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. 2022 Jul 7;22(14):5116.
doi: 10.3390/s22145116.

Understanding Emotions in Children with Developmental Disabilities during Robot Therapy Using EDA

Affiliations

Understanding Emotions in Children with Developmental Disabilities during Robot Therapy Using EDA

Taisuke Nagae et al. Sensors (Basel). .

Abstract

Recent technological advancements have led to the emergence of supportive robotics to help children with developmental disabilities become independent. In conventional research, in robot therapy, experiments are often conducted by operating the robot out of the subject's sight. In this paper, robot therapy using a system that can autonomously recognize the emotions of a child with developmental disabilities and provide feedback was developed. The aim was to quantitatively infer emotional changes in children using skin conductance (EDA) during robot therapy. It was demonstrated that the robot could recognize emotions autonomously and provide feedback to the subjects. Additionally, a quantitative evaluation was conducted using EDA. By analyzing the symptoms related to developmental disorders, it may be possible to improve the recognition rate and tailor therapy based on symptoms.

Keywords: EDA; HRI; children with autism; emotions; robot therapy; social robot.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The procedure of the interaction between child and NAO robot.
Figure 2
Figure 2
(Left) the scene in camera 1, C: child, T: therapist; (center) the scene in camera 3, C: child, T: therapist; (right) the experiment setup, C: child, T: therapist. At the very beginning of session, child and therapist sit in front of desk for around 10 min to relax before child was exposed to the robot. CAM 1 records only face and upper body of the participants. CAM 2 is used for recognizing the facial expressions. CAM 3 records the whole scene of session for reference.
Figure 3
Figure 3
(Left) facial-expression cards, from left, sad, anger, surprise, happy; (right) the expressions by NAO, from left, sad, anger, surprise, happy.
Figure 4
Figure 4
Raw and wavelet transferred data, and z scores of all subjects.
Figure 5
Figure 5
Average of recognition rate in all subjects.
Figure 6
Figure 6
(Left) recognition rate for each emotion in raw data; (right) recognition rate for each emotion in wavelet-transferred data.

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