Assistive Robots for the Social Management of Health: A Framework for Robot Design and Human-Robot Interaction Research.

There is a close connection between health and the quality of one's social life. Strong social bonds are essential for health and wellbeing, but often health conditions can detrimentally affect a person's ability to interact with others. This can become a vicious cycle resulting in further decline in health. For this reason, the social management of health is an important aspect of healthcare. We propose that socially assistive robots (SARs) could help people with health conditions maintain positive social lives by supporting them in social interactions. This paper makes three contributions, as detailed below. We develop a framework of social mediation functions that robots could perform, motivated by the special social needs that people with health conditions have. In this framework we identify five types of functions that SARs could perform: (a) changing how the person is perceived, (b) enhancing the social behavior of the person, (c) modifying the social behavior of others, (d) providing structure for interactions, and (e) changing how the person feels. We thematically organize and review the existing literature on robots supporting human-human interactions, in both clinical and non-clinical settings, and explain how the findings and design ideas from these studies can be applied to the functions identified in the framework. Finally, we point out and discuss challenges in designing SARs for supporting social interactions, and highlight opportunities for future robot design and HRI research on the mediator role of robots.

Using topic modeling to infer the emotional state of people living with Parkinson's disease.

Individuals with Parkinson's disease (PD) often exhibit facial masking (hypomimia), which causes reduced facial expressiveness. This can make it difficult for those who interact with the person to correctly read their emotional state and can lead to problematic social and therapeutic interactions. In this article, we develop a probabilistic model for an assistive device, which can automatically infer the emotional state of a person with PD using the topics that arise during the course of a conversation. We envision that the model can be situated in a device that could monitor the emotional content of the interaction between the caregiver and a person living with PD, providing feedback to the caregiver in order to correct their immediate and perhaps incorrect impressions arising from a reliance on facial expressions. We compare and contrast two approaches: using the Latent Dirichlet Allocation (LDA) generative model as the basis for an unsupervised learning tool, and using a human-crafted sentiment analysis tool, the Linguistic Inquiry and Word Count (LIWC). We evaluated both approaches using standard machine learning performance metrics such as precision, recall, and F1scores. Our performance analysis of the two approaches suggests that LDA is a suitable classifier when the word count in a document is approximately that of the average sentence, i.e., 13 words. In that case, the LDA model correctly predicts the interview category 86% of the time and LIWC correctly predicts it 29% of the time. On the other hand, when tested with interviews with an average word count of 303 words, the LDA model correctly predicts the interview category 56% of the time and LIWC, 74% of the time. Advantages and disadvantages of the two approaches are discussed.

Effects of Assistive Robot Behavior on Impressions of Patient Psychological Attributes: Vignette-Based Human-Robot Interaction Study.

BACKGROUND: As robots are increasingly designed for health management applications, it is critical to not only consider the effects robots will have on patients but also consider a patient's wider social network, including the patient's caregivers and health care providers, among others. OBJECTIVE: In this paper we investigated how people evaluate robots that provide care and how they form impressions of the patient the robot cares for, based on how the robot represents the patient. METHODS: We have used a vignette-based study, showing participants hypothetical scenarios describing behaviors of assistive robots (patient-centered or task-centered) and measured their influence on people's evaluations of the robot itself (emotional intelligence [EI], trustworthiness, and acceptability) as well as people's perceptions of the patient for whom the robot provides care. RESULTS: We found that for scenarios describing a robot that acts in a patient-centered manner, the robot will not only be perceived as having higher EI (P=.003) but will also cause people to form more positive impressions of the patient that the robot cares for (P<.001). We replicated and expanded these results to other domains such as dieting, learning, and job training. CONCLUSIONS: These results imply that robots could be used to enhance human-human relationships in the health care context and beyond.

A Within-subjects Experimental Protocol to Assess the Effects of Social Input on Infant EEG.

Despite the importance of social interactions for infant brain development, little research has assessed functional neural activation while infants socially interact. Electroencephalography (EEG) power is an advantageous technique to assess infant functional neural activation. However, many studies record infant EEG only during one baseline condition. This protocol describes a paradigm that is designed to comprehensively assess infant EEG activity in both social and nonsocial contexts as well as tease apart how different types of social inputs differentially relate to infant EEG. The within-subjects paradigm includes four controlled conditions. In the nonsocial condition, infants view objects on computer screens. The joint attention condition involves an experimenter directing the infant's attention to pictures. The joint attention condition includes three types of social input: language, face-to-face interaction, and the presence of joint attention. Differences in infant EEG between the nonsocial and joint attention conditions could be due to any of these three types of input. Therefore, two additional conditions (one with language input while the experimenter is hidden behind a screen and one with face-to-face interaction) were included to assess the driving contextual factors in patterns of infant neural activation. Representative results demonstrate that infant EEG power varied by condition, both overall and differentially by brain region, supporting the functional nature of infant EEG power. This technique is advantageous in that it includes conditions that are clearly social or nonsocial and allows for examination of how specific types of social input relate to EEG power. This paradigm can be used to assess how individual differences in age, affect, socioeconomic status, and parent-infant interaction quality relate to the development of the social brain. Based on the demonstrated functional nature of infant EEG power, future studies should consider the role of EEG recording context and design conditions that are clearly social or nonsocial.

Social attention in ASD: A review and meta-analysis of eye-tracking studies.

Determining whether social attention is reduced in Autism Spectrum Disorder (ASD) and what factors influence social attention is important to our theoretical understanding of developmental trajectories of ASD and to designing targeted interventions for ASD. This meta-analysis examines data from 38 articles that used eye-tracking methods to compare individuals with ASD and TD controls. In this paper, the impact of eight factors on the size of the effect for the difference in social attention between these two groups are evaluated: age, non-verbal IQ matching, verbal IQ matching, motion, social content, ecological validity, audio input and attention bids. Results show that individuals with ASD spend less time attending to social stimuli than typically developing (TD) controls, with a mean effect size of 0.55. Social attention in ASD was most impacted when stimuli had a high social content (showed more than one person). This meta-analysis provides an opportunity to survey the eye-tracking research on social attention in ASD and to outline potential future research directions, more specifically research of social attention in the context of stimuli with high social content.

Eye-tracking measurements of language processing: developmental differences in children at high risk for ASD.

To explore how being at high risk for autism spectrum disorder (ASD), based on having an older sibling diagnosed with ASD, affects word comprehension and language processing speed, 18-, 24- and 36-month-old children, at high and low risk for ASD were tested in a cross- sectional study, on an eye gaze measure of receptive language that measured how accurately and rapidly the children looked at named target images. There were no significant differences between the high risk ASD group and the low risk control group of 18- and 24-month-olds. However, 36-month-olds in the high risk for ASD group performed significantly worse on the accuracy measure, but not on the speed measure. We propose that the language processing efficiency of the high risk group is not compromised, but other vocabulary acquisition factors might have lead to the high risk 36-month-olds to comprehend significantly fewer nouns on our measure.