This humanoid robot can help children describe their concerns better, research shows
Sometimes it can be difficult for children to open their hearts to adults. If there is a mental disorder, this situation can be even more difficult. However, a robot called "Nao" overcame it.
Robots may be more effective in identifying children's mental health problems than parental or self-reported testing, according to a recent study by the University of Cambridge.
The study published on Thursday was also presented at the 31st IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).
A team of roboticists, computer scientists, and psychiatrists conducted the study with 28 children aged eight to thirteen, using a child-sized humanoid robot to administer a series of standard psychological questionnaires to each participant.
Nao tried to understand what problems the children might have by asking open-ended questions about their mental health.
“After I became a mother, I was much more interested in how children express themselves as they grow, and how that might overlap with my work in robotics,” said professor Hatice Gunes, who leads the Affective Intelligence and Robotics Laboratory at Cambridge’s Department of Computer Science and Technology.
“Children are quite tactile, and they’re drawn to technology. If they’re using a screen-based tool, they’re withdrawn from the physical world. But robots are perfect because they’re in the physical world – they’re more interactive, so the children are more engaged.”
How was the process?
For the study, Nao — which stands about 60 centimeters tall — met with 28 children for 45-minute sessions. Members of the research team and a parent or guardian watched from a nearby room.
Children and their parents or guardian completed a common online questionnaire before each session to gauge each child's mental health.
Nao, which has the voice of a child, asked children how they felt last week and administered a questionnaire on feelings and mood and also a questionnaire used in diagnosing anxiety, panic disorder, and low mood.
Kids were placed into three groups based on how likely it was that they would be having mental health issues. Throughout the session, participants spoke with the robot by speaking to it or by touching sensors on its hands and feet. During the session, additional sensors monitored the pulse rate, head, and eye movements of the individuals.
Children were confident during the study
It was suggested that the children got along very well with Nao and felt very comfortable throughout the study. It was also observed that they easily told their inner feelings to the humanoid robot, which they would hesitate to share with their parents.
“Since the robot we use is child-sized and completely non-threatening, children might see the robot as a confidante – they feel like they won’t get into trouble if they share secrets with it,” said Nida Itrat Abbasi, a Ph.D scholar at the university, also the co-author of the study.
“Other researchers have found that children are more likely to divulge private information – like that they’re being bullied, for example – to a robot than they would be to an adult.”
According to the researchers, they plan to expand their survey in the future by including more participants and following them over time. They are also looking into whether similar results can be obtained when children interact with the robot via video chat.
Socially Assistive Robots (SARs) show promise in helping children during therapeutic and clinical interventions. However, using SARs for the evaluation of mental well-being of children has not yet been explored. Thus, this paper presents an empirical study with 28 children 8-13 years old interacting with a Nao robot in a 45-minute session where the robot administered (robotised) the Short Mood and Feelings Questionnaire (SMFQ) and the Revised Child Anxiety and Depression Scale (RCADS). Prior to the experimental session, we also evaluated children’s wellbeing using established standardised approaches via online RCADS questionnaires filled by the children (self-report) and their parents (parent-report). We clustered the participants into three groups (lower, medium, and higher tertile) based on their SMFQ scores. Further, we analysed the questionnaire response across the three clusters and across the different modes of administration (self-report, parent-report, and robotised). Our results show that the robotised evaluation seems to be the most suitable mode in identifying wellbeing related anomalies in children across the three clusters of participants as compared with the self-report and the parent-report modes. Further, children with decreasing levels of wellbeing (lower, medium and higher tertiles) exhibit different response patterns: children of higher tertile are more negative in their responses to the robot while the ones of lower tertile are more positive in their responses to the robot. Findings from this work show that SARs can be a promising tool to potentially evaluate mental wellbeing related concerns in children.
The team had to work out how to enhance both HTC and CHF by adding a series of microscale cavities (dents) to a surface.