Background:Social robots (SRs) are innovative tools in health care, offering both medical and psychological support for patients with heart failure (HF). For successful implementation, patient acceptability of SRs is crucial. Living in urban areas and having a lower comorbidity burden have been linked to higher acceptability; however, the role of psychological factors remains underexplored. Objective:This study aimed to examine the associations between negative (eg, depression and anxiety) and positive (eg, optimism) psychological factors and personality traits (eg, openness and extraversion) with SR acceptability in patients with HF. Methods:Patients with HF watched brief videos about SRs and completed validated measures of depressive symptoms (Patient Health Questionnaire-9), anxiety symptoms (Generalized Anxiety Disorder-7), positive psychological well-being (Brief Inventory of Thriving), and personality traits (Ten-Item Personality Inventory). Medical information was extracted from patients' records. SR acceptability was assessed using the Unified Theory of Acceptance and Use of Technology (UTAUT). Pearson correlations and multiple linear regression, adjusted for age, sex, smart technology experience, urbanicity, and comorbidities, were conducted. Results:Of the 101 patients (women: n=36, 35.6%, mean age 68, SD 10 y), 23% (23/101) scored in the clinical range for depression, and 17% (17/101) scored in the clinical range for anxiety. Well-being scores were moderate, and conscientiousness and agreeableness were the most common. UTAUT behavioral intention was moderate; 69% (67/97) of participants were likely to use an SR if available. Well-being scores correlated positively with SR acceptability in 4 of 5 UTAUT subscales, whereas no significant bivariate associations were observed for psychological distress or personality traits. In the multiple regression models, higher Brief Inventory of Thriving scores were associated with increased SR acceptability, including UTAUT facilitating conditions (B=0.17; P=.01) and behavioral intention (B=0.17; P=.04), independent of depressive and anxiety symptoms. Conclusions:Psychological well-being is associated with determinants of SR acceptability in patients with HF, while psychological distress and personality traits are not associated with these determinants. These patient-level factors ought to be examined more closely before SR implementation.

To be able to learn effectively, robots sometimes will need to select more suitable human teachers. We propose an attribute in human teachers for robots that learn through visual observations, namely human teachers’ awareness of and attention to the robot’s visual capabilities and constraints, and explore how it affects robot learning outcomes. In an in-person experiment involving 72 participants who taught three physical tasks to an iCub humanoid robot, we manipulated teachers’ awareness of the robot’s visual constraints by offering the visual perspective of the robot in one of the experimental conditions. Participants who were able to see the robot’s vision output paid increased attention to ensuring task objects were visible to the robot when providing demonstrations of physical tasks. This emphasis on attention to the robot’s view resulted in better learning outcomes for the robot, as indicated by lower perception error rates and higher learning scores. This study contributes to understanding factors in human teachers that lead to better learning outcomes for robots.

Social robots have great potential in supporting individuals' physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. 9362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centres while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey”, which includes different stages of one's life which could benefit from a social robot, with the goal of increasing long term adoption of social robots for supporting health/wellbeing.

Presentation rehearsal is an essential activity that significantly impacts the quality of presentation delivery, especially for novice presenters, such as university students. However, rehearsals are often not done appropriately, or lack constructive feedback. To encourage effective presentation rehearsal, we devised a system involving a social humanoid robot acting as a public speaking coach, analyzing students' presentations and providing feedback. We monitored acoustic aspects of speech, speech prosodies, and eye contact maintenance during presentations. Our aim was to assess robot acceptance, participants' sense of interpersonal closeness with the robot, and perceived human nature attributes of the robot. This study presents the system development, followed by evaluations with 50 university students, as well as an evaluation by a public speaking coach. We found that the students, on average, gave high acceptance scores for the robot and reported moderate interpersonal closeness with the robot, and attributed human nature attributes to it. Additionally, an expert public speaking coach found the system to be able to provide reliable and relevant feedback to students considering their performance, and he also gave useful insights on potential improvements of the system.

Robots may learn new skills from humans to better assist us with everyday tasks. We propose a novel, biologically inspired imitation approach to enable robots to understand and perform complex actions using high-level programs that incorporate sequential regularities between sub-goals a robot can recognize and physically achieve. To learn a new task, human-provided demonstrations-obtained by a robot through different modalities such as kinesthetic teaching or behavioural observation-are processed by an algorithm to discover multiple possible arrangements of sub-goals that achieve the task goal. When performing the task, the robot first evaluates the available sequences in the program based on user-defined criteria, through mental simulation of the real task, to find the optimal sequence of actions. The selected sequence is then executed using the hierarchical structure of actions embedded in the program. We implemented the proposed learning architecture on an iCub humanoid robot and evaluated the effectiveness of the system in multiple scenarios. Our approach accommodates variations in human teaching styles and is expected to help robots perform tasks with greater flexibility and efficiency, opening the way to more adaptable and intelligent robots.

In the past decade, technology-assisted interventions for mental health have been growing. To appropriately design technologies to support mental health, such as social robots, a crucial step involves understanding the perspectives, needs, and concerns of clinicians who deliver mental health services. Our research aims to understand mental health clinicians’ perspectives on the use of social robots within various aspects of clinical practice. We conducted an online study involving 49 clinicians registered with provincial psychological regulatory bodies within Canada. The participants responded to questionnaires regarding their general views on technology/social robots, rated the degree of advantage/disadvantage of using social robots in different components of clinical practice (Screening, Diagnosis, Intervention, Administration, and Maintenance), and provided open-ended responses regarding potential applications for social robots in clinical activities. Two experimental conditions were designed, in which the participants were either introduced to social robots through a short video (exposure condition) or did not receive such an introduction (non-exposure condition). The results of the quantitative analysis indicated that exposing clinicians to more information about social robots did not yield significant rating differences, but their initial familiarity with social robots was positively associated with their perceptions. On average, clinicians’ ratings were neutrally valenced (around the midline of positive and negative), indicating, as a group, ambivalence about using social robots. They noted more advantages for social robots to support administrative roles and maintenance roles (maintaining clinical gains post-intervention). Qualitative results, i.e., thematic analysis, identified 93 codes, 18 sub-themes, and two overarching themes: perceived advantages and disadvantages. Noteworthy applications of social robots included “intake”, “administering clinical services”, “administered tasks”, and “assistant to clinicians”, while top disadvantages were “unsuitability”, “detrimental to therapeutic relationship”, and “limitations of robot relative to human capabilities”. Clinicians identified social robots as valuable tools for standardized, structured, and non-judgmental therapeutic care; however, concerns about their impact on the therapeutic relationship and limitations compared to human capabilities emphasize the importance of careful integration. The proposed guidelines aim to navigate these challenges and offer a framework for the nuanced incorporation of social robots into mental healthcare practices.

Background: During demographic shifts towards an older population, healthcare systems face increased demands, highlighting the need for innovative approaches that facilitate supporting older adults' well-being and safety. This study aims to demonstrate the effectiveness of zero-effort Ambient Assisted Living technology in recognizing daily activities of older adults via machine learning algorithms by comparing with wearable technology. Methods: Conducted in a smart home environment equipped with a comprehensive range of non- intrusive sensors, the study involved 40 participants, during which they were instructed to perform 23 types of predefined daily living activities, organized in five phases. Data from these activities were concurrently captured by both ambient and wearable sensors. Analysis was performed using five machine learning models: K-Nearest Neighbors, Decision Trees, Random Forest, Adaptive Boosting, and Gaussian Naive Bayes. Results: Ambient sensors, especially using the AdaBoost model, demonstrated high accuracy (0.964) in activity recognition, significantly outperforming wearable sensors (best accuracy 0.367 with Random Forest). When fusing data from both sensor types, the accuracy slightly decreases to 0.909. Despite spatial overlap challenges, ambient sensors accurately recognize activities across various room settings with accuracies all above 0.950. Feature importance analysis reveals that climatic, electrical, and motion-related features are crucial for model classification. Conclusion: This study showcases the efficacy of Ambient Assisted Living technology in recognizing daily indoor activities of older adults. These findings have implications for public health, highlighting Ambient Assisted Living technology's potential to support older adults' independence and well-being, offering a promising direction for future research and application in smart living environments.

Public speaking anxiety is one of the most common subtypes of social anxiety and is a prevalent concern among university students. Many students experience excessive anxiety when giving presentations in front of other people, which can negatively impact their academic performance and overall mental well-being. With limited access to human coaches and interventions, there is a need for innovative technological solutions, including social robots, to extend and enhance mental health support and accessibility. In this paper, we first outline a co-design study with five mental health professionals and a participatory design study with six university students, aiming to design a robotic mental well-being coach to help university students manage public speaking anxiety. Afterwards, we detail a user study with 50 university students to evaluate the usability and acceptability of the developed robotic mental well-being coach system. The findings showed that the robotic coach system, which includes the robot and a tablet, received a usability score of 84.05 and had high acceptability among participants who perceived the robot as knowledgeable and competent. Moreover, participants’ self-reported moods significantly improved following the study. Overall, conducting qualitative and quantitative analysis in this study yields promising results on the potential use of robotic coaches to help university students manage their public speaking anxiety.

We are concurrently witnessing two significant shifts: voice and chat-based conversational user interfaces (CUIs) are becoming ubiquitous (especially more recently due to advances in generative AI and LLMs - large language models), and older people are becoming a very large demographic group (and increasingly adopting of mobile technology on which such interfaces are present). However, despite the recent increase in research activity, age-relevant and inter/cross-generational aspects continue to be underrepresented in both research and commercial product design. Therefore, the overarching aim of this workshop is to increase the momentum for research within the space of hands-free, mobile, and conversational interfaces that centers on age-relevant and inter- and cross-generational interaction. For this, we plan to create an interdisciplinary space that brings together researchers, designers, practitioners, and users, to discuss and share challenges, principles, and strategies for designing such interfaces across the life span. We thus welcome contributions of empirical studies, theories, design, and evaluation of hands-free, mobile, and conversational interfaces designed with aging in mind (e.g. older adults or inter/cross-generational). We particularly encourage contributions focused on leveraging recent advances in generative AI or LLMs. Through this, we aim to grow the community of CUI researchers across disciplinary boundaries (human-computer interaction, voice and language technologies, geronto-technologies, information studies, etc.) that are engaged in the shared goal of ensuring that the aging dimension is appropriately incorporated in mobile / conversational interaction design research.

The use of interactive tools, such as voice assistants and social robots, holds promise as coaching aids during public speaking rehearsals. To create a coach that is both effective and likable, it is important to understand how people perceive these agents when they observe them during actual presentation sessions. Specifically, it is important to assess people’s perceptions of the agents’ physical embodiment and nonverbal social behaviour, taking into account both listening and feedback periods. To this end, we conducted an online study with 168 participants who watched videos of agents acting as public speaking coaches. The study had three conditions: two with a humanoid social robot in either (1) active listening mode, using nonverbal backchannelling, (2) passive listening mode, and (3) a voice assistant agent. The results showed that the social robot in both conditions was perceived more positively in terms of its human-like attributes, and likability than the voice assistant agent. The active listener robot was perceived as more satisfying, more engaging, more natural, and warmer than the voice assistant agent, but this difference was not seen between the passive listener robot and the voice assistant agent. Additionally, the active listener robot was found to be more natural than the passive listening robot. However, there were no significant differences in perceived intelligence, competence, discomfort, and helpfulness between the three agents. Finally, participants’ gender and personality traits were found to affect their evaluations of the agents. The study offered insights into general attitudes towards using social robots and voice assistants as public speaking coaches, which can guide the future design and use of these agents as coaches.

In recent years, university students have reported increased symptoms of stress and anxiety, which can negatively impact their mental well-being and academic performance. However, many students do not seek or receive support for these challenges. With recent technological advances in intelligent agents and their expanding capabilities, there is a potential for intelligent agents to extend and complement mental health care interventions and enhance access to care. Recognizing that there are many further steps to developing user-centred evidence-based approaches for mental health challenges, as a first step to developing effective interventions using intelligent agents, it is important to identify design elements and functionalities that are perceived as engaging and useful by students. In this paper, we present the findings of an online survey with 1054 undergraduate students to explore students’ perceptions of, and preferences for, using different types of intelligent agents (e.g., virtual agents, social robots, etc.) to support their mental well-being, specifically to cope with feelings of stress and anxiety in social situations typically encountered within a university context (e.g., engaging in a group discussion, delivering presentations, expressing opinions, etc.). Students were asked to complete a questionnaire to explore their experience of stress and anxiety in the university context, as well as their perceptions of, and preferences for using different intelligent agents as assistive tools to cope with such feelings. The results provide insights into different design elements as well as social and technical capabilities to consider when designing intelligent agents to help address stress and anxiety among university students.

BackgroundThe aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.ObjectiveThe aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.MethodsA comprehensive search was conducted in six databases—PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science—to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.ResultsSeveral trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.ConclusionOur results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

BackgroundAs global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults.MethodsThe study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron.ResultsAll machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals' daily living activities and their corresponding heart rate responses, with the highest R2 value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance.ConclusionsThe utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals' daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual's cardiovascular health profile. This valuable information can inform the implementation of personalized interventions, preventive measures, and lifestyle modifications to mitigate the risk of cardiovascular diseases and improve overall health outcomes.

For social robots to succeed in places such as homes, they must learn new skills from various people and act in a manner desirable to different users. We introduce a novel biologically inspired approach for robot learning through program-level imitation, inspired by the way primates, including humans, understand and perform complex actions. Our approach enables robots to discover the hierarchical structure of tasks by identifying sequential regularities and sub-goals from diverse human demonstrations. To do so, human-provided demonstrations, which can be obtained by a robot through different modalities (such as kinesthetic teaching, behavioural observation, and verbal instruction), are processed by an algorithm that discovers multiple possibilities for arranging observed sub-goals to achieve a final goal. Prior to acting, the available sequences are evaluated based on user-defined criteria, through mental simulation of the task by the robot, to find the optimal sequence of actions. As a proof-of-concept, we implemented our system on an iCub humanoid robot and present here how our method allowed the robot to adapt its action sequences for task execution when starting the task from different states, incorporating user preference for finishing the task as fast as possible. Our envisaged system is meant to accommodate variations in human teaching styles and is expected to help a robot perform tasks with greater flexibility and efficiency. This work contributes by proposing a framework for robots to learn from humans at an abstract level, opening the way to more adaptable and intelligent robotic assistants in everyday tasks.

Safe, socially compliant, and efficient navigation of low-speed autonomous vehicles (AVs) in pedestrian-rich environments necessitates considering pedestrians' future positions and interactions with the vehicle and others. Despite the inevitable uncertainties associated with pedestrians' predicted trajectories due to their unobserved states (e.g., intent), existing deep reinforcement learning (DRL) algorithms for crowd navigation often neglect these uncertainties when using predicted trajectories to guide policy learning. This omission limits the usability of predictions when diverging from ground truth. This work introduces an integrated prediction and planning approach that incorporates the uncertainties of predicted pedestrian states in the training of a model-free DRL algorithm. A novel reward function encourages the AV to respect pedestrians' personal space, decrease speed during close approaches, and minimize the collision probability with their predicted paths. Unlike previous DRL methods, our model, designed for AV operation in crowded spaces, is trained in a novel simulation environment that reflects realistic pedestrian behaviour in a shared space with vehicles. Results show a 40% decrease in collision rate and a 15% increase in minimum distance to pedestrians compared to the state of the art model that does not account for prediction uncertainty. Additionally, the approach outperforms model predictive control methods that incorporate the same prediction uncertainties in terms of both performance and computational time, while producing trajectories closer to human drivers in similar scenarios.

IntroductionAssistive technology is increasingly used to support the physical needs of differently abled persons but has yet to make inroads on support for cognitive or psychological issues. This gap is an opportunity to address another—the lack of contribution from theoretical social science that can provide insights into problems that cannot be seen. Using Affect Control Theory (ACT), the current project seeks to close that gap with an artificially intelligent application to improve interaction and affect for people with Alzheimer’s Disease and Related Dementias (ADRD). Using sociological theory, it models interactions with persons with ADRD based on self-sentiments, rather than cognitive memory, and informs a cellphone-based assistive tool called VIPCare for supporting caregivers.MethodsStaff focus groups and interviews with family members of persons with ADRD in a long-term residential care facility collected residents’ daily needs and personal histories. Using ACT’s evaluation, potency, and activity dimensions, researchers used these data to formulate a self-sentiment profile for each resident and programmed that profile into the VIPCare application. VIPCare used that profile to simulate affectively intelligent social interactions with each unique resident that reduce deflection from established sentiments and, thus, negative emotions.ResultsWe report on the data collection to design the application, develop self-sentiment profiles for the resident, and generate assistive technology that applies a sociological theory of affect to real world management of interaction, emotion, and mental health.DiscussionBy reducing trial and error in learning to engage people with dementia, this tool has potential to smooth interaction and improve wellbeing for a population vulnerable to distress.

In real-world applications, robots should adapt to users and environments; however, users may not know how to teach new tasks to a robot. We studied whether participants without any experience in teaching a robot would become more proficient robot teachers through repeated kinesthetic human–robot teaching interactions. An experiment was conducted with twenty-eight participants who were asked to kinesthetically teach a humanoid robot different cleaning tasks in five repeated sessions, each session including four tasks. Throughout the sessions, participants’ gaze patterns, methods of manipulating the robot’s arm, their perceived workload, and some physical properties of the demonstrated actions were measured. Our data analyses revealed a diversity in non-experts’ human–robot teaching styles in repeated interactions. Three clusters of human teachers were identified based on participants’ performance in providing the demonstrations. The majority of participants significantly improved their success and speed of kinesthetic demonstrations by performing multiple rounds of teaching the robot. Overall, participants gazed less often at the robot’s hand and perceived less effort over repeated sessions. Our findings highlight how non-experts adapt to robot teaching by being exposed repeatedly to human–robot teaching tasks, without any formal training or external intervention, and we identify the characteristics of successful and improving human teachers.

Smart home devices have great potential for supporting older adults’ health, safety, and independent living. Past reviews have identified only a few studies on the use of smart home devices for older adults and reported low technology readiness levels for the devices. This article presents a systematic literature review to identify the devices that have been used in studies with older adults, the setting in which those devices have been tested, the evaluation methods of the existing user studies, and the limitations. [Method] ACM DL, Scopus, PubMed, and IEEE Xplore were searched for a set of different keywords that included smart home sensors and older adults. The search was limited to “past ten years” (from the search date). Articles written in English that included user studies evaluating smart home devices with older adults were included. PRISMA guidelines were followed. [Results] 3847 unique articles were identified, 48 of which were included in the review. The articles represented research from a large range of countries. The majority of the studies evaluated the devices in participants’ homes, followed by research lab settings. A few articles used other settings such as care centres and hospitals. The studies mainly evaluated the performance of the systems, followed by users’ evaluations, such as perceptions and acceptance. Many studies had long-term interactions (more than a month). [Conclusion] there are still limited studies on the impact and benefits of smart home devices on older adults’ quality of life, health, or well-being. Future studies are needed to better understand these benefits.