Simulating COVID-19 classroom transmission on a university campus.

We study the airborne transmission risk associated with holding in-person classes on university campuses for the original strain and a more contagious variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We adopt a model for airborne transmission risk in an enclosed room that considers room properties, mask efficiency, and initial infection probability of the occupants. Additionally, we study the effect of vaccination on the spread of the virus. The presented model has been evaluated in simulations using fall 2019 (prepandemic) and fall 2020 (hybrid instruction) course registration data of a large US university, allowing for assessing the difference in transmission risk between in-person and hybrid programs and the impact of occupancy reduction, mask-wearing, and vaccination. The simulations indicate that without vaccination, moving 90% of the classes online leads to a 17 to 18× reduction in new cases, and universal mask usage results in an ∼2.7 to 3.6× reduction in new infections through classroom interactions. Furthermore, the results indicate that for the original variant and using vaccines with efficacy greater than 90%, at least 23% (64%) of students need to be vaccinated with (without) mask usage in order to operate the university at full occupancy while preventing an increase in cases due to classroom interactions. For the more contagious variant, even with universal mask usage, at least 93% of the students need to be vaccinated to ensure the same conditions. We show that the model is able to predict trends observed in weekly infection rates for fall 2021.

The Opportunities and Risks of Large Language Models in Mental Health.

Unlabelled: Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.

Robots for use in autism research.

Autism spectrum disorders are a group of lifelong disabilities that affect people's ability to communicate and to understand social cues. Research into applying robots as therapy tools has shown that robots seem to improve engagement and elicit novel social behaviors from people (particularly children and teenagers) with autism. Robot therapy for autism has been explored as one of the first application domains in the field of socially assistive robotics (SAR), which aims to develop robots that assist people with special needs through social interactions. In this review, we discuss the past decade's work in SAR systems designed for autism therapy by analyzing robot design decisions, human-robot interactions, and system evaluations. We conclude by discussing challenges and future trends for this young but rapidly developing research area.

"Robots for good": Ten defining questions.

Ten questions to guide reflection and assessment of the "good" in robotics projects are suggested.

A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm.

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use.

Correlation between performance and quantity/variability of leg exploration in a contingency learning task during infancy.

Quantity and quality of motor exploration are proposed to be fundamental for infant motor development. However, it is still not clear what types of motor exploration contribute to learning. To determine whether changes in quantity of leg movement and/or variability of leg acceleration are related to performance in a contingency learning task, twenty 6-8-month-old infants with typical development participated in a contingency learning task. During this task, a robot provided reinforcement when the infant's right leg peak acceleration was above an individualized threshold. The correlation coefficient between the infant's performance and the change in quantity of right leg movement, linear variability, and nonlinear variability of right leg movement acceleration from baseline were calculated. Simple linear regression and multiple linear regression were calculated to explain the contribution of each variable to the performance individually and collectively. We found significant correlation between the performance and the change in quantity of right leg movement (r = 0.86, p < 0.001), linear variability (r = 0.71, p < 0.001), and nonlinear variability (r = 0.62, p = 0.004) of right leg movement acceleration, respectively. However, multiple linear regression showed that only quantity and linear variability of leg movements were significant predicting factors for the performance ratio (p < 0.001, adjusted R2 = 0.94). These results indicated that the quantity of exploration and variable exploratory strategies could be critical for the motor learning process during infancy.

Socially-Assistive Robots Using Empathy to Reduce Pain and Distress during Peripheral IV Placement in Children.

Objectives: Socially-assistive robots (SAR) have been used to reduce pain and distress in children in medical settings. Patients who perceive empathic treatment have increased satisfaction and improved outcomes. We sought to determine if an empathic SAR could be developed and used to decrease pain and fear associated with peripheral IV placement in children. Methods: We conducted a pilot study of children receiving IV placement. Participating children were randomized to interact with (1) no robot, or a commercially available 3D printed humanoid SAR robot programmed with (2) empathy or (3) distraction conditions. Children and parents completed demographic surveys, and children used an adapted validated questionnaire to rate the robot's empathy on an 8-point Likert scale. Survey scores were compared by the t-test or chi-square test. Pain and fear were measured by self-report using the FACES and FEAR scales, and video tapes were coded using the CHEOPS and FLACC. Scores were compared using repeated measures 2-way ANOVA. This trial is registered with NCT02840942. Results: Thirty-one children with an average age of 9.6 years completed the study. For all measures, mean pain and fear scores were lowest in the empathy group immediately before and after IV placement. Children were more likely to attribute characteristics of empathy to the empathic condition (Likert score 7.24 v. 4.70; p=0.012) and to report that having the empathic vs. distraction robot made the IV hurt less (7.45 vs. 4.88; p=0.026). Conclusions: Children were able to identify SAR designed to display empathic characteristics and reported it helped with IV insertion pain and fear. Mean scores of self-reported or objective pain and fear scales were the lowest in the empathy group and the highest in the distraction condition before and after IV insertion. This result suggests empathy improves SAR functionality when used for painful medical procedures and informs future research into SAR for pain management.

Modeling engagement in long-term, in-home socially assistive robot interventions for children with autism spectrum disorders.

Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user's data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community.

Toward Predicting Infant Developmental Outcomes From Day-Long Inertial Motion Recordings.

As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.

Socially assistive robotics for stroke and mild TBI rehabilitation.

This paper describes an interdisciplinary research project aimed at developing and evaluating effective and user-friendly non-contact robot-assisted therapy, aimed at in-home use. The approach stems from the emerging field of social cognitive neuroscience that seeks to understand phenomena in terms of interactions between the social, cognitive, and neural levels of analysis. This technology-assisted therapy is designed to be safe and affordable, and relies on novel human-robot interaction methods for accelerated recovery of upper-extremity function after lesion-induced hemiparesis. The work is based on the combined expertise in the science and technology of non-contact socially assistive robotics and the clinical science of neurorehabilitation and motor learning, brought together to study how to best enhance recovery after stroke and mild traumatic brain injury. Our approach is original and promising in that it combines several ingredients that individually have been shown to be important for learning and long-term efficacy in motor neurorehabilitation: (1) intensity of task specific training and (2) engagement and self-management of goal-directed actions. These principles motivate and guide the strategies used to develop novel user activity sensing and provide the rationale for development of socially assistive robotics therapy for monitoring and coaching users toward personalized and optimal rehabilitation programs.

Socially Assistive Robots for Helping Pediatric Distress and Pain: A Review of Current Evidence and Recommendations for Future Research and Practice.

OBJECTIVES: Interacting with socially assistive robots (SAR) has been shown to influence human behaviors and emotions. This study sought to review the literature on SAR intervention for reducing pediatric distress and pain in medical settings. METHODS: Databases (PubMed, Cochrane Library, CINAHL, PsycINFO, ERIC, Web of Science, Engineering Village, Scopus, Google Scholar, IEEE Xplore) were searched from database inception to January 2018 with the aid of a medical librarian. Included studies examined any SAR intervention for reducing pain or improving emotional well-being in children related to physical or psychiatric care, with outcomes assessed by some quantitative measure. Study quality was assessed using the modified Downs and Black checklist (max. score, 28). The review is registered in PROSPERO (CRD42016043018). RESULTS: Eight studies met the eligibility criteria and represented 206 children. Of the 2 studies using Wong-Baker's FACES scale, 1 study claimed to be effective at reducing pain (Cohen d=0.49 to 0.62), while the other appeared effective only when parents and child interacted with SAR together. Distress was evaluated using validated measures in 4 studies, 3 of which showed reduction in distress while one showed no difference. Satisfaction surveys from 4 studies showed that children were interested in using SAR again. Quality scores ranged from 8 to 26. CONCLUSIONS: There is limited evidence suggesting that SAR interventions may reduce distress and no clear evidence showing reduction in pain for children in medical settings. Engineers are conducting interventions using SAR in pediatric populations. Health care providers should be engaged in technology research related to children to facilitate testing and improve the effectiveness of these systems.

Long-Term Personalization of an In-Home Socially Assistive Robot for Children With Autism Spectrum Disorders.

Socially assistive robots (SAR) have shown great potential to augment the social and educational development of children with autism spectrum disorders (ASD). As SAR continues to substantiate itself as an effective enhancement to human intervention, researchers have sought to study its longitudinal impacts in real-world environments, including the home. Computational personalization stands out as a central computational challenge as it is necessary to enable SAR systems to adapt to each child's unique and changing needs. Toward that end, we formalized personalization as a hierarchical human robot learning framework (hHRL) consisting of five controllers (disclosure, promise, instruction, feedback, and inquiry) mediated by a meta-controller that utilized reinforcement learning to personalize instruction challenge levels and robot feedback based on each user's unique learning patterns. We instantiated and evaluated the approach in a study with 17 children with ASD, aged 3-7 years old, over month-long interventions in their homes. Our findings demonstrate that the fully autonomous SAR system was able to personalize its instruction and feedback over time to each child's proficiency. As a result, every child participant showed improvements in targeted skills and long-term retention of intervention content. Moreover, all child users were engaged for a majority of the intervention, and their families reported the SAR system to be useful and adaptable. In summary, our results show that autonomous, personalized SAR interventions are both feasible and effective in providing long-term in-home developmental support for children with diverse learning needs.

Robots for the people, by the people: Personalizing human-machine interaction.

Multimodal, interactive, and multitask machine learning can be applied to personalize human-robot and human-machine interactions for the broad diversity of individuals and their unique needs.

Socially assistive robotics for post-stroke rehabilitation.

BACKGROUND: Although there is a great deal of success in rehabilitative robotics applied to patient recovery post stroke, most of the research to date has dealt with providing physical assistance. However, new rehabilitation studies support the theory that not all therapy need be hands-on. We describe a new area, called socially assistive robotics, that focuses on non-contact patient/user assistance. We demonstrate the approach with an implemented and tested post-stroke recovery robot and discuss its potential for effectiveness. RESULTS: We describe a pilot study involving an autonomous assistive mobile robot that aids stroke patient rehabilitation by providing monitoring, encouragement, and reminders. The robot navigates autonomously, monitors the patient's arm activity, and helps the patient remember to follow a rehabilitation program. We also show preliminary results from a follow-up study that focused on the role of robot physical embodiment in a rehabilitation context. CONCLUSION: We outline and discuss future experimental designs and factors toward the development of effective socially assistive post-stroke rehabilitation robots.

Socially assistive robotics: Human augmentation versus automation.

Intelligent, interactive systems provide assistance by facilitating social interactions rather than by automating physical tasks.

Assessing the quality and quantity of social interaction in a socially assistive robot-guided therapeutic setting.

We present an application of a socially assistive robotics (SAR) system in a therapeutic setting. We examine the amount of interaction elicited by the robot in a therapeutic setting with individuals post-stroke. We examine the role of various communication modalities, and their affects on the participants' responses. Seven participants of mild to moderate functional impairment due to stroke interacted with our SAR system during three sessions of motor task practice. The robot guided the users as they performed a wire puzzle task, while providing them with feedback about their performance. We evaluated the amount of verbalization and eye contact made with the robot. Our results indicate that users make eye contact more often than they verbalize when interacting with the robot. Further, user interactions are most frequent at the beginning of a practice session, and occur less frequently as the session progresses. When a user observes that the robot is not responding to a certain type of communication, the user limits the use of that communication modality. These insights should be useful in the design of future robot-based therapeutic interventions.

Motor function assessment using wearable inertial sensors.

We present an approach to wearable sensor-based assessment of motor function in individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the functional ability (FA) scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional motor capabilities of individuals post stroke. It is comprised of 17 tasks, 15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks using IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach.

Communication and knowledge sharing in human-robot interaction and learning from demonstration.

Inexpensive personal robots will soon become available to a large portion of the population. Currently, most consumer robots are relatively simple single-purpose machines or toys. In order to be cost effective and thus widely accepted, robots will need to be able to accomplish a wide range of tasks in diverse conditions. Learning these tasks from demonstrations offers a convenient mechanism to customize and train a robot by transferring task related knowledge from a user to a robot. This avoids the time-consuming and complex process of manual programming. The way in which the user interacts with a robot during a demonstration plays a vital role in terms of how effectively and accurately the user is able to provide a demonstration. Teaching through demonstrations is a social activity, one that requires bidirectional communication between a teacher and a student. The work described in this paper studies how the user's visual observation of the robot and the robot's auditory cues affect the user's ability to teach the robot in a social setting. Results show that auditory cues provide important knowledge about the robot's internal state, while visual observation of a robot can hinder an instructor due to incorrect mental models of the robot and distractions from the robot's movements.