSociety is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem. ObjectiveOur objective is to evaluate the quality of care perceived by patients and caregivers for an integrated care pathway in an outpatient clinic using a social robot for patient-reported outcome measure (PROM) interviews versus the currently used professional interviews. MethodsA multicenter, two-parallel-group, nonblinded, randomized controlled trial was used to test for noninferiority of the quality of care delivered through robot-assisted care. The randomization was performed using a computer-generated table. The setting consisted of two outpatient clinics, and the study took place from July to December 2019. Of 419 patients who visited the participating outpatient clinics, 110 older patients met the criteria for recruitment. Inclusion criteria were the ability to speak and read Dutch and being assisted by a participating health care professional. Exclusion criteria were serious hearing or vision problems, serious cognitive problems, and paranoia or similar psychiatric problems. The intervention consisted of a social robot conducting a 36-item PROM. As the main outcome measure, the customized Consumer Quality Index (CQI) was used, as reported by patients and caregivers for the outpatient pathway of care. ResultsIn total, 75 intermediately frail older patients were included in the study, randomly assigned to the intervention and control groups, and processed: 36 female (48%) and 39 male (52%); mean age 77.4 years (SD 7.3), range 60-91 years. There was no significant difference in the total patient CQI scores between the patients included in the robot-assisted care pathway (mean 9.27, SD 0.65, n=37) and those in the control group (mean 9.00, SD 0.70, n=38): P=.08, 95% CI –0.04 to 0.58. There was no significant difference in the total CQI scores between caregivers in the intervention group (mean 9.21, SD 0.76, n=30) and those in the control group (mean 9.09, SD 0.60, n=35): P=.47, 95% CI –0.21 to 0.46. No harm or unintended effects occurred. ConclusionsGeriatric patients and their informal caregivers valued robot-assisted and nonrobot-assisted care pathways equally. Trial RegistrationClinicalTrials.gov NCT03857789; https://clinicaltrials.gov/ct2/show/NCT03857789

As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human–robot interaction (HRI), with dialogue management (DM) being a key component in those interactive systems. Yet, to overcome current challenges and manage smooth, informative, and engaging interaction, a more structural approach to combining HRI and DM is needed. In this systematic review, we analyze the current use of DM in HRI and focus on the type of dialogue manager used, its capabilities, evaluation methods, and the challenges specific to DM in HRI. We identify the challenges and current scientific frontier related to the DM approach, interaction domain, robot appearance, physical situatedness, and multimodality. [ABSTRACT FROM AUTHOR]

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot—a need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable, the robot takes control, undermining children's ability to co-regulate the interaction. Co-regulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light animations for the robot to use during their conversation. A crucial additional feature is that children are able to coordinate their involvement of the co-creation process. Results from a user study (n = 59 school children, 7–11 years old) showed that the co-creation activity successfully facilitated co-regulation by improving children's agency. It also positively affected the acceptance of the robot. We furthermore identified five distinct profiles detailing the different needs and motivations children have for the level of involvement they chose during the co-creation process. [ABSTRACT FROM AUTHOR]

This article aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases. [ABSTRACT FROM AUTHOR]

Smart devices, such as smart phones, voice assistants and social robots, provide users with a range of input modalities, e.g., speech, touch, gestures, and vision. In recent years, advancements in processing of these input channels enable more natural interaction (e.g., automated speech, face, and gesture recognition, dialog generation, emotion expression etc.) experiences for users. However, there are several important challenges that need to be addressed to create these user experiences. One challenge is that most smart devices do not have sufficient computing resources to execute the Artificial Intelligence (AI) techniques locally. Another challenge is that users expect responses in near real-time when they interact with these devices. Moreover, users also want to be able to seamlessly switch between devices and services any time and from anywhere and expect personalized and privacy-aware services. To address these challenges, we design and develop a cloud-based middleware (CMI) which helps to develop multi-modal interaction applications and easily integrate applications to AI services. In this middleware, services developed by different producers with different protocols and smart devices with different capabilities and protocols can be integrated easily. In CMI, applications stream data from devices to cloud services for processing and consume the results. It supports data streaming from multiple devices to multiple services (and vice versa). CMI provides an integration framework for decoupling the services and devices and enabling application developers to concentrate on "interaction" instead of AI techniques. We provide simple examples to illustrate the conceptual ideas incorporated in CMI. [ABSTRACT FROM AUTHOR]

Simple Summary: Sleep problems are faced by many children with cancer, and they can lead to negative physical and psychological outcomes, as well as to a lower quality of life. Educating families on healthy sleep hygiene to reduce sleep problems is important. We explored the use of a social robot to provide sleep hygiene education, as social robots seem to be an innovative and suitable tool for children. We developed an interactive education program during which the robot discussed six sleep hygiene topics with 28 children by asking them questions, followed by providing them information. We used multiple methods and found that the use of a social robot at the outpatient clinic was feasible, that children and parents had mostly positive experiences, and that the sleep hygiene of children was better two weeks after the education regimen. Our work may underline the value of providing education through a social robot. Further research is needed to develop and implement this intervention. Objectives: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with a social robot at a pediatric oncology outpatient clinic regarding the feasibility, experiences, and preliminary effectiveness. Methods: Researchers approached children (8 to 12 years old) who were receiving anticancer treatment and who were visiting the outpatient clinic with their parents during the two-week study period. The researchers completed observation forms regarding feasibility, and parents completed the Children's Sleep Hygiene Scale before and two weeks after the educational regimen. The experiences of children and parents were evaluated in semi-structured interviews. We analyzed open answers by labeling each answer with a topic reflecting the content and collapsed these topics into categories. We used descriptive statistics to describe the feasibility and experiences, and a dependent-samples t-test to evaluate the preliminary effectiveness. Results: Twenty-eight families participated (58% response rate) and all interactions with the robot were completed. The children and parents reported that they learned something new (75% and 50%, respectively), that they wanted to learn from the robot more often (83% and 75%, respectively), and that they applied the sleeping tips from the robot afterwards at home (54%). Regarding the preliminary effectiveness, children showed a statistically significant improvement in their sleep hygiene (p = 0.047, d = 0.39). Conclusions: Providing an educational regimen on sleep hygiene in a novel, interactive way by using a social robot at the outpatient clinic seemed feasible, and the children and parents mostly exhibited positive reactions. We found preliminary evidence that the sleep hygiene of children with cancer improved. [ABSTRACT FROM AUTHOR]

We define hybrid intelligence (HI) as the combination of human and machine intelligence, augmenting human intellect and capabilities instead of replacing them and achieving goals that were unreachable by either humans or machines. HI is an important new research focus for artificial intelligence, and we set a research agenda for HI by formulating four challenges. [ABSTRACT FROM AUTHOR]

Background /Objectives Healthcare professionals (HCP) are confronted with an increased demand for assessments of important health status measures, such as patient-reported outcome measurements (PROM), and the time this requires. The aim of this study was to investigate the effectiveness and acceptability of using an HCP robot assistant, and to test the hypothesis that a robot can autonomously acquire PROM data from older adults. Design A pilot randomised controlled cross-over study where a social robot and a nurse administered three PROM questionnaires with a total of 52 questions. Setting A clinical outpatient setting with community-dwelling older adults. Participants Forty-two community-dwelling older adults (mean age: 77.1 years, SD: 5.7 years, 45% female). Measurements The primary outcome was the task time required for robot-patient and nurse-patient interactions. Secondary outcomes were the similarity of the data and the percentage of robot interactions completed autonomously. The questionnaires resulted in two values (robot and nurse) for three indexes of frailty, well-being and resilience. The data similarity was determined by comparing these index values using Bland-Altman plots, Cohen's kappa (κ) and intraclass correlation coefficients (ICC). Acceptability was assessed using questionnaires. Results The mean robot interview duration was 16.57 min (SD=1.53 min), which was not significantly longer than the nurse interviews (14.92 min, SD=8.47 min; p=0.19). The three Bland-Altman plots showed moderate to substantial agreement between the frailty, well-being and resilience scores (κ=0.61, 0.50 and 0.45, and ICC=0.79, 0.86 and 0.66, respectively). The robot autonomously completed 39 of 42 interviews (92.8%). Conclusion Social robots may effectively and acceptably assist HCPs by interviewing older adults. [ABSTRACT FROM AUTHOR]

Agents in teamwork may be highly interdependent on each other, and the awareness of interdependences is an important requirement for designing and consequently implementing a multiagent system. In this article, we propose a formal graphical and domain-independent language that can facilitate the identification of comprehensive interdependences among the agents in teamwork. Moreover, a formal semantics is also introduced to precisely express and explain the properties of a graphical structure. The novel feature of the graphical language is that it complements the Interdependence Analysis Color Scheme in a way that explicitly models negative influences and, in addition, provides a visual-communication aid for developers. To demonstrate the applicability and sufficiency of the graphical language in a variety of domains, our case studies include a multirobot scenario and a human-robot scenario. [ABSTRACT FROM AUTHOR]